User Friction in Infinite Scrolling Algorithmic Feeds: Examining the Impact of App Use and Algorithm Dependence on News Knowledge

User Friction in Infinite Scrolling Algorithmic Feeds: Examining the Impact of App Use and Algorithm Dependence on News Knowledge | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article User Friction in Infinite Scrolling Algorithmic Feeds: Examining the Impact of App Use and Algorithm Dependence on News Knowledge Cong Liu, Rui Qiao This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6917973/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background The rapid expansion of mobile applications featuring personalized recommendation algorithms and infinite scrolling news feeds has raised concerns about their role in shaping societal knowledge acquisition. Grounded in the frameworks of algorithm dependence, this study investigates how different types of algorithmic Apps (news, social media, and short video) impact users’ news knowledge. Specifically, we examine the mediating effect of algorithm dependence on the relationship between App usage and news knowledge acquisition, introducing user friction as a mechanism, with perceived information narrowing as a moderating factor. Methods Data was collected via online survey with 354 responded participants. Results Results revealed that short video Apps decrease users’ news knowledge, social media Apps indirectly reduce news knowledge through algorithm dependence, and news Apps diminish news knowledge only among users perceiving high levels of information narrowing. Conclusions These findings suggest the potential for introducing user friction to regulate algorithmic curation and mitigate its negative impact on knowledge gain, especially within algorithmic news Apps. This study contributes to understanding the complex interplay between algorithmic dependence and knowledge gain, highlighting user-centered approaches to enhancing informational diversity in algorithm-driven media. Social science/Cultural and media studies Social science/Psychology algorithm recommendation user friction infinite scroll algorithm dependence news knowledge information narrowing Figures Figure 1 Figure 2 Figure 3 Introduction The widespread proliferation of algorithmic recommendation applications (Apps) featuring infinite scrolling news feeds has raised significant concerns among both the public and scholars. From the perspective of Techno-Social Ecology, these applications are no longer simple information tools; instead, they have gradually embedded themselves into users’ daily lives, forming a complex ecosystem (Couldry & Mejias, 2020). Toutiao (Today’s Headlines) emerged as one of the earliest and most watched personalized news recommendation Apps with the slogan “what you care about is the headline” in 2012. Characterized by a lack of editorial staff, absence of content production, and no explicit stance or values, its operational core is a set of algorithms built from the code. Following Toutiao’s lead, similar algorithmic recommendation news Apps, such as Yidian (One-point Information) and Kuaibao (Daily Express), have surfaced. Short video Apps like Douyin (the Chinese version of TikTok, launched in 2016) utilize a funnel mechanism to push videos, sharing the decentralized recommendation algorithm principle with Toutiao. The Weibo microblog platform is based on recommendation algorithms for social networking. Its recommendations are tailored according to users’ historical data and preferences of similar user groups, promoting content, accounts, or intelligently sorting information. Techno-Social Ecology theory emphasizes that algorithmic recommendation technology, as an embedded medium, interacts with social structures and individual behaviors, profoundly shaping the ways users’ access and process information. In this ecosystem, news Apps, social media Apps, and short video Apps serve as the primary information channels, each fulfilling distinct functions of information access, social interaction, and entertainment content consumption. For this reason, these three types of algorithmic recommendation applications together form the core channels for information acquisition among users and have thus been selected as the primary media types in this study. These algorithm-driven applications share a distinctive feature: the infinite scroll interface. This design element enables users to continuously scroll through content without any clear indication of an endpoint, making it easier to keep scrolling than to stop. By eliminating natural pause cues for the brain, it becomes more challenging for users to disengage, while algorithmic personalized recommendations further reduce the cognitive effort required for decision-making (Abdollahpouri, 2021; Natarajan, 2024). The infinite scrolling interface, combined with algorithms and simplified interaction gestures, has created what is often referred to as a “frictionless” digital environment that minimizes operational barriers, immersing users in content consumption and potentially fostering a dependency on algorithmic recommendations, which in turn influences their news knowledge acquisition and information processing. Despite their role as important channels for information and knowledge acquisition in society (Carpini & Keeter, 1996), algorithm-driven applications have faced increasing scrutiny for the negative effects of their recommendation systems. Terms such as filter bubbles (Pariser, 2011), information cocoons (Sunstein, 2006), and echo chambers (Sunstein, 2002) describe the process by which people selectively obtain information from media, leading to a narrowing of information diversity. The assumption that users can fully engage with information—whether through active seeking or incidental news exposure—and thereby accumulate knowledge is increasingly questioned with the rise of algorithmic recommendation technologies. Users are not truly in a high-choice media environment, as proposed by Van Aelst et al. (2017), because algorithmic recommendations filter out a wealth of information, resulting in users receiving and accumulating limited and biased knowledge. People’s Daily’s (2018) critique of information cocoons has heightened public awareness of algorithms and sparked intense debate among experts and scholars in China. Therefore, this study focuses on examining how the usage of algorithmic recommendation Apps impacts news knowledge acquisition and explores users’ perceived information narrowing. As infinite scrolling algorithmic feeds increasingly shape the landscape of information consumption, it becomes essential to understand the effects of various types of algorithmic Apps on the dissemination of news knowledge, particularly their negative impacts. Building on the foundation of Media Dependency Theory (Ball-Rokeach & DeFleur, 1976), this study proposes the concept of Algorithm Dependency. Media Dependency Theory posits that users’ reliance on specific media is determined by its ability to meet their informational needs, a dependency that becomes especially pronounced in today’s complex information environment. In the context of algorithmic recommendations, this dependency evolves into algorithm dependency, wherein users' reliance on applications is reinforced not only by the medium itself but also by the intensifying effects of personalized recommendations. Through this theoretical framework, the study aims to reveal how "frictionless" algorithmic recommendation Apps foster usage dependency, thereby shaping users’ processes of news knowledge acquisition. It should be clarified that news knowledge in this study refers to an individual’s awareness of current social issues that have been prominently reported in mainstream media. It is measured by evaluating participants’ accuracy in identifying true or false statements about popular news topics. Literature Review Algorithm Dependence Theory and Algorithmic App Use Media System Dependence (MSD) theory, introduced by Ball-Rokeach and DeFleur in 1976, provides a foundational framework for understanding the nuanced relationship between individuals and media, emphasizing how audiences develop dependencies on media to fulfill specific needs or achieve goals (Ball-Rokeach & DeFleur, 1976 ; Ball-Rokeach, Rokeach, & Grube, 1984 ). This theory explains the dynamics of dependency in the context of both traditional and new media. Dependence on certain media or technology has been defined as the psychological and behavioral reliance that individuals develop in their daily lives, often manifesting as excessive use or addiction (Huang et al., 2024 ; Zhang, Yin, Zhang, Li, & Li, 2025 ). In the MSD framework, media usage has emerged as a significant predictor of media dependence. Empirical research has confirmed a positive correlation between media usage and dependence, across various media platforms and demographic groups (Aldamen, 2023 ; Bernart et al., 2016; Kim & Jung, 2017 ; Loges & Ball-Rokeach, 1993 ; Lowrey, 2004 ; Ng, 2022 ; Xie, Pentina, & Hancock, 2023 ). In the context of algorithmic media, dependency patterns evolve further, giving rise to what can be termed algorithmic dependence. Algorithmic feeds with infinite scrolling and personalized recommendations have been likened to a “pleasure machine”, deliberately designed to progressively satisfy user needs in order to manipulate users, identify vulnerabilities, cultivate compulsive habits, and exploit user attention (Bhargava & Velasquez, 2021; Helm & Matzner, 2023 ; Matzner, 2022 ). This argument has been discussed in the context of psychological hedonism (Gal, 2017 ; Reviglio & Agosti, 2020 ). Neuroscientific research has revealed that TikTok’s personalized video recommendations in contrast to non-personalized ones activate brain regions associated with self-referential processing and reward systems. This finding underpins how recommended algorithms are able to keep the user’s attention to suggested contents (Su et al., 2021 ). Guess and colleagues’ (2023) study also revealed that moving users out of algorithmic feeds to the reverse-chronologically-ordered feeds substantially decreased the time they spent on the platforms and their activity. Similarly, Rixen et al ( 2023 ) identified external and internal loops within algorithmic feeds: frequent App openings (external loop) and continuous scrolling (internal loop), where users enter for a specific purpose but end up browsing unconsciously. Based on this understanding, we propose the following hypotheses to investigate the relationship between the use of different types of algorithmic Apps and algorithmic dependence: H1a: Use of algorithmic news Apps is positively related to algorithm dependence. H1b: Use of algorithmic social media Apps is positively related to algorithm dependence. H1c: Use of algorithmic short video Apps is positively related to algorithm dependence. Impact of Different Types of Algorithmic Apps on News Knowledge Algorithmic News Apps Algorithmic news Apps, which tend to provide users with full news reports and articles, similar with traditional forms of news consumption like newspapers, television news, and online news sites, which have been found to increase knowledge, especially in political settings (Chaffee & Kanihan, 1997 ; Dimitrova et al., 2014 ). Studies have shown a positive relationship between the usage frequency of online websites that feature full-length news articles and factual knowledge (Andersen et al., 2016 ; Kaufhold et al., 2010 ). Although the Beam ( 2014 ) study showed a direct negative effect of personalized news recommender system use on knowledge, an indirect positive effect mediated by elaboration also exists when only recommended headlines are displayed. This suggests that algorithmic news Apps, by virtue of offering comprehensive news coverage, tend to foster deeper elaborations and retention of information, contributing positively to users’ news knowledge (Eveland, 2001 ). Algorithmic Social Media Apps By contrast, algorithmic social media Apps often provide snack news, requiring users to click on links to access full articles. This mode of news consumption has not shown the same positive effects on political knowledge as seen with full-length articles (Barberá et al., 2015 ; Dimitrova et al., 2014 ; Shehata & Stromback, 2018). Evidence suggests that while social media can increase incidental exposure to news, leading to some positive effects on information recall and recognition, these effects are contingent on users engaging with full-length stories (Lee & Kim, 2017 ). Consistently, it was found that News-Finds-Me perception does not influence climate change knowledge (Su et al., 2024 ). Only social media use for news rather than general social media use was found to be positively related to political knowledge, and news elaboration is a key mediator (Park & Kaye, 2019 ). Furthermore, social media’s partial control environment, characterized by personalized content that may reinforce confirmation biases (Kim et al., 2013 ) and limit exposure to diverse viewpoints, has been linked to lower levels of political knowledge (Cacciatore et al., 2018 ; Shehata & Strömbäck, 2018 ; Stroud, 2011 ). Thus, despite the potential for social media to guide users to news websites, the predominant use of social media for entertainment or relationship-oriented purposes (Bright, 2016 ) and the promotion of selective exposure to like-minded information may hinder substantive political learning (Pariser, 2011 ). As was criticized, social media is structured to encourage “agnotology”, the cultural creation of ignorance (John & Nissenbaum, 2019 ). Algorithmic Short Video Apps Algorithmic short video Apps present a unique case for examining the impact on users’ news knowledge, especially when compared to algorithmic news and social media Apps. Short video Apps, known for their heavily customized and entertainment-oriented content, align closely with the concept of snack news, which provides a brief overview of news topics through headlines, short teasers, and images (Schäfer et al., 2017 ). The format of short videos stands in stark contrast to the more comprehensive news coverage provided by algorithmic news Apps, and the mixed approach of algorithmic social media Apps that may offer both in-depth articles and snackable news. Engagement with short video Apps typically involves a low level of elaboration, as the content is designed for fast consumption, aiming to give users a superficial overview of current events rather than fostering a deep understanding (Dimitrova et al., 2014 ). Research indicates that reliance on snack news, prevalent on social media and short video platforms, is either unrelated or potentially harmful to the recall of political facts (Cacciatore et al., 2018 ; Dimitrova et al., 2011; Shehata & Strömbäck, 2018 ), suggesting a diminished capacity for these formats to contribute meaningfully to users’ news knowledge. It is worth noting that repeated encounters with the same news topics due to algorithms on social media or short video Apps create an illusion of knowing more than actual learning (Schäfer, 2020 ). Findings suggests that the inherent characteristics of short video Apps, primarily their focus on quick, entertaining news glimpses, may impede users’ ability to engage deeply with news content, limiting their understanding and retention of news knowledge. Given these considerations, the following hypotheses are proposed: H2a: The use of algorithmic news Apps is positively related to news knowledge. H2b: The use of algorithmic social media Apps is negatively related to news knowledge. H2c: Use of algorithmic short video Apps is negatively related to news knowledge. Algorithm Dependence and News Knowledge Early research based on newspaper and television media has shown from different perspectives how media dependency influences information acquisition, processing, and social behaviors. For example, Fry ( 1979 ) suggests that individuals with higher media dependence may have advantages in processing information, potentially widening the knowledge gap. Gaziano ( 1990 ) finds that lower income and less educated groups rely more on traditional media for news, which may limit their knowledge levels and social participation. Moy et al. ( 2005 ) emphasize that media dependence is also linked to trust and civic engagement, noting that highly dependent individuals may be more active in public affairs due to high trust in the media, but this could also weaken their critical thinking. These studies suggest that the relationship between media dependence and knowledge acquisition is quite complex. It is also underexplored the conditions under which media dependence may have positive or negative effects. With the shift to algorithm-driven media, dependency patterns evolve, algorithmic applications highlight the complex interplay between users’ reliance on these systems and the breadth of their news knowledge. While such systems may prioritize engagement over informational value, potentially leading to a filter bubble effect, which is defined as a cluster of information that has been filtered and selected based on predictions of who a user is and what they will do next (Eg et al., 2023 ). In the filter bubbles, users are exposed to the narrowing and fragmentation of news sources and perspectives, thereby negatively impacting their overall news knowledge (Geiß et al., 2021 ; Sunstein, 2007 ; Swart, 2021 ; Thorson & Wells, 2016 ). Moreover, once users develop algorithmic dependency, characterized by internal and external loops, they are prone to entering a state of mindless scrolling (Baym et al., 2020 ; de Segovia Vicente et al., 2024). As the technological processes of liking, commenting, and retweeting have been mechanistically integrated into buttons (Burgess & Baym, 2020 ), these actions have become increasingly habitual, blending voluntary and involuntary behaviors, both conscious and unconscious (Karppi, 2018 ). Passive scanning of incidentally encountered information is defined as first-level incidental news exposure, while intentional processing of incidentally encountered content appraised as relevant is defined as second-level incidental news exposure (Matthes et al. 2020 ). It was assumed that second-level incidental