From Crisis to Change: Analyzing the Lasting Influence of COVID-19 on Airbnb Users through Structural Topic Modeling

From Crisis to Change: Analyzing the Lasting Influence of COVID-19 on Airbnb Users through Structural Topic Modeling | 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 From Crisis to Change: Analyzing the Lasting Influence of COVID-19 on Airbnb Users through Structural Topic Modeling Kai Ding, Le Li, Rongteng (Renata) Zhang, Yuhua Chen This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5660543/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 08 Jun, 2025 Read the published version in Humanities and Social Sciences Communications → Version 1 posted 10 You are reading this latest preprint version Abstract A key challenge for the peer-to-peer (P2P) accommodation industry is keeping pace with the evolving expectations and behavior of guests over time, shaped by diverse experiences and shifting preferences. This study utilizes advanced text analytics to examine the lasting impact of COVID-19 on Airbnb users’ priorities regarding accommodation attributes, both during the pandemic and in the year that followed. Employing a longitudinal research design, we analyze a dataset of 461,509 reviews from 18,465 listed properties across four major cities in different countries (i.e., the United Kingdom, the United States, Spain) known for their Airbnb presence. Our findings highlight that the most significant and enduring impact of the pandemic on guest behavior is an increased prioritization of health-related features. Although certain attributes that were previously valued remain relevant, there has been a marked transition in user perceptions; specifically, hedonic and aesthetic values have diminished in importance relative to health-centric considerations. Furthermore, the policies and practices adopted during the pandemic reveal additional dimensions of its lasting influence, shaping guest expectations and preferences. Noteworthy among these are enhanced booking and cancellation flexibility, as well as the implementation of contactless services and the provision of protective equipment. This research contributes to understanding how crises can reshape guest priorities within the context of sharing economy accommodations, offering valuable insights for both academic researchers and practitioners. Business and commerce/Business and management Business and commerce/Information systems and information technology Social science/Business and management Text analytics Longitudinal research Airbnb reviews Structural topic modeling Guest priority COVID-19 Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1. Introduction The COVID-19 pandemic has still left an indelible mark on the global travel and accommodation sectors, forcing profound changes in guest behavior and service expectations (Srivastava & Kumar, 2021). As a key player in the sharing economy, Airbnb has been particularly affected, with significant reductions in travel activity and a shift in guest priorities. The global pandemic, which primarily spreads through respiratory droplets, necessitated the implementation of physical distancing, heightened hygiene practices, and reduced in-person interactions (World Health Organization, 2020). Due to the close interactions between guests and hosts in peer-to-peer (P2P) accommodations, and the importance of social engagement as a key factor in choosing P2P accommodations over traditional hotels (e.g., Filieri et al., 2023; Cheng and Jin, 2019), platforms like Airbnb were particularly hard-hit by the pandemic (Abril, 2020). A global leader in P2P accommodation sharing, Airbnb faced significant disruption during the pandemic, which reshaped the market and guest behavior. Despite an initial revenue decline in 2020, these shifts redefined the accommodation landscape in the post-pandemic era, compelling providers to adjust their offerings to meet new guest expectations (Nuttah et al., 2024). Existing studies primarily focus on the immediate effects of COVID-19 on Airbnb or examine its impact during the pandemic (Gyódi, 2022; Jang & Kim, 2022; Buzzacchi et al., 2023). Academically, while existing studies provide valuable insights into the short-term disruptions caused by COVID-19, they offer limited understanding of the behavioral shifts that unfolded throughout the pandemic and beyond, particularly as global conditions and travel restrictions evolved. This gap leaves open questions about the durability of such changes, including whether adaptations in guest preferences, such as safety concerns, persisted after the World Health Organization (WHO) declared the pandemic’s end. Practically, understanding these long-term trends is crucial for Airbnb and other accommodation providers to refine their business strategies. As the industry recovers and guest behavior stabilizes, insights into lasting changes can help platforms better anticipate and meet the needs of travelers in a post-pandemic world, ensuring their competitiveness in a transformed marketplace. As travel restrictions eased and the industry began to recover, however, there remained considerable uncertainty surrounding the long-term shifts in guest behavior (Braje et al., 2022; Buzzacchi et al., 2023). Therefore, there is a significant gap in understanding how these behaviors have evolved throughout the entirety of the pandemic and whether they have persisted in the post-pandemic era. In this study, we address the gap in understanding the long-term shifts in guest behavior in P2P accommodation. By utilizing Structural Topic Modeling (STM), we analyze a large dataset of Airbnb reviews to uncover emerging themes and patterns that reflect the long-term effects of the pandemic on guest expectations and preferences. In the following sections, we first review the literature on the impact of COVID-19 on P2P accommodation and STM. We then describe our research methodology, including the use of text analytics to analyze guest reviews. Our findings are presented in detail, followed by a discussion of their implications for both academic researchers and practitioners. Finally, we conclude by highlighting the contributions of this study to the ongoing discourse on the transformation of P2P accommodations in a post-pandemic world. 2. Literature review 2.2 Theoretical foundation In examining the changes in consumer behavior brought about by the COVID-19 pandemic, various theories offer valuable perspectives to understand the driving forces behind these shifts. One such perspective is provided by the Health Belief Model, which suggests that consumers’ choices in hospitality and tourism are heavily influenced by their perceptions of health risks and the benefits they associate with safer alternatives (Mirakzadeh et al., 2021; Naseer et al., 2022). Building on this, the Theory of Planned Behavior further elaborates on how attitudes towards safety, subjective norms, and perceived behavioral control collectively shape the intention to choose specific accommodations (Huang et al., 2020; Tajeddini et al., 2021). Moreover, the Technology Acceptance Model offers insights into the adoption of digital platforms, such as Airbnb, emphasizing how perceived usefulness—particularly in terms of contactless stays and flexible policies—drives consumer preferences (Jung et al., 2021). In this study, we mainly draw on Social Exchange Theory (SET), which suggests that individuals engage in interactions based on a cost-benefit analysis and the expectation of reciprocal benefits, and has been widely applied in the hospitality sector (Khan & Hefny, 2019; Priporas et al., 2017; Wang et al., 2022). Applied to the P2P accommodation context, SET helps explain how guests re-evaluated their choices during and after the pandemic, weighing factors such as safety, flexibility, and perceived risk against the benefits of travel and accommodation experiences. As the pandemic heightened concerns over health and safety, guests likely made decisions based on the perceived “cost” of potential exposure to COVID-19, altering their behavior towards preferences for accommodations that prioritized hygiene and safety protocols. SET can offer robust frameworks for analyzing the pandemic-induced changes in consumer behavior within the P2P accommodation sector, providing both explanatory power and theoretical insights into these long-term shifts. 2.2 Impact of COVID-19 on P2P Accommodations The COVID-19 pandemic brought unprecedented disruptions to the travel and hospitality sectors, deeply impacting P2P accommodations like Airbnb. This section explores how the pandemic has influenced the preferences of Airbnb users, drawing insights from various studies that examine the evolving dynamics of P2P accommodation during and after the pandemic. The pandemic’s impact on the accommodation-sharing sector revealed a paradox in which the sector’s strengths, such as flexibility and personal interaction, became vulnerabilities amidst the crisis (Gerwe, 2021). Studies have highlighted how hosts’ responses to the pandemic varied significantly. Farmaki et al. (2020) interviewed hosts and found that reactions ranged from temporarily halting operations to adopting safety protocols and emphasizing remote hosting options. In the Chinese context, Zhang et al. (2021) found that the pandemic acted as an accelerator for some hosts to embrace the original ethos of the sharing economy, emphasizing personal, experiential hosting rather than a purely commercial focus. Airbnb users’ preferences have experienced significant changes due to the COVID-19 pandemic. One of the shifts has been the increased preference for entire flat rentals over shared accommodations and traditional hotels. Bresciani et al. (2021) found that, in response to physical distancing concerns, guests showed a clear preference for renting entire flats, driven by the desire for