news exposure has stronger effects on knowledge acquisition than first-level incidental news exposure. However, within algorithm-driven information streams dominated by snack news, cognitive processing frequently remains at the lower first-level, making it difficult for users to transition to deeper processing when encountering more substantial content. This results in missed opportunities for meaningful engagement and hinders effective knowledge acquisition. Research has further revealed that user engagement with personalized news is low, with many participants admitting to superficial and incomplete reading of news stories (Oeldorf-Hirsch & Srinivasan, 2022 ). Based on this body of research, the following hypothesis is proposed: H3: Algorithm dependence is negatively related to news knowledge. The Moderation Effect of Perceived Information Narrowing on the Relationship Between Algorithmic App Use and Dependence Researchers generally agree that transferring control from platforms to users is a key strategy for mitigating the potentially problematic consequences of algorithms. It has been argued that operators have a responsibility to provide design solutions that more effectively raise user awareness and autonomy. Concepts such as algorithmic experience, which advocates transparent labelling of personalized content, and algorithmic sovereignty, which promotes user control over algorithmic processes, highlight the need for user empowerment in navigating algorithm-driven environments (Alvarado & Waern, 2018; Reviglio & Agosti, 2020 ). Mattis et al. ( 2024 ) advocate for personalizing diversity nudges within algorithmic recommender systems to enhance the diversity of users’ news consumption. Shifting from platform responsibility to user perspectives, studies have indicated the positive influence of users’ algorithm awareness and proactive control on news feed curation. For example, algorithmic awareness has been found to lead to more active engagement with Facebook, bolstered overall feelings of control on the site (Eslami et al., 2015 ). Furthermore, users who actively personalize their feeds tend to consume more diverse content (Merten, 2021 ), and awareness of algorithmic curation’s impact on content diversity influences trust and reliance on these platforms (Shin, 2021 ; Wölker & Powell, 2021 ). Rather than advocating for the complete abandonment of algorithmic Apps to avoid the negative impacts, researchers emphasize terms like mindful or conscious scrolling, encouraging individuals to observe and learn from their experiences, allowing them to create personal guidelines for their technology use (Baym et al., 2020 ; Rauch, 2018 ). With this awareness, individuals can create opportunities to break habits and change behaviors (Levy, 2016), thus, potentially improve their cognitive elaboration levels and gaining knowledge. Building on the advocacy for user control and mindfulness, this study examines perceived information narrowing as a form of user-empowered friction that could potentially mitigate excessive dependence on algorithmic Apps. Unlike algorithm awareness, which focuses on users’ general understanding of algorithmic functions, perceived information narrowing highlights the consequences of algorithmic personalization and offers a more nuanced reflection of mindful engagement with algorithmic Apps. More importantly, perceived information narrowing could serve as a rapid feedback mechanism from users to the algorithm, aiding in the adjustment of content diversity within the news feed. To explore this further, the following research questions are proposed: RQ: How does perceived information narrowing moderate the relationship between (1) algorithmic news App use, (2) algorithmic social media App use, (3) algorithmic short video App use and algorithm dependence? Current Study This study seeks to comparatively analyze the influence of different categories of algorithmic Apps—specifically news, social media, and short video Apps—on the public’s acquisition of news knowledge. Grounded in the hedonistic critique of infinite scrolling within algorithmic feeds, which posits that such designs exploit users' attention and foster dependency, this research investigates the mediating role of algorithm dependence in the relationship between App usage and knowledge acquisition. Furthermore, the study introduces the concept of user friction as a mechanism in the development of algorithm dependence, with perceived information narrowing functioning as a moderating factor. This framework highlights the importance of user mindfulness during App engagement as a proactive strategy for regulating algorithm-curated content. Figure 1 illustrates the conceptual framework of this study. Methods Data Collection In this study, snowball sampling was used, and questionnaires were distributed via the online survey platform wjx.cn in October 2019. In total, 354 participants responded to the survey. Participation in this online survey was voluntary and the survey responses were anonymous. The participants comprised 33.6% males and 66.4% females, 40.1% aged between 18 and 25 years, 44.8% aged between 26 and 40 years, 10.5% aged between 41 and 50 years, and 4.6% aged above 51 years. The majority had college or undergraduate education (55.1%), followed by postgraduate (39.5%), and high school or less (5.4%). Their daily time spent on Sina Microblog was 2.87 (SD = 1.38), time spent on TikTok was 2.19 (SD = 1.33), and time spent on TouTiao was 2.39 (SD = 1.30) on a 6-point scale shown in Table 1 . Table 1 Characteristics of the participants Percentage Gender 1 = Male 33.6 2 = Female 66.4 Age 1 = 18–25 40.1 2 = 26–40 44.8 3 = 41–50 10.5 4 = Above 51 4.6 Education 1 = Middle school or less 3.1 2 = High school 2.3 3 = College or university 55.1 4 = Postgraduate 39.5 Time spent on algorithmic news Apps per day (Mean = 2.39, SD = 1.30) 1 = Never 26.6 2 = Less than 30min 36.7 3 = 30-60min 19.8 4 = 1–2 hours 9.3 5 = 2–3 hours 3.1 6 = More than 3 hours 4.5 Time spent on algorithmic social media Apps per day (Mean = 2.87, SD = 1.38) 1 = Never 13.8 2 = Less than 30min 32.8 3 = 30-60min 27.1 4 = 1–2 hours 11.3 5 = 2–3 hours 8.5 6 = More than 3 hours 6.5 Time spent on algorithmic short video Apps per day (Mean = 2.19, SD = 1.33) 1 = Never 38.4 2 = Less than 30min 31.4 3 = 30-60min 15.0 4 = 1–2 hours 7.6 5 = 2–3 hours 3.7 6 = More than 3 hours 4.0 Measurements Use of algorithmic Apps The use of three types of Apps featuring algorithmic recommendations was measured: news Apps (e.g., Toutiao and Yidian), social media Apps (e.g., Sina Weibo), and short video Apps (e.g., TikTok and Kuaishou). Participants were asked to report their time spent on these three Apps per day (1 = never, 2 = less than 30 minutes, 3 = 30–60 minutes, 4 = 1–2 hours, 5 = 2–3 hours, 6 = more than 3 hours). News knowledge Referring to Beam ( 2014 ) as well as Lee and Kim ( 2016 ), news knowledge was measured by five true or false statements concerning social issues prominently reported in mainstream media at the time of survey implementation. Participants were asked to rate whether each statement was true, false, or uncertain. The statements included “Trump and Kim Jong-un met in Vietnam in February,” “Bingbing Fan was fined more than 800 million yuan for tax evasion,” and “Betel nut was a first-class carcinogen.” The number of correct answers was recorded to generate a score from 0 to 5, with higher scores indicating greater knowledge of the most popular news online at the time. Algorithm dependence The four-item scale measuring dependence on Apps with algorithmic recommendations was adapted from the scale for Facebook persistence and overuse (Orosz et al., 2016 ). This scale contains two items on behavioral and affective persistence (i.e., “I often search for Internet connections to visit these algorithmic Apps,” “I feel bad if I don’t check these algorithmic Apps daily”) and two items on excessive use (i.e., “I spent time on these algorithmic Apps at the expense of my obligations,” and “I used these algorithmic Apps instead of sleeping”). Participants evaluated these items on a five-point Likert scale (1 = completely disagree, 3 = neutral, 5 = completely agree). Cronbach’s alpha was .79. Perceived information narrowing Four items were adapted from the algorithmic media content awareness scale (Zarouali et al., 2021 ) and the perception of filter bubbles scale (Klug & Strang, 2019 ) measuring participants’ perceived information narrowing on the selected algorithmic Apps. The first two items focused on awareness of customization of the recommended content (“I feel that the recommended content is tailored for me”, “The recommended content matches my interests”) and the third and fourth items focused on the perception of filter bubbles (“The recommended content is becoming more and more similar”, “The recommended information is similar in themes”). Cronbach’s alpha was .65. Results Correlation Analysis As displayed in Table 2 , Pearson’s correlation analysis showed that news knowledge was negatively related to the use of algorithmic social media Apps (r = − .13, p < .05), use of algorithmic short video Apps (r = − .15, p < .01), and algorithm dependence (r = − .16, p < .01), while it was not significantly correlated with the use of algorithmic news Apps (r = − .01, p = .800) or perceived information narrowing (r = − .02, p = .670). Algorithm dependence was positively correlated with the use of algorithmic social media Apps (r = .40, p < .001), use of algorithmic short video Apps (r = .23, p < .001), and perceived information narrowing (r = .17, p < .01), but was not significantly correlated with the use of algorithmic news Apps (r = .10, p = .073). Table 2 Correlations among the key variables (1) (2) (3) (4) (5) (6) Use of algorithmic News Apps (1) 1 .50*** − .10 .15** .10 − .01 Use of algorithmic Social Media Apps (2) 1 .05 .11* .40*** − .13* Use of algorithmic Short Video Apps (3) 1 .10 .23*** − .15** Perceived Information Narrowing (4) 1 .17** − .02 Algorithm Dependence (5) 1 − .16** News Knowledge (6) 1 Note. * p < .05, ** p < .01, *** p < .001. Moderated Mediation Analysis on News Knowledge To examine the moderated mediation effects for the three types of algorithmic Apps on news knowledge, PROCESS Model 7 (Hayes, 2018 ) was used for data analysis. This model allows for testing a mediation effect with a moderating variable on the direct path between the independent variable and the mediator. Specifically, algorithmic App use was defined as the independent variable, algorithm dependence was defined as the mediator, news knowledge was defined as the dependent variable, and perceived information narrowing was included as a moderator between algorithmic App use and algorithm dependence. Results for each App category (news, social media, and short video) are displayed in Fig. 2. In the first model, news knowledge was predicted with use of algorithmic news Apps as the independent variable, algorithm dependence as the mediator, and perceived information narrowing as the moderator. The results showed that the direct link between news App use and news knowledge was nonsignificant (b = .002, p = .962). H1a was not supported. The relationship between use of news Apps and algorithm dependence is moderated by perceived information narrowing moderated this link (b = .15, p < .01). Specifically, post-hoc analysis showed that when the level of perceived information narrowing was high, the effect of the use of algorithmic news Apps on algorithm dependence was significant and positive (b = .395, p < .01), but when the level of perceived information narrowing was medium (b = .095, p = .469) or low (b = − .085, p = .583), the effect of use of algorithmic news Apps on algorithm dependence was nonsignificant. The interaction between the use of algorithmic news Apps and perceived information narrowing on algorithm dependence is shown in Fig. 3 . Thus, H2a was partially supported and RQ1 was responded positively. Algorithm dependence is negatively correlated with news knowledge (b = − .064, p < .01), supporting H3. In summary, there was a nonsignificant direct effect of algorithmic news App use on news knowledge (b = .002, p = .962), but a negative indirect effect on news knowledge mediated by algorithm dependence and moderated by perceived information narrowing (b = − .010, BootCI [-.019, − .002]), which was significant only among those with high levels of perceived information narrowing (b = − .025, BootCI [-.054, − .004]). Note b1 = coefficient in the news App model, b2 = coefficient in the social media App model, b3 = coefficient in the short video App model Figure 2. Relationships among use of algorithmic Apps, algorithm dependence, news knowledge, and perceived information narrowing In the second model, news knowledge was predicted with use of algorithmic social media Apps as the independent variable, algorithm dependence as the mediator, and perceived information narrowing as the moderator. The results showed that there was no direct effect of use of social media Apps on news knowledge (b = − .070, p = .178). H1b was not supported. The use of social media Apps was positively related to algorithmic dependence (b = 1.668, p < .05), algorithmic dependence was negatively related to news knowledge (b = − .052, p < .05), and perceived information narrowing had a nonsignificant moderating effect (b = − .058, p = .240). H2b and H3 was supported and RQ2 was responded negatively in this model. In sum, there was a nonsignificant direct effect of algorithmic social media App use on news knowledge (b = − .070, p = .178), whereas there was a negative indirect effect through algorithm dependence, which was not moderated by perceived information narrowing (b = .003, BootCI [-.004, .010]). In the third model, news knowledge was predicted with use of algorithmic short video Apps as the independent variable, algorithm dependence as the mediator, and perceived information narrowing as the moderator. The results showed that the direct effect of the use of short video Apps on news knowledge was significant and negative (b = − .109, p < .05), whereas the indirect effect of algorithm dependence (b = − .036, p = .956; b = − .054, p < .05) and the moderating effect of perceived information narrowing (b = − .040, p = .409) were not significant. H1c and H3 was supported, H2c was not supported, and RQ3 was responded negatively in this model. In sum, there was a negative direct effect of the use of algorithmic short video Apps on news knowledge (b = − .109, p < .05) but a nonsignificant indirect effect; thus, there was no significant moderated mediation (b = − .002, BootCI [-.010, .005]). Discussion This study investigated the effects of three distinct types of media platforms that utilize algorithmic recommendation systems on public news knowledge. Overall, this investigation presents a rather concerning portrayal of the public’s acquisition of news knowledge within the context of the algorithmic Apps under study. First, it was found that algorithmic Apps focused on short videos directly diminish users’ news knowledge. This effect is likely related to the primary entertainment-focused nature of these platforms. From a techno-social ecology perspective, the integration of algorithm-driven, short-form content into everyday routines contributes to a fragmented, entertainment-oriented media ecosystem that prioritizes engagement over informational depth. The consumption of snack news through these Apps tends to give users the illusion of being informed without actually providing them with in-depth factual knowledge (Schäfer, 2020 ). Second, this study found that algorithmic social media Apps indirectly reduce news knowledge by increasing user dependence on algorithms. This dependence facilitates the filter bubble effect, thus limiting the exposure to diverse perspectives and knowledge. Similar to short video Apps, the repetition of topics filled with snack news on algorithmic social media feeds may increase familiarity with certain news topics but does not necessarily translate into a well-rounded understanding, serving merely as a heuristic for judging knowledge (Metcalfe et al., 1993 ). Third, the relationship between the use of algorithmic news Apps and news knowledge is the most complex among the three categories studied. It was observed that, when users perceived a high degree of information narrowing, the use of algorithmic news Apps indirectly diminished news knowledge through increased algorithmic dependence. This finding is consistent with the techno-social ecology perspective, which posits that digital media environments embedded in everyday life can subtly shape patterns of information exposure and knowledge acquisition, reinforcing certain viewpoints while filtering out others. By contrast, in cases of low-to-moderate perceived information narrowing, the use of algorithmic news Apps does not significantly impact news knowledge. This suggests that even Apps designed primarily for news dissemination fail to effectively enhance news knowledge. Unlike traditional news websites, algorithmic news Apps follow algorithmic logic, which offers an infinite and personalized stream of headlines. Users must sift through numerous tailored headlines to find news that interests them, which can result in fragmented information intake and cognitive overload (Van Aelst et al., 2017 ). Moreover, readers still need to click on titles that interest them and read full articles for news elaboration and engagement to facilitate news knowledge gain. This step is paramount for transitioning from a superficial encounter with news to deeper understanding and retention of information. If only headlines are read, the information obtained remains fragmented, creating an illusion of knowledge. Algorithm dependence was found to be an important underlying mechanism that explains why the use of algorithmic Apps can potentially decrease news knowledge. Literature suggests that algorithmic experience encompasses three dimensions: cognitive, affective, and behavioral (Swart, 2021 ). Most existing literature explores the relationship between algorithms and knowledge from the perspective of cognitive processing, such as elaboration curation (Eveland, 2001 ; Park & Kaye, 2019 ). However, the algorithm-elaboration-knowledge is not the only path between algorithmic feeds and knowledge. Given that algorithmic Apps are fundamentally designed to promote hedonism and continuously exploit user attention, this study prioritizes the affective and behavioral dimensions of use, specifically the persistence and overuse of these Apps. From an algorithm dependency perspective, this affective and behavioral engagement fosters a reliance that undermines deep cognitive processing, as users become more accustomed to passive content consumption. By examining the mediating role of algorithm dependence in the relationship between algorithmic App use and knowledge acquisition, the findings confirm that affective and behavioral engagement with these Apps is detrimental to knowledge gain, contrasting with the positive effects of cognitive elaboration. The most intriguing and significant finding is the friction effect of user perception of information narrowing in safeguarding against algorithm dependence and diminished knowledge gain. It was found to be a key factor in reversing the generally negative impact of algorithmic Apps on knowledge gain. From a techno-social ecology viewpoint, this suggests that users’ awareness of content narrowing can serve as a self-regulating mechanism within the broader media ecosystem, helping to counterbalance the algorithm’s natural tendency toward homogeneity. Finding of the study showed that when using algorithmic news Apps, users who perceive a high degree of information narrowing are more likely to become behaviorally and affectively reliant on these Apps, leading to overuse and subsequently reduced knowledge acquisition. Conversely, users who perceive medium or low levels of information narrowing do not develop such dependence, thereby avoiding any negative impact on knowledge gain. This suggests that perceived information narrowing can function as a form of user friction, helping to regulate the extent of algorithmic customization and ultimately promoting knowledge acquisition. Additionally, perceived information narrowing may act as an early indicator of the filter-bubble effect, emphasizing the importance of empowering users to engage in proactive personalization. Current algorithmic Apps provide personalization settings that function much like a simple “on/off switch”. Turning off these settings or quit the Apps entirely can lead to considerable inconvenience and cost, often leading many users to keep personalization features activated to avoid these drawbacks. However, this study suggests that a more nuanced approach to personalization could be more effective and meaningful. A “faucet valve” type of switch to the content streams would be ideal, where users have the ability to finely adjust the degree of personalized content they receive. Algorithmic Apps and platforms should proactively provide users with periodic feedback alerts that enable them to report or assess their perceived degree of information narrowing during App use. This feedback can assist in adjusting the breadth of content delivered by the algorithms. When users report a high level of narrowing, algorithms should then expand content diversity to counteract this effect. This finding aligns with researchers’ advocacy for promoting news diversity through personalized nudges in algorithmic recommender systems, suggesting that such nudges can gradually increase the diversity of users’ news consumption, helping them develop new reading habits and explore novel interests (Mattis et al., 2024 ). This finding also supports the concept of algorithmic sovereignty which advocates users’ right to decide how, and to what extent, algorithms control online life (Reviglio & Agosti, 2020 ). While existing practices of customization on online platforms, services, and content are often performed without the user’s explicit choice, future efforts of algorithmic Apps should aim to actively offer explicit user friction choices within the infinite scrolling of algorithmic feeds (Lee et al., 2019 ; Merten, 2021 ; Zuiderveen Borgesius et al., 2016 ). This study had certain limitations. One was the inherent selection bias in our sample. The data collection method, online snowball sampling, yielded a participant pool skewed towards younger individuals with higher education levels, and caution must be exercised when extrapolating these results to broader populations, particularly older adults and those with lower educational attainment, who may exhibit different preferences and behaviors regarding algorithmic App usage. Besides, the cross-sectional design of the study does not rule out reversed causal mechanisms as explanations of the findings (e.g., participants with little knowledge might be more inclined to use short video apps). A longitudinal design would have been a better choice. Another limitation is the methodology employed to measure the usage of algorithmic Apps. While some studies suggest a negligible correlation between time spent on social media and dependence, engagement with social media activities—such as sharing, liking, and commenting—has been positively linked to dependence (Kim & Jung, 2017 ). Our research utilized time/frequency as metrics for algorithmic App usage, potentially overlooking the depth of user engagement. Future investigations could benefit from measuring algorithmic App use with detailed engagement into distinct categories, such as politics, entertainment, and sports, to ascertain their differential impacts on algorithm dependence. Furthermore, the timing of the study, which was conducted at the end of 2019, coincided with a period of intense scrutiny and debate over China’s algorithm recommendation technology. The subsequent introduction of regulatory measures, including the Personal Information Protection Law and the Internet Information Service Algorithm Recommendation Management Provisions, suggests that our conclusions may be most relevant to the early stages of algorithmic recommendation technology and a regulatory environment. Conclusion This study scrutinized the impact of news, social media, and short video algorithmic Apps on news knowledge, revealing that such systems might impede rather than enhance comprehensive news knowledge through different underlying mechanisms. Algorithmic dependence played a key mediating role. Our findings contribute to the understanding of algorithm dependence by illustrating its mediating role in the relationship between algorithmic App use and knowledge acquisition. The study highlights how reliance on algorithmic curation can fragment users’ informational environment, leading to cognitive shortcuts that limit comprehensive knowledge gain. Moreover, the moderating effect of perceived information narrowing suggests the potential for introducing a user friction mechanism to regulate algorithmic curation and mitigate the filter bubble effect, particularly within algorithmic news Apps. This psychological cue may prompt users to seek a broader range of information, acting as a self-regulatory prompt. To support this process, platforms could offer personalized settings that provide feedback on content diversity or suggestions for broadening information exposure. Within algorithmic news applications, such features could allow users to adjust the flow of personalized content, actively counteracting filter bubble effects and increasing news diversity. Declarations Ethical approval Approval for the online survey procedures of this study was obtained from the academic committee (act as ethics committee) of School of Humanities, Shanghai University of Finance and Economics on 1 st , September, 2019 (Approval number: 2019-9-1). All procedures involving human participants in the research were performed in accordance with the ethical standards of the institutional research committee and with the 1964 Declaration of Helsinki and its later amendments or comparable standards. Informed consent Informed consent was secured from participants upon the completion of the online survey in October 2019. The initial page of the online survey provided participants with a succinct written overview of the survey’s objectives and the organizing entity. Additionally, it included a confidentiality statement assuring that their responses would be kept confidential and solely utilized for academic research purposes. Consent for publication The authors consent to the publication of this manuscript and affirm that it represents honest and original work. Availability of data and materials The data underlying this article will be shared on reasonable request to the corresponding author. Competing interests The authors declare that there is no conflict of interest regarding the publication of this manuscript. Funding This research is funded by National Social Science Fund of China (Grant Number: 22CWX017). Acknowledgement Not applicable. References Abdollahpouri H, Burke R, Mobasher B (2021) User-centered evaluation of popularity bias in recommender systems. In Proceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization (pp. 119–129). ACM. https://doi.org/10.1145/3450613.3456831 Aldamen Y (2023) Understanding social media dependency, and uses and gratifications as a communication system in the migration era: Syrian refugees in host countries as a case study. Social Sci 12(6):322. https://doi.org/10.3390/socsci12060322 Andersen K, Bjarnoe C, Albak E, Moller S, Jensen L, Petersen T (2016) How news type matters: Indirect effects of media use on political participation through knowledge and efficacy. J Media Psychol 28(3):111–122. https://doi.org/10.1027/1864-1105/a000176 Ball-Rokeach SJ, DeFleur ML (1976) A dependency model of mass-media effects. Communication Res 3(1):3–21. https://doi.org/10.1177/009365027600300101 Ball-Rokeach SJ, Rokeach M, Grube JW (1984) The great American values test: Influencing behavior and belief through television. Free, New York, NY Barberá P, Jost JT, Nagler J, Tucker JA, Bonneau R (2015) Tweeting from left to right: Is online political communication more than an echo chamber? Psychol Sci 26(10):1531–1542. https://doi.org/10.1177/0956797615594620 Baym NK, Wagman KB, Persaud CJ (2020) Mindfully scrolling: Rethinking Facebook after time deactivated. Social Media + Soc 6(2):2056305120919105. https://doi.org/10.1177/2056305120919105 Beam MA (2014) Automating the news: How personalized news recommender system design choices impact news reception. Communication Res 41(8):1019–1041. https://doi.org/10.1177/0093650213497979 Bright J (2016) The social news gap: How news reading and news sharing diverge. J Communication 66(3):343–365. https://doi.org/10.1111/jcom.12232 Burgess J, Baym N (2020) Twitter: A biography. NYU, New York, NY Cacciatore MA, Yeo SK, Scheufele DA, Xenos MA, Brossard D, Corley EA (2018) Is Facebook making us dumber? Exploring social media use as a predictor of political knowledge. Journalism Mass Communication Q 95(2):404–424. https://doi.org/10.1177/1077699018770447 Carpini MXD, Keeter S (1996) What Americans know about politics and why it matters. Yale University Press, New Haven, CT Chaffee SH, Kanihan SF (1997) Learning about politics from the mass media. Political Communication 14(4):421–430. https://doi.org/10.1080/105846097199218 Couldry N, Mejias UA (2020) The costs of connection: How data are colonizing human life and appropriating it for capitalism. Stanford University Press Dimitrova DV, Shehata A, Strömbäck J, Nord LW (2014) The effects of digital media on political knowledge and participation in election campaigns: Evidence from panel data. Communication Res 41(1):95–118. https://doi.org/10.1177/0093650211426004 Eg R, Tønnesen ÖD, Tennfjord MK (2023) A scoping review of personalized user experiences on social media: The interplay between algorithms and human factors. Computers Hum Behav Rep 9:100253. https://doi.org/10.1016/j.chbr.2022.100253 Eslami M, Rickman A, Vaccaro K, Aleyasen A, Vuong A, Karahalios K, Hamilton K, Sandvig C (2015) April 18–23). I always assumed that I wasn’t really that close to [her]: Reasoning about invisible algorithms in news feeds. In: Proceedings of the 33rd annual ACM conference on human factors in computing systems (pp. 153–162), Seoul, Republic of Korea. https://doi.org/10.1145/2702123.2702556 Eveland WP Jr. (2001) The cognitive mediation model of learning from the news: Evidence from non-election, off-year election, and presidential election contexts. Communication Res 28(5):571–601. https://doi.org/10.1177/009365001028005001 Fry DL (1979) The knowledge gap hypothesis and media dependence: An initial study. Paper presented at the annual meeting of the Association for Education in Journalism and Mass Communication, Houston, TX. Gal MS (2017) Algorithmic challenges to autonomous choice. Mich Telecommunications Technol Law Rev 25:59–104. https://doi.org/10.36645/mtlr.25.1.algorithmic Gaziano C (1990) Media dependence for news: Some neglected groups. Mass Communication Rev 17(2):225–253 Geiß S, Magin M, Jürgens P, Stark B, Emmer M (2021) Loopholes in the echo chambers: How the echo chamber metaphor oversimplifies the effects of information gateways on opinion expression. Digit Journalism 9(5):660–686. https://doi.org/10.1080/21670811.2021.1872976 Guess AM et al (2023) How do social media feed algorithms affect attitudes and behavior in an election campaign? Science 381(6656):398–404. https://doi.org/10.1126/science.abc123 Hayes AF (2018) Introduction to mediation, moderation, and conditional process analysis: A regression-based approach, 2nd edn. The Guilford Press, New York, NY Helm P, Matzner T (2023) Co-addictive human–machine configurations: Relating critical design and algorithm studies to medical-psychiatric research on problematic Internet use. New Media Soc. https://doi.org/10.1177/14614448231165916 Huang S, Lai X, Ke L, Li Y, Wang H, Zhao X, Wang Y (2024) AI Technology panic—is AI dependence bad for mental health? A cross-lagged panel model and the mediating roles of motivations for AI use among adolescents. Psychol Res Behav Manage, 1087–1102 John NA, Nissenbaum A (2019) An agnotological analysis of APIs: Or, disconnectivity and the ideological limits of our knowledge of social media. Inform Soc 35(1):1–12. https://doi.org/10.1080/01972243.2018.1542647 Karppi T (2018) Disconnect: Facebook’s affective bonds. University of Minnesota Press, Minneapolis, MN Kaufhold K, Valenzuela S, de Zuniga HG (2010) Citizen journalism and democracy: How user-generated news use relates to political knowledge and participation. Journalism Mass Communication Q 87(3–4):515–529. https://doi.org/10.1177/107769901008700304 Kim Y, Chen HT, de Zuniga HG (2013) Stumbling upon news on the Internet: Effects of incidental news exposure and relative entertainment use on political engagement. Comput Hum Behav 29(6):2607–2614. https://doi.org/10.1016/j.chb.2013.06.005 Kim YC, Jung JY (2017) SNS dependency and interpersonal storytelling: An extension of media system dependency theory. New Media Soc 19(9):1458–1475. https://doi.org/10.1177/1461444816636611 Klug K, Strang C (2019) The filter bubble in social media communication: How users evaluate personalized information in the Facebook newsfeed. In Media Trust in a Digital World: Communication at Crossroads (pp. 159–180). Springer. https://doi.org/10.1007/978-3-319-70445-6_10 Lee EJ, Kim YW (2016) Effects of infographics on news elaboration, acquisition, and evaluation: Prior knowledge and issue involvement as moderators. New Media Soc 18(8):1579–1598. https://doi.org/10.1177/1461444814567982 Lee F, Chan M, Chen H, Li S (2019) Consumptive news feed curation on social media as proactive personalization: A study of six East Asian markets. Journalism Stud 20(15):2277–2292. https://doi.org/10.1080/1461670X.2019.1614851 Lee JK, Kim E (2017) Incidental exposure to news: Predictors in the social media setting and effects on information gain online. Comput Hum Behav 75:1008–1015. https://doi.org/10.1016/j.chb.2017.02.018 Loges WE, Ball-Rokeach SJ (1993) Dependency relations and newspaper readership. Journalism Q 70(3):602–614. https://doi.org/10.1177/107769909307000309 Lowrey W (2004) Media dependency during a large-scale social disruption: The case of September 11. Mass Communication Soc 7(3):339–357. https://doi.org/10.1207/s15327825mcs0703_5 Matthes J, Nanz A, Stubenvoll M, Heiss R (2020) Processing news on social media: The political incidental news exposure model (PINE). Journalism 21(8):1031–1048. https://doi.org/10.1177/1464884920919090 Mattis N, Masur P, Möller J, van Atteveldt W (2024) Nudging towards news diversity: A theoretical framework for facilitating diverse news consumption through recommender design. New Media Soc 26(7):3681–3706. https://doi.org/10.1177/14614448231130542 Matzner T (2022) Algorithms as complementary abstractions. New Media Soc. https://doi.org/10.1177/14614448221078604 Merten L (2021) Block, hide or follow—personal news curation practices on social media. Digit Journalism 9(8):1018–1039. https://doi.org/10.1080/21670811.2021.1907207 Metcalfe J, Schwartz BL, Joaquim SG (1993) The cue-familiarity heuristic in metacognition. J Exp Psychol 19(4):851–861. https://doi.org/10.1037/0278-7393.19.4.851 Moy P, Torres M, Tanaka K, McCluskey M (2005) Knowledge or trust? Investigating linkages between media reliance and participation. Communication Res 32(1):59–86. https://doi.org/10.1177/0093650204271399 Natarajan N (2024) Do they stop? How do they stop? Why do they stop? Whether, how, and why teens insert frictions into social media’s infinite scroll. Int J Communication 18:20. https://ijoc.org/index.php/ijoc/article/view/21618 Ng YL (2022) Exploring the association between use of conversational artificial intelligence and social capital: survey evidence from Hong Kong. New Media Soc 1(2):1–16 Oeldorf-Hirsch A, Srinivasan P (2022) An unavoidable convenience: How post-millennials engage with the news that finds them on social and mobile media. Journalism 23(9):1939–1954. https://doi.org/10.1177/1464884920909086 Orosz G, Tóth-Király I, Bőthe B (2016) Four facets of Facebook intensity—the development of the multidimensional Facebook intensity scale. Pers Indiv Differ 100:95–104. https://doi.org/10.1016/j.paid.2015.11.038 Pariser E (2011) The filter bubble: How the new personalized web is