greater privacy and control over their accommodation environment. Nicolau et al. (2023) also found that travelers prefer Airbnb entire flats/apartments during periods of rising pandemic risk, validating a preference for Airbnb in high-risk scenarios. This preference for privacy is closely linked to changes in locational choices. Turk and Sap (2021) noted that while the physical attributes of listings remained largely consistent with pre-COVID trends, there were significant shifts in spatial preferences. These shifts can be attributed to the pandemic’s impact on travel restrictions and heightened safety concerns, prompting travelers to seek out accommodations in less crowded or more isolated locations. Trust also emerged as a crucial factor influencing traveler behavior in the post-pandemic period. Braje et al. (2022) observed that trust in both the platforms and the hosts played a more significant role in determining repurchase intentions among short-term rental users. This finding underscores the increasing importance of safety and reliability in accommodation choices during uncertain times. Hygiene and cleanliness, in particular, have become paramount concerns for travelers. Godovykh et al. (2023) highlighted that transparency around cleanliness measures significantly boosted guest trust and positively influenced their behavioral intentions. Similarly, Kim et al. (2022) found a substantial shift in consumer preferences, with cleanliness overtaking location as the primary consideration during the pandemic. More recent studies continue to deepen our understanding of these shifts. Chen et al. (2023) explored continuous sharing behavior (CSB) among providers and found that positive feedback significantly influenced providers’ willingness to continue sharing, with intrinsic motivation playing a key mediating role. Meanwhile, FB Teixeira (2023) systematically investigated the reasons why guests opt for or reject P2P hospitality, highlighting utilitarian factors such as feeling welcomed by hosts and comfort in the neighborhood as primary motivators, while also noting safety concerns as an important consideration. These insights suggest that while traditional motivators for P2P accommodation remain relevant, the pandemic has introduced new priorities, particularly around safety and trust. In the post-pandemic context, Sahadev et al. (2023) revealed that user-generated content, specifically ratings and sentiments, had significant positive effects on occupancy rates, demonstrating the continued importance of guest feedback in driving demand. Furthermore, Wu et al. (2023) examined host-guest interactions, emphasizing that such interactions, particularly for sociable guests, played a critical role in fostering commercial friendships and co-creating value. This research highlights the evolving nature of host-guest dynamics, suggesting that while safety and trust are paramount, the interpersonal elements of the P2P accommodation experience still hold significant value for certain segments of the market. Despite significant advances in understanding the key attributes valued by Airbnb users, a crucial gap remains in the literature regarding the long-term effects of the COVID-19 pandemic on consumer preferences. While numerous studies have focused on guest behavior either during or immediately after the pandemic (Braje et al., 2022; Turk and Sap, 2021), there is a need for longitudinal studies to track the long-term recovery and adaptation of the Airbnb market, as the immediate post-pandemic period may not fully reflect enduring changes. Existing studies often fail to provide a comprehensive perspective that tracks how guest preferences have changed from pre-pandemic conditions, through various stages of the pandemic, and into the post-pandemic era following the WHO declaration of the pandemic’s end. It remains unclear whether the observed shifts in guest behavior represent long-term, structural changes or merely short-term adaptations in response to the crisis. The inherently dynamic nature of the P2P accommodation sector further justify the importance of this research gap. 2.3 Structural topic model To explore the evolving preferences of Airbnb users amidst the progression of the pandemic, we employed structural topic modeling (STM) to analyze Airbnb reviews. STM, a contemporary addition to the suite of topic modeling algorithms developed in recent decades, examines the observed words in a text corpus to uncover latent topics or themes. It uses the Bayesian generative topic model which assumes each topic as a distribution over words, and each document as a mixture of topics (Blei, 2012; Roberts et al., 2014). This research employed STM for two main reasons. Firstly, STM is a mixed membership model, allowing documents to cover multiple topics—an ideal characteristic for analyzing accommodation reviews, which often contain various preference traits. Secondly, STM enables the incorporation