changing what we read and how we think. Penguin Park CS, Kaye BK (2019) Mediating roles of news curation and news elaboration in the relationship between social media use for news and political knowledge. J Broadcast Electron Media 63(3):455–473. https://doi.org/10.1080/08838151.2019.1653107 People’s Daily (2018), May 2 The Scream Effect and the Information Cocoon. People’s Daily Online. Retrieved August 23, 2024, from http://opinion.people.com.cn/n1/2018/0502/c1003-29959124.html Rauch J (2018) Slow media: Why slow is satisfying, sustainable, and smart. Oxford University Press, Oxford, UK Reviglio U, Agosti C (2020) Thinking outside the black-box: The case for algorithmic sovereignty in social media. Social Media + Soc 6(2):1–12. https://doi.org/10.1177/2056305120926630 Rixen JO et al (2023) The loop and reasons to break it: Investigating infinite scrolling behavior in social media applications and reasons to stop. Proceedings of the ACM on Human-Computer Interaction, 7 (MHCI), 1–22. https://doi.org/10.1145/35012345 Schäfer S, Sülflow M, Müller P (2017) The special taste of snack news: An application of niche theory to understand the appeal of Facebook as a news source. First Monday . http://dx.doi.org/10.5210/fm.v22i4.7431 Schäfer S (2020) Illusion of knowledge through Facebook news? Effects of snack news in a news feed on perceived knowledge, attitude strength, and willingness for discussions. Comput Hum Behav 103:1–12. https://doi.org/10.1016/j.chb.2019.08.019 Shehata A, Strömbäck J (2018) Learning political news from social media: Network media logic and current affairs news learning in a high-choice media environment. Communication Res 48(1):1–23. https://doi.org/10.1177/0093650217749354 Shin D (2021) The effects of explainability and causability on perception, trust, and acceptance: Implications for explainable AI. Int J Hum Comput Stud 146:102551. https://doi.org/10.1016/j.ijhcs.2020.102551 Stroud NJ (2011) Niche news: The politics of news choice. Oxford University Press, New York, NY Su C, Zhou H, Gong L, Teng B, Geng F, Hu Y (2021) Viewing personalized video clips recommended by TikTok activates default mode network and ventral tegmental area. NeuroImage 237:118136. https://doi.org/10.1016/j.neuroimage.2021.118136 Su Y, Luo C, Borah P (2024) Learning about climate change with algorithmic news? A two-wave panel study examining the role of news-finds-me perception. J Computer-Mediated Communication 29(5). https://doi.org/10.1093/jcmc/zmad045 Sunstein CR (2006) Infotopia: How many minds produce knowledge. Oxford University Press, New York, NY Sunstein CR (2007) Republic.com 2.0. Princeton University Press, Princeton, NJ Sunstein CR (2002) The law of group polarization. J Political Philos 10(2):175–195. https://doi.org/10.1111/1467-9760.00148 Swart J (2021) Experiencing algorithms: How young people understand, feel about, and engage with algorithmic news selection on social media. Social Media + Soc 7(2):1–11. https://doi.org/10.1177/20563051211008828 Thorson K, Wells C (2016) Curated flows: A framework for mapping media exposure in the digital age. Communication Theory 26(3):309–328. https://doi.org/10.1111/comt.12087 Van Aelst P, Stromback J, Aalberg T, Esser F, de Vreese C, Matthes J, Reinemann C (2017) Political communication in a high-choice media environment: A challenge for democracy? Annals Int Communication Association 41(1):3–27. https://doi.org/10.1080/23808985.2017.1288551 Wölker A, Powell TE (2021) Algorithms in the newsroom? News readers’ perceived credibility and selection of automated journalism. Journalism 22(1):86–103. https://doi.org/10.1177/1464884918757072 Xie T, Pentina I, Hancock T (2023) Friend, mentor, lover: does chatbot engagement lead to psychological dependence? J Service Manage 34(4):806–828. 10.1108/JOSM-02-2022-007232 Zarouali B, Boerman SC, de Vreese CH (2021) Is this recommended by an algorithm? The development and validation of the algorithmic media content awareness scale (AMCA-scale). Telematics Inform 62:101607. https://doi.org/10.1016/j.tele.2021.101607 Zhang X, Yin M, Zhang M, Li Z, Li H (2025) The Development and Validation of an Artificial Intelligence Chatbot Dependence Scale. Cyberpsychology Behav Social Netw 28(2):126–131 Zuiderveen Borgesius F, Trilling D, Möller J, Bodó B, de Vreese CH, Helberger N (2016) Should we worry about filter bubbles? Internet Policy Rev J Internet Regul 5(1). https://doi.org/10.14763/2016.1.401 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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1","display":"","copyAsset":false,"role":"figure","size":419654,"visible":true,"origin":"","legend":"\u003cp\u003eResearch conceptual framework\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6917973/v1/21e255c7583f993094cfac23.png"},{"id":97687739,"identity":"561835e9-46d7-43ec-b19b-29e08d6ef2aa","added_by":"auto","created_at":"2025-12-08 10:29:18","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":442534,"visible":true,"origin":"","legend":"\u003cp\u003eRelationshipsamong use of algorithmic Apps, algorithm dependence, news knowledge, and perceived information narrowing\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNote. \u003c/strong\u003eb1 = coefficient in the news App model, b2 = coefficient in the social media App model, b3 = coefficient in the short video App model\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-6917973/v1/fa53a82d66f2f5407b67e26d.png"},{"id":97892998,"identity":"14357435-f860-4684-ab0b-90740bd957a8","added_by":"auto","created_at":"2025-12-10 15:25:22","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":439436,"visible":true,"origin":"","legend":"\u003cp\u003eRelationship between use of algorithmic news Apps and algorithm dependence at different levels of perceived information narrowing\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-6917973/v1/86d1891dab1453a93f7aef2e.png"},{"id":99793412,"identity":"e855403c-53be-4a98-9321-e037774ba558","added_by":"auto","created_at":"2026-01-08 13:31:32","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2076054,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6917973/v1/3267f81c-f30f-4dcc-a40c-6a9c71cf5d7d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"User Friction in Infinite Scrolling Algorithmic Feeds: Examining the Impact of App Use and Algorithm Dependence on News Knowledge","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe widespread proliferation of algorithmic recommendation applications (Apps) featuring infinite scrolling news feeds has raised significant concerns among both the public and scholars. From the perspective of Techno-Social Ecology, these applications are no longer simple information tools; instead, they have gradually embedded themselves into users\u0026rsquo; daily lives, forming a complex ecosystem (Couldry \u0026amp; Mejias, 2020). Toutiao (Today\u0026rsquo;s Headlines) emerged as one of the earliest and most watched personalized news recommendation Apps with the slogan \u0026ldquo;what you care about is the headline\u0026rdquo; in 2012. Characterized by a lack of editorial staff, absence of content production, and no explicit stance or values, its operational core is a set of algorithms built from the code. Following Toutiao\u0026rsquo;s lead, similar algorithmic recommendation news Apps, such as Yidian (One-point Information) and Kuaibao (Daily Express), have surfaced. Short video Apps like Douyin (the Chinese version of TikTok, launched in 2016) utilize a funnel mechanism to push videos, sharing the decentralized recommendation algorithm principle with Toutiao. The Weibo microblog platform is based on recommendation algorithms for social networking. Its recommendations are tailored according to users\u0026rsquo; historical data and preferences of similar user groups, promoting content, accounts, or intelligently sorting information. Techno-Social Ecology theory emphasizes that algorithmic recommendation technology, as an embedded medium, interacts with social structures and individual behaviors, profoundly shaping the ways users\u0026rsquo; access and process information. In this ecosystem, news Apps, social media Apps, and short video Apps serve as the primary information channels, each fulfilling distinct functions of information access, social interaction, and entertainment content consumption. For this reason, these three types of algorithmic recommendation applications together form the core channels for information acquisition among users and have thus been selected as the primary media types in this study.\u003c/p\u003e\n\u003cp\u003eThese algorithm-driven applications share a distinctive feature: the infinite scroll interface. This design element enables users to continuously scroll through content without any clear indication of an endpoint, making it easier to keep scrolling than to stop. By eliminating natural pause cues for the brain, it becomes more challenging for users to disengage, while algorithmic personalized recommendations further reduce the cognitive effort required for decision-making (Abdollahpouri, 2021; Natarajan, 2024). The infinite scrolling interface, combined with algorithms and simplified interaction gestures, has created what is often referred to as a \u0026ldquo;frictionless\u0026rdquo; digital environment that minimizes operational barriers, immersing users in content consumption and potentially fostering a dependency on algorithmic recommendations, which in turn influences their news knowledge acquisition and information processing.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDespite their role as important channels for information and knowledge acquisition in society (Carpini \u0026amp; Keeter, 1996), algorithm-driven applications have faced increasing scrutiny for the negative effects of their recommendation systems. Terms such as filter bubbles (Pariser, 2011), information cocoons (Sunstein, 2006), and echo chambers (Sunstein, 2002) describe the process by which people selectively obtain information from media, leading to a narrowing of information diversity. The assumption that users can fully engage with information\u0026mdash;whether through active seeking or incidental news exposure\u0026mdash;and thereby accumulate knowledge is increasingly questioned with the rise of algorithmic recommendation technologies. Users are not truly in a high-choice media environment, as proposed by Van Aelst et al. (2017), because algorithmic recommendations filter out a wealth of information, resulting in users receiving and accumulating limited and biased knowledge. People\u0026rsquo;s Daily\u0026rsquo;s (2018) critique of information cocoons has heightened public awareness of algorithms and sparked intense debate among experts and scholars in China. Therefore, this study focuses on examining how the usage of algorithmic recommendation Apps impacts news knowledge acquisition and explores users\u0026rsquo; perceived information narrowing.\u003c/p\u003e\n\u003cp\u003eAs infinite scrolling algorithmic feeds increasingly shape the landscape of information consumption, it becomes essential to understand the effects of various types of algorithmic Apps on the dissemination of news knowledge, particularly their negative impacts. Building on the foundation of Media Dependency Theory (Ball-Rokeach \u0026amp; DeFleur, 1976), this study proposes the concept of Algorithm Dependency. Media Dependency Theory posits that users\u0026rsquo; reliance on specific media is determined by its ability to meet their informational needs, a dependency that becomes especially pronounced in today\u0026rsquo;s complex information environment. In the context of algorithmic recommendations, this dependency evolves into algorithm dependency, wherein users\u0026apos; reliance on applications is reinforced not only by the medium itself but also by the intensifying effects of personalized recommendations. Through this theoretical framework, the study aims to reveal how \u0026quot;frictionless\u0026quot; algorithmic recommendation Apps foster usage dependency, thereby shaping users\u0026rsquo; processes of news knowledge acquisition. It should be clarified that news knowledge in this study refers to an individual\u0026rsquo;s awareness of current social issues that have been prominently reported in mainstream media. It is measured by evaluating participants\u0026rsquo; accuracy in identifying true or false statements about popular news topics.\u003c/p\u003e"},{"header":"Literature Review","content":"\u003cdiv id=\"Sec2\" class=\"Section2\"\u003e\u003ch2\u003eAlgorithm Dependence Theory and Algorithmic App Use\u003c/h2\u003e\u003cp\u003eMedia System Dependence (MSD) theory, introduced by Ball-Rokeach and DeFleur in 1976, provides a foundational framework for understanding the nuanced relationship between individuals and media, emphasizing how audiences develop dependencies on media to fulfill specific needs or achieve goals (Ball-Rokeach \u0026amp; DeFleur, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e1976\u003c/span\u003e; Ball-Rokeach, Rokeach, \u0026amp; Grube, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e1984\u003c/span\u003e). This theory explains the dynamics of dependency in the context of both traditional and new media. Dependence on certain media or technology has been defined as the psychological and behavioral reliance that individuals develop in their daily lives, often manifesting as excessive use or addiction (Huang et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Zhang, Yin, Zhang, Li, \u0026amp; Li, \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). In the MSD framework, media usage has emerged as a significant predictor of media dependence. Empirical research has confirmed a positive correlation between media usage and dependence, across various media platforms and demographic groups (Aldamen, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Bernart et al., 2016; Kim \u0026amp; Jung, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Loges \u0026amp; Ball-Rokeach, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e1993\u003c/span\u003e; Lowrey, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Ng, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Xie, Pentina, \u0026amp; Hancock, \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn the context of algorithmic media, dependency patterns evolve further, giving rise to what can be termed algorithmic dependence. Algorithmic feeds with infinite scrolling and personalized recommendations have been likened to a \u0026ldquo;pleasure machine\u0026rdquo;, deliberately designed to progressively satisfy user needs in order to manipulate users, identify vulnerabilities, cultivate compulsive habits, and exploit user attention (Bhargava \u0026amp; Velasquez, 2021; Helm \u0026amp; Matzner, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Matzner, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). This argument has been discussed in the context of psychological hedonism (Gal, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Reviglio \u0026amp; Agosti, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Neuroscientific research has revealed that TikTok\u0026rsquo;s personalized video recommendations in contrast to non-personalized ones activate brain regions associated with self-referential processing and reward systems. This finding underpins how recommended algorithms are able to keep the user\u0026rsquo;s attention to suggested contents (Su et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Guess and colleagues\u0026rsquo; (2023) study also revealed that moving users out of algorithmic feeds to the reverse-chronologically-ordered feeds substantially decreased the time they spent on the platforms and their activity. Similarly, Rixen et al (\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) identified external and internal loops within algorithmic feeds: frequent App openings (external loop) and continuous scrolling (internal loop), where users enter for a specific purpose but end up browsing unconsciously.\u003c/p\u003e\u003cp\u003eBased on this understanding, we propose the following hypotheses to investigate the relationship between the use of different types of algorithmic Apps and algorithmic dependence:\u003c/p\u003e\u003cp\u003eH1a: Use of algorithmic news Apps is positively related to algorithm dependence.\u003c/p\u003e\u003cp\u003eH1b: Use of algorithmic social media Apps is positively related to algorithm dependence.\u003c/p\u003e\u003cp\u003eH1c: Use of algorithmic short video Apps is positively related to algorithm dependence.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eImpact of Different Types of Algorithmic Apps on News Knowledge\u003c/h2\u003e\u003cdiv id=\"Sec4\" class=\"Section3\"\u003e\u003ch2\u003eAlgorithmic News Apps\u003c/h2\u003e\u003cp\u003eAlgorithmic news Apps, which tend to provide users with full news reports and articles, similar with traditional forms of news consumption like newspapers, television news, and online news sites, which have been found to increase knowledge, especially in political settings (Chaffee \u0026amp; Kanihan, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e1997\u003c/span\u003e; Dimitrova et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Studies have shown a positive relationship between the usage frequency of online websites that feature full-length news articles and factual knowledge (Andersen et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Kaufhold et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Although the Beam (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) study showed a direct negative effect of personalized news recommender system use on knowledge, an indirect positive effect mediated by elaboration also exists when only recommended headlines are displayed. This suggests that algorithmic news Apps, by virtue of offering comprehensive news coverage, tend to foster deeper elaborations and retention of information, contributing positively to users\u0026rsquo; news knowledge (Eveland, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2001\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\n\u003ch3\u003eAlgorithmic Social Media Apps\u003c/h3\u003e\n\u003cp\u003eBy contrast, algorithmic social media Apps often provide snack news, requiring users to click on links to access full articles. This mode of news consumption has not shown the same positive effects on political knowledge as seen with full-length articles (Barber\u0026aacute; et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Dimitrova et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Shehata \u0026amp; Stromback, 2018). Evidence suggests that while social media can increase incidental exposure to news, leading to some positive effects on information recall and recognition, these effects are contingent on users engaging with full-length stories (Lee \u0026amp; Kim, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Consistently, it was found that News-Finds-Me perception does not influence climate change knowledge (Su et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Only social media use for news rather than general social media use was found to be positively related to political knowledge, and news elaboration is a key mediator (Park \u0026amp; Kaye, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Furthermore, social media\u0026rsquo;s partial control environment, characterized by personalized content that may reinforce confirmation biases (Kim et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) and limit exposure to diverse viewpoints, has been linked to lower levels of political knowledge (Cacciatore et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Shehata \u0026amp; Str\u0026ouml;mb\u0026auml;ck, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Stroud, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Thus, despite the potential for social media to guide users to news websites, the predominant use of social media for entertainment or relationship-oriented purposes (Bright, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) and the promotion of selective exposure to like-minded information may hinder substantive political learning (Pariser, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). As was criticized, social media is structured to encourage \u0026ldquo;agnotology\u0026rdquo;, the cultural creation of ignorance (John \u0026amp; Nissenbaum, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eAlgorithmic Short Video Apps\u003c/h3\u003e\n\u003cp\u003eAlgorithmic short video Apps present a unique case for examining the impact on users\u0026rsquo; news knowledge, especially when compared to algorithmic news and social media Apps. Short video Apps, known for their heavily customized and entertainment-oriented content, align closely with the concept of snack news, which provides a brief overview of news topics through headlines, short teasers, and images (Sch\u0026auml;fer et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The format of short videos stands in stark contrast to the more comprehensive news coverage provided by algorithmic news Apps, and the mixed approach of algorithmic social media Apps that may offer both in-depth articles and snackable news. Engagement with short video Apps typically involves a low level of elaboration, as the content is designed for fast consumption, aiming to give users a superficial overview of current events rather than fostering a deep understanding (Dimitrova et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Research indicates that reliance on snack news, prevalent on social media and short video platforms, is either unrelated or potentially harmful to the recall of political facts (Cacciatore et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Dimitrova et al., 2011; Shehata \u0026amp; Str\u0026ouml;mb\u0026auml;ck, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), suggesting a diminished capacity for these formats to contribute meaningfully to users\u0026rsquo; news knowledge. It is worth noting that repeated encounters with the same news topics due to algorithms on social media or short video Apps create an illusion of knowing more than actual learning (Sch\u0026auml;fer, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Findings suggests that the inherent characteristics of short video Apps, primarily their focus on quick, entertaining news glimpses, may impede users\u0026rsquo; ability to engage deeply with news content, limiting their understanding and retention of news knowledge.\u003c/p\u003e\u003cp\u003eGiven these considerations, the following hypotheses are proposed:\u003c/p\u003e\u003cp\u003eH2a: The use of algorithmic news Apps is positively related to news knowledge.\u003c/p\u003e\u003cp\u003eH2b: The use of algorithmic social media Apps is negatively related to news knowledge.\u003c/p\u003e\u003cp\u003eH2c: Use of algorithmic short video Apps is negatively related to news knowledge.\u003c/p\u003e\n\u003ch3\u003eAlgorithm Dependence and News Knowledge\u003c/h3\u003e\n\u003cp\u003eEarly research based on newspaper and television media has shown from different perspectives how media dependency influences information acquisition, processing, and social behaviors. For example, Fry (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e1979\u003c/span\u003e) suggests that individuals with higher media dependence may have advantages in processing information, potentially widening the knowledge gap. Gaziano (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e1990\u003c/span\u003e) finds that lower income and less educated groups rely more on traditional media for news, which may limit their knowledge levels and social participation. Moy et al. (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2005\u003c/span\u003e) emphasize that media dependence is also linked to trust and civic engagement, noting that highly dependent individuals may be more active in public affairs due to high trust in the media, but this could also weaken their critical thinking. These studies suggest that the relationship between media dependence and knowledge acquisition is quite complex. It is also underexplored the conditions under which media dependence may have positive or negative effects.\u003c/p\u003e\u003cp\u003eWith the shift to algorithm-driven media, dependency patterns evolve, algorithmic applications highlight the complex interplay between users\u0026rsquo; reliance on these systems and the breadth of their news knowledge. While such systems may prioritize engagement over informational value, potentially leading to a filter bubble effect, which is defined as a cluster of information that has been filtered and selected based on predictions of who a user is and what they will do next (Eg et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In the filter bubbles, users are exposed to the narrowing and fragmentation of news sources and perspectives, thereby negatively impacting their overall news knowledge (Gei\u0026szlig; et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Sunstein, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Swart, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Thorson \u0026amp; Wells, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eMoreover, once users develop algorithmic dependency, characterized by internal and external loops, they are prone to entering a state of mindless scrolling (Baym et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; de Segovia Vicente et al., 2024). As the technological processes of liking, commenting, and retweeting have been mechanistically integrated into buttons (Burgess \u0026amp; Baym, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), these actions have become increasingly habitual, blending voluntary and involuntary behaviors, both conscious and unconscious (Karppi, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Passive scanning of incidentally encountered information is defined as first-level incidental news exposure, while intentional processing of incidentally encountered content appraised as relevant is defined as second-level incidental news exposure (Matthes et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). It was assumed that second-level incidental news exposure has stronger effects on knowledge acquisition than first-level incidental news exposure. However, within algorithm-driven information streams dominated by snack news, cognitive processing frequently remains at the lower first-level, making it difficult for users to transition to deeper processing when encountering more substantial content. This results in missed opportunities for meaningful engagement and hinders effective knowledge acquisition. Research has further revealed that user engagement with personalized news is low, with many participants admitting to superficial and incomplete reading of news stories (Oeldorf-Hirsch \u0026amp; Srinivasan, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eBased on this body of research, the following hypothesis is proposed:\u003c/p\u003e\u003cp\u003eH3: Algorithm dependence is negatively related to news knowledge.\u003c/p\u003e\u003cp\u003e\u003cb\u003eThe Moderation Effect of Perceived Information Narrowing on the Relationship Between Algorithmic App Use and Dependence\u003c/b\u003e\u003c/p\u003e\u003cp\u003eResearchers generally agree that transferring control from platforms to users is a key strategy for mitigating the potentially problematic consequences of algorithms. It has been argued that operators have a responsibility to provide design solutions that more effectively raise user awareness and autonomy. Concepts such as algorithmic experience, which advocates transparent labelling of personalized content, and algorithmic sovereignty, which promotes user control over algorithmic processes, highlight the need for user empowerment in navigating algorithm-driven environments (Alvarado \u0026amp; Waern, 2018; Reviglio \u0026amp; Agosti, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Mattis et al. (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) advocate for personalizing diversity nudges within algorithmic recommender systems to enhance the diversity of users\u0026rsquo; news consumption.\u003c/p\u003e\u003cp\u003eShifting from platform responsibility to user perspectives, studies have indicated the positive influence of users\u0026rsquo; algorithm awareness and proactive control on news feed curation. For example, algorithmic awareness has been found to lead to more active engagement with Facebook, bolstered overall feelings of control on the site (Eslami et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Furthermore, users who actively personalize their feeds tend to consume more diverse content (Merten, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), and awareness of algorithmic curation\u0026rsquo;s impact on content diversity influences trust and reliance on these platforms (Shin, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; W\u0026ouml;lker \u0026amp; Powell, \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Rather than advocating for the complete abandonment of algorithmic Apps to avoid the negative impacts, researchers emphasize terms like mindful or conscious scrolling, encouraging individuals to observe and learn from their experiences, allowing them to create personal guidelines for their technology use (Baym et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Rauch, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). With this awareness, individuals can create opportunities to break habits and change behaviors (Levy, 2016), thus, potentially improve their cognitive elaboration levels and gaining knowledge.\u003c/p\u003e\u003cp\u003eBuilding on the advocacy for user control and mindfulness, this study examines perceived information narrowing as a form of user-empowered friction that could potentially mitigate excessive dependence on algorithmic Apps. Unlike algorithm awareness, which focuses on users\u0026rsquo; general understanding of algorithmic functions, perceived information narrowing highlights the consequences of algorithmic personalization and offers a more nuanced reflection of mindful engagement with algorithmic Apps. More importantly, perceived information narrowing could serve as a rapid feedback mechanism from users to the algorithm, aiding in the adjustment of content diversity within the news feed.\u003c/p\u003e\u003cp\u003eTo explore this further, the following research questions are proposed:\u003c/p\u003e\u003cp\u003eRQ: How does perceived information narrowing moderate the relationship between (1) algorithmic news App use, (2) algorithmic social media App use, (3) algorithmic short video App use and algorithm dependence?\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eCurrent Study\u003c/h2\u003e\u003cp\u003eThis study seeks to comparatively analyze the influence of different categories of algorithmic Apps\u0026mdash;specifically news, social media, and short video Apps\u0026mdash;on the public\u0026rsquo;s acquisition of news knowledge. Grounded in the hedonistic critique of infinite scrolling within algorithmic feeds, which posits that such designs exploit users' attention and foster dependency, this research investigates the mediating role of algorithm dependence in the relationship between App usage and knowledge acquisition. Furthermore, the study introduces the concept of user friction as a mechanism in the development of algorithm dependence, with perceived information narrowing functioning as a moderating factor. This framework highlights the importance of user mindfulness during App engagement as a proactive strategy for regulating algorithm-curated content. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e illustrates the conceptual framework of this study.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003eData Collection\u003c/h2\u003e\u003cp\u003eIn this study, snowball sampling was used, and questionnaires were distributed via the online survey platform wjx.cn in October 2019. In total, 354 participants responded to the survey. Participation in this online survey was voluntary and the survey responses were anonymous. The participants comprised 33.6% males and 66.4% females, 40.1% aged between 18 and 25 years, 44.8% aged between 26 and 40 years, 10.5% aged between 41 and 50 years, and 4.6% aged above 51 years. The majority had college or undergraduate education (55.1%), followed by postgraduate (39.5%), and high school or less (5.4%). Their daily time spent on Sina Microblog was 2.87 (SD\u0026thinsp;=\u0026thinsp;1.38), time spent on TikTok was 2.19 (SD\u0026thinsp;=\u0026thinsp;1.33), and time spent on TouTiao was 2.39 (SD\u0026thinsp;=\u0026thinsp;1.30) on a 6-point scale shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eCharacteristics of the participants\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePercentage\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eGender\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u0026thinsp;=\u0026thinsp;Male\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e33.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2\u0026thinsp;=\u0026thinsp;Female\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e66.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u0026thinsp;=\u0026thinsp;18\u0026ndash;25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e40.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2\u0026thinsp;=\u0026thinsp;26\u0026ndash;40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e44.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3\u0026thinsp;=\u0026thinsp;41\u0026ndash;50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e10.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4\u0026thinsp;=\u0026thinsp;Above 51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003eEducation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u0026thinsp;=\u0026thinsp;Middle school or less\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2\u0026thinsp;=\u0026thinsp;High school\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3\u0026thinsp;=\u0026thinsp;College or university\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e55.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4\u0026thinsp;=\u0026thinsp;Postgraduate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e39.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e\u003cp\u003eTime spent on algorithmic news Apps per day\u003c/p\u003e\u003cp\u003e(Mean\u0026thinsp;=\u0026thinsp;2.39, SD\u0026thinsp;=\u0026thinsp;1.30)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u0026thinsp;=\u0026thinsp;Never\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e26.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2\u0026thinsp;=\u0026thinsp;Less than 30min\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e36.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3\u0026thinsp;=\u0026thinsp;30-60min\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e19.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4\u0026thinsp;=\u0026thinsp;1\u0026ndash;2 hours\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e9.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5\u0026thinsp;=\u0026thinsp;2\u0026ndash;3 hours\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6\u0026thinsp;=\u0026thinsp;More than 3 hours\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e\u003cp\u003eTime spent on algorithmic social media Apps per day\u003c/p\u003e\u003cp\u003e(Mean\u0026thinsp;=\u0026thinsp;2.87, SD\u0026thinsp;=\u0026thinsp;1.38)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u0026thinsp;=\u0026thinsp;Never\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e13.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2\u0026thinsp;=\u0026thinsp;Less than 30min\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e32.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3\u0026thinsp;=\u0026thinsp;30-60min\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e27.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4\u0026thinsp;=\u0026thinsp;1\u0026ndash;2 hours\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e11.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5\u0026thinsp;=\u0026thinsp;2\u0026ndash;3 hours\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6\u0026thinsp;=\u0026thinsp;More than 3 hours\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e\u003cp\u003eTime spent on algorithmic short video Apps per day\u003c/p\u003e\u003cp\u003e(Mean\u0026thinsp;=\u0026thinsp;2.19, SD\u0026thinsp;=\u0026thinsp;1.33)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u0026thinsp;=\u0026thinsp;Never\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e38.