of document-level covariates in the analysis. Given the study’s focus on identifying shifts in Airbnb users’ preferences during the pandemic, factors such as the date of the reviewer’s stay were crucial to include. Additionally, review extremity, distinguishing between positive and negative reviews, could also be considered as a covariate within STM (Roberts et al., 2016). Figure 1 shows the framework of STM for analyzing textual data, allowing for the integration of covariates that influence both the prevalence and content of topics. In the context of this study, we leverage STM to analyze Airbnb reviews and track changing user preferences during and after the COVID-19 pandemic, incorporating review date and sentence polarity as covariates. The model begins by using these covariates to influence the document-topic proportions ( θ ), which dictate the distribution of topics within each review. Similarly, sentence polarity (positive or negative sentiment) affects how users express their preferences, with more negative sentiment possibly amplifying discussions around negative experiences during the pandemic. Per-word topic assignments ( z ) assign each word in a review to a specific topic, thereby identifying how individual words relate to broader themes. Topic word distributions ( β ) specify the probability of words occurring in each topic. These distributions are influenced by content covariates ( Y ), allowing the language used in each topic to evolve based on the covariates. 3. Methodology 3.1 Data collection In this study, we collected data from InsideAirbnb.com, a platform that openly shares Airbnb data scraped from the official Airbnb site. We focused on textual reviews collected between May 2020 and May 2024 from four major cities: New York, London, Los Angeles, and Barcelona, which are popular tourist destinations that experienced pronounced impacts from COVID-19, making them particularly relevant for our analysis. This timeframe captures critical stages of the pandemic, from the initial outbreak through various responses, including lockdowns and the gradual reopening of economies. It allows for longitudinal analysis of guest sentiment, helping to identify trends in guest preferences and behaviors during the crisis and recovery phases. In addition, these cities were selected to enhance the generalizability of our findings, as they are among the top destinations for Airbnb listings worldwide and boast a significant number of English-language reviews. Figure 2 provides the methodological framework of this research. 3.2 Data pre-processing For the topic modeling analysis of Airbnb online reviews, we followed a structured text pre-processing procedure in line with previous research (Ding et al., 2024; Xu, 2020). (1) We began by filtering the data to include only English-language reviews using the “textcat” package from R programming. (2) Next, the text was cleaned by converting all content to lowercase, removing punctuation and non-alphabetic characters, and eliminating common stopwords (e.g., is, a, and the). (3) Subsequently, we applied tokenization to break down the text into individual words, followed by lemmatization to reduce words to their root forms. Lemmatization converts words like ‘running’ or ‘ran’ to their base form ‘run,’ which allows for a more accurate interpretation of the underlying content. (4) Since topic modeling tends to perform poorly with a short text, reviews containing fewer than six words were excluded. Additionally, uncommon words that appeared in less than 2% of the initial corpus were removed (Korfiatis et al., 2019). The final dataset comprises 461,509 reviews across 18,465 listed properties. Table 1 provides a summary of the review statistics, categorized by city. Table 1 Summary statistics of review sample by cities City Year Frequency Percentage New York 2020 18,345 3.97% 2021 28,245 6.12% 2022 25,432 5.51% 2023 40,789 8.83% 2024 24,754 5.37% London 2020 12,874 2.79% 2021 32,039 6.94% 2022 21,901 4.75% 2023 30,432 6.59% 2024 17,584 3.81% Los Angeles 2020 13,123 2.84% 2021 25,678 5.56% 2022 32,345 6.99% 2023 30,672 6.64% 2024 19,021 4.12% Barcelona 2020 9,032 1.96% 2021 18,456 4.00% 2022 22,567 4.89% 2023 26,875 5.83% 2024 11,345 2.46% Total 461,509 100% 3.3 Covariate set up To gain an understanding of Airbnb user sentiment and how it evolves over time, we incorporate the polarity of each sentence as well as the review date into the model to analyze their influence on topic prevalence. To capture the sentiment of user reviews, we use the “Sentimentr” package in R, a tool that employs Bayesian classifiers to categorize each sentence based on its polarity. 