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2\u0026thinsp;=\u0026thinsp;Less than 30min\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e31.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3\u0026thinsp;=\u0026thinsp;30-60min\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e15.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4\u0026thinsp;=\u0026thinsp;1\u0026ndash;2 hours\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5\u0026thinsp;=\u0026thinsp;2\u0026ndash;3 hours\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6\u0026thinsp;=\u0026thinsp;More than 3 hours\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eMeasurements\u003c/h2\u003e\u003cdiv id=\"Sec12\" class=\"Section3\"\u003e\u003ch2\u003eUse of algorithmic Apps\u003c/h2\u003e\u003cp\u003eThe use of three types of Apps featuring algorithmic recommendations was measured: news Apps (e.g., Toutiao and Yidian), social media Apps (e.g., Sina Weibo), and short video Apps (e.g., TikTok and Kuaishou). Participants were asked to report their time spent on these three Apps per day (1\u0026thinsp;=\u0026thinsp;never, 2\u0026thinsp;=\u0026thinsp;less than 30 minutes, 3\u0026thinsp;=\u0026thinsp;30\u0026ndash;60 minutes, 4\u0026thinsp;=\u0026thinsp;1\u0026ndash;2 hours, 5\u0026thinsp;=\u0026thinsp;2\u0026ndash;3 hours, 6\u0026thinsp;=\u0026thinsp;more than 3 hours).\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eNews knowledge\u003c/h2\u003e\u003cp\u003eReferring to Beam (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) as well as Lee and Kim (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), news knowledge was measured by five true or false statements concerning social issues prominently reported in mainstream media at the time of survey implementation. Participants were asked to rate whether each statement was true, false, or uncertain. The statements included \u0026ldquo;Trump and Kim Jong-un met in Vietnam in February,\u0026rdquo; \u0026ldquo;Bingbing Fan was fined more than 800\u0026nbsp;million yuan for tax evasion,\u0026rdquo; and \u0026ldquo;Betel nut was a first-class carcinogen.\u0026rdquo; The number of correct answers was recorded to generate a score from 0 to 5, with higher scores indicating greater knowledge of the most popular news online at the time.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eAlgorithm dependence\u003c/h2\u003e\u003cp\u003eThe four-item scale measuring dependence on Apps with algorithmic recommendations was adapted from the scale for Facebook persistence and overuse (Orosz et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). This scale contains two items on behavioral and affective persistence (i.e., \u0026ldquo;I often search for Internet connections to visit these algorithmic Apps,\u0026rdquo; \u0026ldquo;I feel bad if I don\u0026rsquo;t check these algorithmic Apps daily\u0026rdquo;) and two items on excessive use (i.e., \u0026ldquo;I spent time on these algorithmic Apps at the expense of my obligations,\u0026rdquo; and \u0026ldquo;I used these algorithmic Apps instead of sleeping\u0026rdquo;). Participants evaluated these items on a five-point Likert scale (1\u0026thinsp;=\u0026thinsp;completely disagree, 3\u0026thinsp;=\u0026thinsp;neutral, 5\u0026thinsp;=\u0026thinsp;completely agree). Cronbach\u0026rsquo;s alpha was .79.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003ePerceived information narrowing\u003c/h2\u003e\u003cp\u003eFour items were adapted from the algorithmic media content awareness scale (Zarouali et al., \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and the perception of filter bubbles scale (Klug \u0026amp; Strang, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) measuring participants\u0026rsquo; perceived information narrowing on the selected algorithmic Apps. The first two items focused on awareness of customization of the recommended content (\u0026ldquo;I feel that the recommended content is tailored for me\u0026rdquo;, \u0026ldquo;The recommended content matches my interests\u0026rdquo;) and the third and fourth items focused on the perception of filter bubbles (\u0026ldquo;The recommended content is becoming more and more similar\u0026rdquo;, \u0026ldquo;The recommended information is similar in themes\u0026rdquo;). Cronbach\u0026rsquo;s alpha was .65.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003eCorrelation Analysis\u003c/h2\u003e\u003cp\u003eAs displayed in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Pearson\u0026rsquo;s correlation analysis showed that news knowledge was negatively related to the use of algorithmic social media Apps (r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;.13, p\u0026thinsp;\u0026lt;\u0026thinsp;.05), use of algorithmic short video Apps (r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;.15, p\u0026thinsp;\u0026lt;\u0026thinsp;.01), and algorithm dependence (r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;.16, p\u0026thinsp;\u0026lt;\u0026thinsp;.01), while it was not significantly correlated with the use of algorithmic news Apps (r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;.01, p\u0026thinsp;=\u0026thinsp;.800) or perceived information narrowing (r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;.02, p\u0026thinsp;=\u0026thinsp;.670). Algorithm dependence was positively correlated with the use of algorithmic social media Apps (r\u0026thinsp;=\u0026thinsp;.40, p\u0026thinsp;\u0026lt;\u0026thinsp;.001), use of algorithmic short video Apps (r\u0026thinsp;=\u0026thinsp;.23, p\u0026thinsp;\u0026lt;\u0026thinsp;.001), and perceived information narrowing (r\u0026thinsp;=\u0026thinsp;.17, p\u0026thinsp;\u0026lt;\u0026thinsp;.01), but was not significantly correlated with the use of algorithmic news Apps (r\u0026thinsp;=\u0026thinsp;.10, p\u0026thinsp;=\u0026thinsp;.073).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eCorrelations among the key variables\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(1)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(2)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(3)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(4)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(5)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(6)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUse of algorithmic News Apps (1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.50***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026minus;\u0026thinsp;.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.15**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026minus;\u0026thinsp;.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUse of algorithmic Social Media Apps (2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.11*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.40***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026minus;\u0026thinsp;.13*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUse of algorithmic Short Video Apps (3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.23***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026minus;\u0026thinsp;.15**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePerceived Information Narrowing (4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.17**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026minus;\u0026thinsp;.02\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAlgorithm Dependence (5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026minus;\u0026thinsp;.16**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNews Knowledge (6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003cb\u003eNote. * p\u0026thinsp;\u0026lt;\u0026thinsp;.05, ** p\u0026thinsp;\u0026lt;\u0026thinsp;.01, *** p\u0026thinsp;\u0026lt;\u0026thinsp;.001.\u003c/b\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003eModerated Mediation Analysis on News Knowledge\u003c/h2\u003e\u003cp\u003eTo examine the moderated mediation effects for the three types of algorithmic Apps on news knowledge, PROCESS Model 7 (Hayes, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) was used for data analysis. This model allows for testing a mediation effect with a moderating variable on the direct path between the independent variable and the mediator. Specifically, algorithmic App use was defined as the independent variable, algorithm dependence was defined as the mediator, news knowledge was defined as the dependent variable, and perceived information narrowing was included as a moderator between algorithmic App use and algorithm dependence. Results for each App category (news, social media, and short video) are displayed in Fig.\u0026nbsp;2.\u003c/p\u003e\u003cp\u003eIn the first model, news knowledge was predicted with use of algorithmic news Apps as the independent variable, algorithm dependence as the mediator, and perceived information narrowing as the moderator. The results showed that the direct link between news App use and news knowledge was nonsignificant (b\u0026thinsp;=\u0026thinsp;.002, p\u0026thinsp;=\u0026thinsp;.962). H1a was not supported. The relationship between use of news Apps and algorithm dependence is moderated by perceived information narrowing moderated this link (b\u0026thinsp;=\u0026thinsp;.15, p\u0026thinsp;\u0026lt;\u0026thinsp;.01). Specifically, post-hoc analysis showed that when the level of perceived information narrowing was high, the effect of the use of algorithmic news Apps on algorithm dependence was significant and positive (b\u0026thinsp;=\u0026thinsp;.395, p\u0026thinsp;\u0026lt;\u0026thinsp;.01), but when the level of perceived information narrowing was medium (b\u0026thinsp;=\u0026thinsp;.095, p\u0026thinsp;=\u0026thinsp;.469) or low (b\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;.085, p\u0026thinsp;=\u0026thinsp;.583), the effect of use of algorithmic news Apps on algorithm dependence was nonsignificant. The interaction between the use of algorithmic news Apps and perceived information narrowing on algorithm dependence is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Thus, H2a was partially supported and RQ1 was responded positively. Algorithm dependence is negatively correlated with news knowledge (b\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;.064, p\u0026thinsp;\u0026lt;\u0026thinsp;.01), supporting H3. In summary, there was a nonsignificant direct effect of algorithmic news App use on news knowledge (b\u0026thinsp;=\u0026thinsp;.002, p\u0026thinsp;=\u0026thinsp;.962), but a negative indirect effect on news knowledge mediated by algorithm dependence and moderated by perceived information narrowing (b\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;.010, BootCI [-.019, \u0026minus;\u0026thinsp;.002]), which was significant only among those with high levels of perceived information narrowing (b\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;.025, BootCI [-.054, \u0026minus;\u0026thinsp;.004]).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eNote\u003c/strong\u003e\u003cp\u003eb1\u0026thinsp;=\u0026thinsp;coefficient in the news App model, b2\u0026thinsp;=\u0026thinsp;coefficient in the social media App model, b3\u0026thinsp;=\u0026thinsp;coefficient in the short video App model\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eFigure 2.\u003c/b\u003e Relationships among use of algorithmic Apps, algorithm dependence, news knowledge, and perceived information narrowing\u003c/p\u003e\u003cp\u003eIn the second model, news knowledge was predicted with use of algorithmic social media Apps as the independent variable, algorithm dependence as the mediator, and perceived information narrowing as the moderator. The results showed that there was no direct effect of use of social media Apps on news knowledge (b\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;.070, p\u0026thinsp;=\u0026thinsp;.178). H1b was not supported. The use of social media Apps was positively related to algorithmic dependence (b\u0026thinsp;=\u0026thinsp;1.668, p\u0026thinsp;\u0026lt;\u0026thinsp;.05), algorithmic dependence was negatively related to news knowledge (b\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;.052, p\u0026thinsp;\u0026lt;\u0026thinsp;.05), and perceived information narrowing had a nonsignificant moderating effect (b\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;.058, p\u0026thinsp;=\u0026thinsp;.240). H2b and H3 was supported and RQ2 was responded negatively in this model. In sum, there was a nonsignificant direct effect of algorithmic social media App use on news knowledge (b\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;.070, p\u0026thinsp;=\u0026thinsp;.178), whereas there was a negative indirect effect through algorithm dependence, which was not moderated by perceived information narrowing (b\u0026thinsp;=\u0026thinsp;.003, BootCI [-.004, .010]).\u003c/p\u003e\u003cp\u003eIn the third model, news knowledge was predicted with use of algorithmic short video Apps as the independent variable, algorithm dependence as the mediator, and perceived information narrowing as the moderator. The results showed that the direct effect of the use of short video Apps on news knowledge was significant and negative (b\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;.109, p\u0026thinsp;\u0026lt;\u0026thinsp;.05), whereas the indirect effect of algorithm dependence (b\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;.036, p\u0026thinsp;=\u0026thinsp;.956; b\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;.054, p\u0026thinsp;\u0026lt;\u0026thinsp;.05) and the moderating effect of perceived information narrowing (b\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;.040, p\u0026thinsp;=\u0026thinsp;.409) were not significant. H1c and H3 was supported, H2c was not supported, and RQ3 was responded negatively in this model. In sum, there was a negative direct effect of the use of algorithmic short video Apps on news knowledge (b\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;.109, p\u0026thinsp;\u0026lt;\u0026thinsp;.05) but a nonsignificant indirect effect; thus, there was no significant moderated mediation (b\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;.002, BootCI [-.010, .005]).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study investigated the effects of three distinct types of media platforms that utilize algorithmic recommendation systems on public news knowledge. Overall, this investigation presents a rather concerning portrayal of the public\u0026rsquo;s acquisition of news knowledge within the context of the algorithmic Apps under study.\u003c/p\u003e\u003cp\u003eFirst, it was found that algorithmic Apps focused on short videos directly diminish users\u0026rsquo; news knowledge. This effect is likely related to the primary entertainment-focused nature of these platforms. From a techno-social ecology perspective, the integration of algorithm-driven, short-form content into everyday routines contributes to a fragmented, entertainment-oriented media ecosystem that prioritizes engagement over informational depth. The consumption of snack news through these Apps tends to give users the illusion of being informed without actually providing them with in-depth factual knowledge (Sch\u0026auml;fer, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eSecond, this study found that algorithmic social media Apps indirectly reduce news knowledge by increasing user dependence on algorithms. This dependence facilitates the filter bubble effect, thus limiting the exposure to diverse perspectives and knowledge. Similar to short video Apps, the repetition of topics filled with snack news on algorithmic social media feeds may increase familiarity with certain news topics but does not necessarily translate into a well-rounded understanding, serving merely as a heuristic for judging knowledge (Metcalfe et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e1993\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThird, the relationship between the use of algorithmic news Apps and news knowledge is the most complex among the three categories studied. It was observed that, when users perceived a high degree of information narrowing, the use of algorithmic news Apps indirectly diminished news knowledge through increased algorithmic dependence. This finding is consistent with the techno-social ecology perspective, which posits that digital media environments embedded in everyday life can subtly shape patterns of information exposure and knowledge acquisition, reinforcing certain viewpoints while filtering out others. By contrast, in cases of low-to-moderate perceived information narrowing, the use of algorithmic news Apps does not significantly impact news knowledge. This suggests that even Apps designed primarily for news dissemination fail to effectively enhance news knowledge. Unlike traditional news websites, algorithmic news Apps follow algorithmic logic, which offers an infinite and personalized stream of headlines. Users must sift through numerous tailored headlines to find news that interests them, which can result in fragmented information intake and cognitive overload (Van Aelst et al., \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Moreover, readers still need to click on titles that interest them and read full articles for news elaboration and engagement to facilitate news knowledge gain. This step is paramount for transitioning from a superficial encounter with news to deeper understanding and retention of information. If only headlines are read, the information obtained remains fragmented, creating an illusion of knowledge.\u003c/p\u003e\u003cp\u003eAlgorithm dependence was found to be an important underlying mechanism that explains why the use of algorithmic Apps can potentially decrease news knowledge. Literature suggests that algorithmic experience encompasses three dimensions: cognitive, affective, and behavioral (Swart, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Most existing literature explores the relationship between algorithms and knowledge from the perspective of cognitive processing, such as elaboration curation (Eveland, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Park \u0026amp; Kaye, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). However, the algorithm-elaboration-knowledge is not the only path between algorithmic feeds and knowledge. Given that algorithmic Apps are fundamentally designed to promote hedonism and continuously exploit user attention, this study prioritizes the affective and behavioral dimensions of use, specifically the persistence and overuse of these Apps. From an algorithm dependency perspective, this affective and behavioral engagement fosters a reliance that undermines deep cognitive processing, as users become more accustomed to passive content consumption. By examining the mediating role of algorithm dependence in the relationship between algorithmic App use and knowledge acquisition, the findings confirm that affective and behavioral engagement with these Apps is detrimental to knowledge gain, contrasting with the positive effects of cognitive elaboration.\u003c/p\u003e\u003cp\u003eThe most intriguing and significant finding is the friction effect of user perception of information narrowing in safeguarding against algorithm dependence and diminished knowledge gain. It was found to be a key factor in reversing the generally negative impact of algorithmic Apps on knowledge gain. From a techno-social ecology viewpoint, this suggests that users\u0026rsquo; awareness of content narrowing can serve as a self-regulating mechanism within the broader media ecosystem, helping to counterbalance the algorithm\u0026rsquo;s natural tendency toward homogeneity. Finding of the study showed that when using algorithmic news Apps, users who perceive a high degree of information narrowing are more likely to become behaviorally and affectively reliant on these Apps, leading to overuse and subsequently reduced knowledge acquisition. Conversely, users who perceive medium or low levels of information narrowing do not develop such dependence, thereby avoiding any negative impact on knowledge gain. This suggests that perceived information narrowing can function as a form of user friction, helping to regulate the extent of algorithmic customization and ultimately promoting knowledge acquisition. Additionally, perceived information narrowing may act as an early indicator of the filter-bubble effect, emphasizing the importance of empowering users to engage in proactive personalization. Current algorithmic Apps provide personalization settings that function much like a simple \u0026ldquo;on/off switch\u0026rdquo;. Turning off these settings or quit the Apps entirely can lead to considerable inconvenience and cost, often leading many users to keep personalization features activated to avoid these drawbacks. However, this study suggests that a more nuanced approach to personalization could be more effective and meaningful. A \u0026ldquo;faucet valve\u0026rdquo; type of switch to the content streams would be ideal, where users have the ability to finely adjust the degree of personalized content they receive. Algorithmic Apps and platforms should proactively provide users with periodic feedback alerts that enable them to report or assess their perceived degree of information narrowing during App use. This feedback can assist in adjusting the breadth of content delivered by the algorithms. When users report a high level of narrowing, algorithms should then expand content diversity to counteract this effect. This finding aligns with researchers\u0026rsquo; advocacy for promoting news diversity through personalized nudges in algorithmic recommender systems, suggesting that such nudges can gradually increase the diversity of users\u0026rsquo; news consumption, helping them develop new reading habits and explore novel interests (Mattis et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This finding also supports the concept of algorithmic sovereignty which advocates users\u0026rsquo; right to decide how, and to what extent, algorithms control online life (Reviglio \u0026amp; Agosti, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). While existing practices of customization on online platforms, services, and content are often performed without the user\u0026rsquo;s explicit choice, future efforts of algorithmic Apps should aim to actively offer explicit user friction choices within the infinite scrolling of algorithmic feeds (Lee et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Merten, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Zuiderveen Borgesius et al., \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThis study had certain limitations. One was the inherent selection bias in our sample. The data collection method, online snowball sampling, yielded a participant pool skewed towards younger individuals with higher education levels, and caution must be exercised when extrapolating these results to broader populations, particularly older adults and those with lower educational attainment, who may exhibit different preferences and behaviors regarding algorithmic App usage. Besides, the cross-sectional design of the study does not rule out reversed causal mechanisms as explanations of the findings (e.g., participants with little knowledge might be more inclined to use short video apps). A longitudinal design would have been a better choice. Another limitation is the methodology employed to measure the usage of algorithmic Apps. While some studies suggest a negligible correlation between time spent on social media and dependence, engagement with social media activities\u0026mdash;such as sharing, liking, and commenting\u0026mdash;has been positively linked to dependence (Kim \u0026amp; Jung, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Our research utilized time/frequency as metrics for algorithmic App usage, potentially overlooking the depth of user engagement. Future investigations could benefit from measuring algorithmic App use with detailed engagement into distinct categories, such as politics, entertainment, and sports, to ascertain their differential impacts on algorithm dependence. Furthermore, the timing of the study, which was conducted at the end of 2019, coincided with a period of intense scrutiny and debate over China\u0026rsquo;s algorithm recommendation technology. The subsequent introduction of regulatory measures, including the Personal Information Protection Law and the Internet Information Service Algorithm Recommendation Management Provisions, suggests that our conclusions may be most relevant to the early stages of algorithmic recommendation technology and a regulatory environment.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study scrutinized the impact of news, social media, and short video algorithmic Apps on news knowledge, revealing that such systems might impede rather than enhance comprehensive news knowledge through different underlying mechanisms. Algorithmic dependence played a key mediating role. Our findings contribute to the understanding of algorithm dependence by illustrating its mediating role in the relationship between algorithmic App use and knowledge acquisition. The study highlights how reliance on algorithmic curation can fragment users\u0026rsquo; informational environment, leading to cognitive shortcuts that limit comprehensive knowledge gain. Moreover, the moderating effect of perceived information narrowing suggests the potential for introducing a user friction mechanism to regulate algorithmic curation and mitigate the filter bubble effect, particularly within algorithmic news Apps. This psychological cue may prompt users to seek a broader range of information, acting as a self-regulatory prompt. To support this process, platforms could offer personalized settings that provide feedback on content diversity or suggestions for broadening information exposure. Within algorithmic news applications, such features could allow users to adjust the flow of personalized content, actively counteracting filter bubble effects and increasing news diversity.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eEthical approval\u003c/p\u003e\n\u003cp\u003eApproval for the online survey procedures of this study was obtained from the academic committee (act as ethics committee) of School of Humanities, Shanghai University of Finance and Economics on 1\u003csup\u003est\u003c/sup\u003e, September, 2019 (Approval number: 2019-9-1). All procedures involving human participants in the research were performed in accordance with the ethical standards of the institutional research committee and with the 1964 Declaration of Helsinki and its later amendments or comparable standards.\u003c/p\u003e\n\u003cp\u003eInformed consent\u003c/p\u003e\n\u003cp\u003eInformed consent was secured from participants upon the completion of the online survey in\u0026nbsp;October 2019. The initial page of the online survey provided participants with a succinct written overview of the survey\u0026rsquo;s objectives and the organizing entity. Additionally, it included a confidentiality statement assuring that their responses would be kept confidential and solely utilized for academic research purposes.\u003c/p\u003e\n\u003cp\u003eConsent for publication\u003cbr\u003e\u0026nbsp;The authors consent to the publication of this manuscript and affirm that it represents honest and original work.\u003c/p\u003e\n\u003cp\u003eAvailability of data and materials\u003cbr\u003e\u0026nbsp;The data underlying this article will be shared on reasonable request to the corresponding author.\u003c/p\u003e\n\u003cp\u003eCompeting interests\u003cbr\u003e\u0026nbsp;The authors declare that there is no conflict of interest regarding the publication of this manuscript.\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eThis research is funded by National Social Science Fund of China (Grant Number: 22CWX017).\u003c/p\u003e\n\u003cp\u003eAcknowledgement\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAbdollahpouri H, Burke R, Mobasher B (2021) User-centered evaluation of popularity bias in recommender systems. In \u003cem\u003eProceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization\u003c/em\u003e (pp. 119\u0026ndash;129). ACM. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1145/3450613.3456831\u003c/span\u003e\u003cspan address=\"10.1145/3450613.3456831\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAldamen Y (2023) Understanding social media dependency, and uses and gratifications as a communication system in the migration era: Syrian refugees in host countries as a case study. Social Sci 12(6):322. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/socsci12060322\u003c/span\u003e\u003cspan address=\"10.3390/socsci12060322\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAndersen K, Bjarnoe C, Albak E, Moller S, Jensen L, Petersen T (2016) How news type matters: Indirect effects of media use on political participation through knowledge and efficacy. J Media Psychol 28(3):111\u0026ndash;122. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1027/1864-1105/a000176\u003c/span\u003e\u003cspan address=\"10.1027/1864-1105/a000176\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBall-Rokeach SJ, DeFleur ML (1976) A dependency model of mass-media effects. Communication Res 3(1):3\u0026ndash;21. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1177/009365027600300101\u003c/span\u003e\u003cspan address=\"10.1177/009365027600300101\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBall-Rokeach SJ, Rokeach M, Grube JW (1984) The great American values test: Influencing behavior and belief through television. Free, New York, NY\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBarber\u0026aacute; P, Jost JT, Nagler J, Tucker JA, Bonneau R (2015) Tweeting from left to right: Is online political communication more than an echo chamber? Psychol Sci 26(10):1531\u0026ndash;1542. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1177/0956797615594620\u003c/span\u003e\u003cspan address=\"10.1177/0956797615594620\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBaym NK, Wagman KB, Persaud CJ (2020) Mindfully scrolling: Rethinking Facebook after time deactivated. Social Media + Soc 6(2):2056305120919105. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1177/2056305120919105\u003c/span\u003e\u003cspan address=\"10.1177/2056305120919105\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBeam MA (2014) Automating the news: How personalized news recommender system design choices impact news reception. Communication Res 41(8):1019\u0026ndash;1041. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1177/0093650213497979\u003c/span\u003e\u003cspan address=\"10.1177/0093650213497979\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBright J (2016) The social news gap: How news reading and news sharing diverge. J Communication 66(3):343\u0026ndash;365. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/jcom.12232\u003c/span\u003e\u003cspan address=\"10.1111/jcom.12232\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBurgess J, Baym N (2020) Twitter: A biography. NYU, New York, NY\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCacciatore MA, Yeo SK, Scheufele DA, Xenos MA, Brossard D, Corley EA (2018) Is Facebook making us dumber? Exploring social media use as a predictor of political knowledge. Journalism Mass Communication Q 95(2):404\u0026ndash;424. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1177/1077699018770447\u003c/span\u003e\u003cspan address=\"10.1177/1077699018770447\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCarpini MXD, Keeter S (1996) What Americans know about politics and why it matters. Yale University Press, New Haven, CT\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChaffee SH, Kanihan SF (1997) Learning about politics from the mass media. Political Communication 14(4):421\u0026ndash;430. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/105846097199218\u003c/span\u003e\u003cspan address=\"10.1080/105846097199218\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCouldry N, Mejias UA (2020) The costs of connection: How data are colonizing human life and appropriating it for capitalism. Stanford University Press\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDimitrova DV, Shehata A, Str\u0026ouml;mb\u0026auml;ck J, Nord LW (2014) The effects of digital media on political knowledge and participation in election campaigns: Evidence from panel data. Communication Res 41(1):95\u0026ndash;118. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1177/0093650211426004\u003c/span\u003e\u003cspan address=\"10.1177/0093650211426004\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eEg R, T\u0026oslash;nnesen \u0026Ouml;D, Tennfjord MK (2023) A scoping review of personalized user experiences on social media: The interplay between algorithms and human factors. Computers Hum Behav Rep 9:100253. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.chbr.2022.100253\u003c/span\u003e\u003cspan address=\"10.1016/j.chbr.2022.100253\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eEslami M, Rickman A, Vaccaro K, Aleyasen A, Vuong A, Karahalios K, Hamilton K, Sandvig C (2015) April 18\u0026ndash;23). I always assumed that I wasn\u0026rsquo;t really that close to [her]: Reasoning about invisible algorithms in news feeds. In: \u003cem\u003eProceedings of the 33rd annual ACM conference on human factors in computing systems\u003c/em\u003e (pp. 153\u0026ndash;162), Seoul, Republic of Korea. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1145/2702123.2702556\u003c/span\u003e\u003cspan address=\"10.1145/2702123.2702556\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eEveland WP Jr. (2001) The cognitive mediation model of learning from the news: Evidence from non-election, off-year election, and presidential election contexts. Communication Res 28(5):571\u0026ndash;601. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1177/009365001028005001\u003c/span\u003e\u003cspan address=\"10.1177/009365001028005001\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFry DL (1979) The knowledge gap hypothesis and media dependence: An initial study. \u003cem\u003ePaper presented at the annual meeting of the Association for Education in Journalism and Mass Communication, Houston, TX.\u003c/em\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGal MS (2017) Algorithmic challenges to autonomous choice. Mich Telecommunications Technol Law Rev 25:59\u0026ndash;104. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.36645/mtlr.25.1.algorithmic\u003c/span\u003e\u003cspan address=\"10.36645/mtlr.25.1.algorithmic\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGaziano C (1990) Media dependence for news: Some neglected groups. Mass Communication Rev 17(2):225\u0026ndash;253\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGei\u0026szlig; S, Magin M, J\u0026uuml;rgens P, Stark B, Emmer M (2021) Loopholes in the echo chambers: How the echo chamber metaphor oversimplifies the effects of information gateways on opinion expression. Digit Journalism 9(5):660\u0026ndash;686. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/21670811.2021.1872976\u003c/span\u003e\u003cspan address=\"10.1080/21670811.2021.1872976\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGuess AM et al (2023) How do social media feed algorithms affect attitudes and behavior in an election campaign? Science 381(6656):398\u0026ndash;404. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1126/science.abc123\u003c/span\u003e\u003cspan address=\"10.1126/science.abc123\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHayes AF (2018) Introduction to mediation, moderation, and conditional process analysis: A regression-based approach, 2nd edn. The Guilford Press, New York, NY\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHelm P, Matzner T (2023) Co-addictive human\u0026ndash;machine configurations: Relating critical design and algorithm studies to medical-psychiatric research on problematic Internet use. New Media Soc. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1177/14614448231165916\u003c/span\u003e\u003cspan address=\"10.1177/14614448231165916\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHuang S, Lai X, Ke L, Li Y, Wang H, Zhao X, Wang Y (2024) AI Technology panic\u0026mdash;is AI dependence bad for mental health? A cross-lagged panel model and the mediating roles of motivations for AI use among adolescents. Psychol Res Behav Manage, 1087\u0026ndash;1102\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJohn NA, Nissenbaum A (2019) An agnotological analysis of APIs: Or, disconnectivity and the ideological limits of our knowledge of social media. Inform Soc 35(1):1\u0026ndash;12. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/01972243.2018.1542647\u003c/span\u003e\u003cspan address=\"10.1080/01972243.2018.1542647\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKarppi T (2018) Disconnect: Facebook\u0026rsquo;s affective bonds. University of Minnesota Press, Minneapolis, MN\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKaufhold K, Valenzuela S, de Zuniga HG (2010) Citizen journalism and democracy: How user-generated news use relates to political knowledge and participation. Journalism Mass Communication Q 87(3\u0026ndash;4):515\u0026ndash;529. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1177/107769901008700304\u003c/span\u003e\u003cspan address=\"10.1177/107769901008700304\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKim Y, Chen HT, de Zuniga HG (2013) Stumbling upon news on the Internet: Effects of incidental news exposure and relative entertainment use on political engagement. Comput Hum Behav 29(6):2607\u0026ndash;2614. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.chb.2013.06.005\u003c/span\u003e\u003cspan address=\"10.1016/j.chb.2013.06.005\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKim YC, Jung JY (2017) SNS dependency and interpersonal storytelling: An extension of media system dependency theory. New Media Soc 19(9):1458\u0026ndash;1475. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1177/1461444816636611\u003c/span\u003e\u003cspan address=\"10.1177/1461444816636611\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKlug K, Strang C (2019) The filter bubble in social media communication: How users evaluate personalized information in the Facebook newsfeed. In \u003cem\u003eMedia Trust in a Digital World: Communication at Crossroads\u003c/em\u003e (pp. 159\u0026ndash;180). Springer. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/978-3-319-70445-6_10\u003c/span\u003e\u003cspan address=\"10.1007/978-3-319-70445-6_10\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLee EJ, Kim YW (2016) Effects of infographics on news elaboration, acquisition, and evaluation: Prior knowledge and issue involvement as moderators. New Media Soc 18(8):1579\u0026ndash;1598. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1177/1461444814567982\u003c/span\u003e\u003cspan address=\"10.1177/1461444814567982\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLee F, Chan M, Chen H, Li S (2019) Consumptive news feed curation on social media as proactive personalization: A study of six East Asian markets. Journalism Stud 20(15):2277\u0026ndash;2292. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/1461670X.2019.1614851\u003c/span\u003e\u003cspan address=\"10.1080/1461670X.2019.1614851\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLee JK, Kim E (2017) Incidental exposure to news: Predictors in the social media setting and effects on information gain online. Comput Hum Behav 75:1008\u0026ndash;1015. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.chb.2017.02.018\u003c/span\u003e\u003cspan address=\"10.1016/j.chb.2017.02.018\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLoges WE, Ball-Rokeach SJ (1993) Dependency relations and newspaper readership. Journalism Q 70(3):602\u0026ndash;614. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1177/107769909307000309\u003c/span\u003e\u003cspan address=\"10.1177/107769909307000309\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLowrey W (2004) Media dependency during a large-scale social disruption: The case of September 11. Mass Communication Soc 7(3):339\u0026ndash;357. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1207/s15327825mcs0703_5\u003c/span\u003e\u003cspan address=\"10.1207/s15327825mcs0703_5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMatthes J, Nanz A, Stubenvoll M, Heiss R (2020) Processing news on social media: The political incidental news exposure model (PINE). Journalism 21(8):1031\u0026ndash;1048. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1177/1464884920919090\u003c/span\u003e\u003cspan address=\"10.1177/1464884920919090\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMattis N, Masur P, M\u0026ouml;ller J, van Atteveldt W (2024) Nudging towards news diversity: A theoretical framework for facilitating diverse news consumption through recommender design. New Media Soc 26(7):3681\u0026ndash;3706. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1177/14614448231130542\u003c/span\u003e\u003cspan address=\"10.1177/14614448231130542\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMatzner T (2022) Algorithms as complementary abstractions. New Media Soc. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1177/14614448221078604\u003c/span\u003e\u003cspan address=\"10.1177/14614448221078604\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMerten L (2021) Block, hide or follow\u0026mdash;personal news curation practices on social media. Digit Journalism 9(8):1018\u0026ndash;1039. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/21670811.2021.1907207\u003c/span\u003e\u003cspan address=\"10.1080/21670811.2021.1907207\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMetcalfe J, Schwartz BL, Joaquim SG (1993) The cue-familiarity heuristic in metacognition. J Exp Psychol 19(4):851\u0026ndash;861. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1037/0278-7393.19.4.851\u003c/span\u003e\u003cspan address=\"10.1037/0278-7393.19.4.851\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMoy P, Torres M, Tanaka K, McCluskey M (2005) Knowledge or trust? Investigating linkages between media reliance and participation. Communication Res 32(1):59\u0026ndash;86. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1177/0093650204271399\u003c/span\u003e\u003cspan address=\"10.1177/0093650204271399\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNatarajan N (2024) Do they stop? How do they stop? Why do they stop? Whether, how, and why teens insert frictions into social media\u0026rsquo;s infinite scroll. Int J Communication 18:20. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://ijoc.org/index.php/ijoc/article/view/21618\u003c/span\u003e\u003cspan address=\"https://ijoc.org/index.php/ijoc/article/view/21618\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNg YL (2022) Exploring the association between use of conversational artificial intelligence and social capital: survey evidence from Hong Kong. New Media Soc 1(2):1\u0026ndash;16\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOeldorf-Hirsch A, Srinivasan P (2022) An unavoidable convenience: How post-millennials engage with the news that finds them on social and mobile media. Journalism 23(9):1939\u0026ndash;1954. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1177/1464884920909086\u003c/span\u003e\u003cspan address=\"10.1177/1464884920909086\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOrosz G, T\u0026oacute;th-Kir\u0026aacute;ly I, Bőthe B (2016) Four facets of Facebook intensity\u0026mdash;the development of the multidimensional Facebook intensity scale. Pers Indiv Differ 100:95\u0026ndash;104. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.paid.2015.11.038\u003c/span\u003e\u003cspan address=\"10.1016/j.paid.2015.11.038\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePariser E (2011) The filter bubble: How the new personalized web is changing what we read and how we think. Penguin\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePark CS, Kaye BK (2019) Mediating roles of news curation and news elaboration in the relationship between social media use for news and political knowledge. J Broadcast Electron Media 63(3):455\u0026ndash;473. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/08838151.2019.1653107\u003c/span\u003e\u003cspan address=\"10.1080/08838151.2019.1653107\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePeople\u0026rsquo;s Daily (2018), May 2 The Scream Effect and the Information Cocoon. \u003cem\u003ePeople\u0026rsquo;s Daily Online.\u003c/em\u003e Retrieved August 23, 2024, from \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://opinion.people.com.cn/n1/2018/0502/c1003-29959124.html\u003c/span\u003e\u003cspan address=\"http://opinion.people.com.cn/n1/2018/0502/c1003-29959124.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRauch J (2018) Slow media: Why slow is satisfying, sustainable, and smart. Oxford University Press, Oxford, UK\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eReviglio U, Agosti C (2020) Thinking outside the black-box: The case for algorithmic sovereignty in social media. Social Media + Soc 6(2):1\u0026ndash;12. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1177/2056305120926630\u003c/span\u003e\u003cspan address=\"10.1177/2056305120926630\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRixen JO et al (2023) The loop and reasons to break it: Investigating infinite scrolling behavior in social media applications and reasons to stop. \u003cem\u003eProceedings of the ACM on Human-Computer Interaction, 7\u003c/em\u003e(MHCI), 1\u0026ndash;22. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1145/35012345\u003c/span\u003e\u003cspan address=\"10.1145/35012345\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSch\u0026auml;fer S, S\u0026uuml;lflow M, M\u0026uuml;ller P (2017) The special taste of snack news: An application of niche theory to understand the appeal of Facebook as a news source. \u003cem\u003eFirst Monday\u003c/em\u003e. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://dx.doi.org/10.5210/fm.v22i4.7431\u003c/span\u003e\u003cspan address=\"10.5210/fm.v22i4.7431\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSch\u0026auml;fer S (2020) Illusion of knowledge through Facebook news? Effects of snack news in a news feed on perceived knowledge, attitude strength, and willingness for discussions. Comput Hum Behav 103:1\u0026ndash;12. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.chb.2019.08.019\u003c/span\u003e\u003cspan address=\"10.1016/j.chb.2019.08.019\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eShehata A, Str\u0026ouml;mb\u0026auml;ck J (2018) Learning political news from social media: Network media logic and current affairs news learning in a high-choice media environment. Communication Res 48(1):1\u0026ndash;23. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1177/0093650217749354\u003c/span\u003e\u003cspan address=\"10.1177/0093650217749354\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eShin D (2021) The effects of explainability and causability on perception, trust, and acceptance: Implications for explainable AI. Int J Hum Comput Stud 146:102551. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.ijhcs.2020.102551\u003c/span\u003e\u003cspan address=\"10.1016/j.ijhcs.2020.102551\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eStroud NJ (2011) Niche news: The politics of news choice. Oxford University Press, New York, NY\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSu C, Zhou H, Gong L, Teng B, Geng F, Hu Y (2021) Viewing personalized video clips recommended by TikTok activates default mode network and ventral tegmental area. NeuroImage 237:118136. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.neuroimage.2021.118136\u003c/span\u003e\u003cspan address=\"10.1016/j.neuroimage.2021.118136\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSu Y, Luo C, Borah P (2024) Learning about climate change with algorithmic news? A two-wave panel study examining the role of news-finds-me perception. J Computer-Mediated Communication 29(5). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/jcmc/zmad045\u003c/span\u003e\u003cspan address=\"10.1093/jcmc/zmad045\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSunstein CR (2006) Infotopia: How many minds produce knowledge. Oxford University Press, New York, NY\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSunstein CR (2007) Republic.com 2.0. Princeton University Press, Princeton, NJ\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSunstein CR (2002) The law of group polarization. J Political Philos 10(2):175\u0026ndash;195. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/1467-9760.00148\u003c/span\u003e\u003cspan address=\"10.1111/1467-9760.00148\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSwart J (2021) Experiencing algorithms: How young people understand, feel about, and engage with algorithmic news selection on social media. Social Media + Soc 7(2):1\u0026ndash;11. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1177/20563051211008828\u003c/span\u003e\u003cspan address=\"10.1177/20563051211008828\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eThorson K, Wells C (2016) Curated flows: A framework for mapping media exposure in the digital age. Communication Theory 26(3):309\u0026ndash;328. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/comt.12087\u003c/span\u003e\u003cspan address=\"10.1111/comt.12087\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eVan Aelst P, Stromback J, Aalberg T, Esser F, de Vreese C, Matthes J, Reinemann C (2017) Political communication in a high-choice media environment: A challenge for democracy? Annals Int Communication Association 41(1):3\u0026ndash;27. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/23808985.2017.1288551\u003c/span\u003e\u003cspan address=\"10.1080/23808985.2017.1288551\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eW\u0026ouml;lker A, Powell TE (2021) Algorithms in the newsroom? News readers\u0026rsquo; perceived credibility and selection of automated journalism. Journalism 22(1):86\u0026ndash;103. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1177/1464884918757072\u003c/span\u003e\u003cspan address=\"10.1177/1464884918757072\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eXie T, Pentina I, Hancock T (2023) Friend, mentor, lover: does chatbot engagement lead to psychological dependence? J Service Manage 34(4):806\u0026ndash;828. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1108/JOSM-02-2022-007232\u003c/span\u003e\u003cspan address=\"10.1108/JOSM-02-2022-007232\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZarouali B, Boerman SC, de Vreese CH (2021) Is this recommended by an algorithm? The development and validation of the algorithmic media content awareness scale (AMCA-scale). Telematics Inform 62:101607. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.tele.2021.101607\u003c/span\u003e\u003cspan address=\"10.1016/j.tele.2021.101607\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhang X, Yin M, Zhang M, Li Z, Li H (2025) The Development and Validation of an Artificial Intelligence Chatbot Dependence Scale. Cyberpsychology Behav Social Netw 28(2):126\u0026ndash;131\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZuiderveen Borgesius F, Trilling D, M\u0026ouml;ller J, Bod\u0026oacute; B, de Vreese CH, Helberger N (2016) Should we worry about filter bubbles? Internet Policy Rev J Internet Regul 5(1). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.14763/2016.1.401\u003c/span\u003e\u003cspan address=\"10.14763/2016.1.401\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"algorithm recommendation, user friction, infinite scroll, algorithm dependence, news knowledge, information narrowing","lastPublishedDoi":"10.21203/rs.3.rs-6917973/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6917973/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eThe rapid expansion of mobile applications featuring personalized recommendation algorithms and infinite scrolling news feeds has raised concerns about their role in shaping societal knowledge acquisition. Grounded in the frameworks of algorithm dependence, this study investigates how different types of algorithmic Apps (news, social media, and short video) impact users\u0026rsquo; news knowledge. Specifically, we examine the mediating effect of algorithm dependence on the relationship between App usage and news knowledge acquisition, introducing user friction as a mechanism, with perceived information narrowing as a moderating factor.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eData was collected via online survey with 354 responded participants.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eResults revealed that short video Apps decrease users\u0026rsquo; news knowledge, social media Apps indirectly reduce news knowledge through algorithm dependence, and news Apps diminish news knowledge only among users perceiving high levels of information narrowing.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eThese findings suggest the potential for introducing user friction to regulate algorithmic curation and mitigate its negative impact on knowledge gain, especially within algorithmic news Apps. This study contributes to understanding the complex interplay between algorithmic dependence and knowledge gain, highlighting user-centered approaches to enhancing informational diversity in algorithm-driven media.\u003c/p\u003e","manuscriptTitle":"User Friction in Infinite Scrolling Algorithmic Feeds: Examining the Impact of App Use and Algorithm Dependence on News Knowledge","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-08 10:29:13","doi":"10.21203/rs.3.rs-6917973/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"68fb3616-53f5-4867-9794-428d321af3d7","owner":[],"postedDate":"December 8th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":59097104,"name":"Social science/Cultural and media studies"},{"id":59097105,"name":"Social science/Psychology"}],"tags":[],"updatedAt":"2026-01-06T16:09:31+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-08 10:29:13","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6917973","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6917973","identity":"rs-6917973","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}