3.4 Determining the topic number The most important task in applying topic modeling is determining the appropriate number of topics. Although there is no universally correct number, statistical metrics can guide the selection process. In this study, we approach topic selection in two phases. The first phase involves using four criteria—Held-Out likelihood, residuals, semantic coherence, and lower bound—to narrow down the candidate number of topics. We specifically selected statistical metrics that are sensitive to changes in the number of topics. In the second phase, we assess the performance of individual topics across different topic solutions using semantic coherence and exclusivity. Semantic coherence is an indicator of topic quality, proposed by Mimno et al. (2011). When the most probable words in each topic frequently co-occur, the semantic coherence of that topic is maximized. Let D ( v , v′ ) represent the co-occurrence frequency of words v and v′ in a specific document. For the M high-frequency words in topic k , the semantic coherence of topic k can be expressed as: $$\:{C}_{k}=\sum\:_{i=2}^{M}\:\sum\:_{j=1}^{i-1}\:\text{l}\text{n}\left(\frac{D\left({v}_{i},{v}_{j}\right)+1}{D\left({v}_{j}\right)}\right)$$ 1 However, with a smaller number of topics, the topics generated are often dominated by common words, leading to an overestimation of the semantic coherence function. Therefore, it is necessary to introduce the topic exclusivity indicator, FREX, which balances word frequency by calculating the weighted harmonic mean of word frequency and exclusivity, thereby improving the model’s quality. The FREX calculation formula is: $$\:{\text{F}\text{R}\text{E}\text{X}}_{k,v}={\left(\frac{\omega\:}{\text{F}\left({\beta\:}_{k,v}/\sum\:_{j=1}^{K}\:{\beta\:}_{j,v}\right)}+\frac{1-\omega\:}{{ECDF}\left({\beta\:}_{k,v}\right)}\right)}^{-1}\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:$$ 2 where ECDF is the empirical cumulative distribution function, and w is the pre-set probability. As demonstrated in Fig. 3 , Held-Out likelihood, residuals, and semantic coherence show greater sensitivity to variations in the number of topics within the range of 5 to 40. After analyzing these three metrics, we determined that a candidate range of 20 to 25 topics was most appropriate. Subsequently, we evaluated the individual performance of topics within this range and found that the model with 22 topics exhibited optimal performance in both statistical metrics shown in Fig. 4 . Additionally, a qualitative examination of the content revealed that the topics had minimal overlap, and the majority were intuitively interpretable. Therefore, we selected the 22-topic solution for this study. 4. Results and discussion 4.1 Identification of topics in reviews Table 2 presents the results of the topic modeling analysis, where each topic is assigned a unique label based on the examination of the most frequently occurring words and the representative reviews associated with each topic. The labeling process aims to capture the essence of each topic by interpreting the key terms and the context in which they appear in the reviews, allowing for a logical categorization. Some topics labeled with “COVID-19” or “Pandemic” continue to be addressed in discussions, even after the pandemic has officially ended. This persistence can be attributed to the fact that certain keywords and phrases associated with these topics remain relevant to Airbnb users. As travelers reflect on their experiences during the pandemic, they continue to discuss elements such as health and safety measures, cleanliness standards, and preferences shaped by recent travel restrictions. The topic proportion for each identified topic represents the relative prevalence or importance of that topic within the entire corpus of reviews. Higher proportions indicate topics that are more commonly discussed by Airbnb users, suggesting these themes are more central to the overall guest experience or concern in the given context. Conversely, topics with lower proportions may highlight niche issues or aspects that are discussed less frequently but are still significant within the dataset. Each topic is interpreted in the following subsections, where a more detailed analysis is provided. Table 2 Topic summary Topic # Topic label Top words Topic Prop. (%) 1 Pandemic hospitality hospitality, coronavirus, global, ongoing, amidst, uncertain, survive 5.3% 2 Flexible booking during COVID-19 communication, book, restriction, change, situation, flexible, reschedule 4.8% 3 Ocean view beach, condo, pool, view, ocean, chair, resort 2.1% 4 Contactless service checkin, process, checkout, contactless, instruction, clear, seamless 4.7% 5 Cleanliness during COVID-19 covid, clean, easy, check, convenient, precaution, protocol 4.6% 6 Safety during COVID-19 safe, clean, secure, cleanliness, share, private, service 5.4% 7 Parking & noise concerns park, street, car, spot, find, noise, walk 4.2% 8 Extended stays during COVID-19 stay, covid, extend, trip, time, plan, long 4.8% 9 Neighborhood exploration walk, distance, close, location, area city, town 4.9% 10 Dirty room clean, dirty, place, bed, bathroom, sheet, floor 6.0% 11 Booking and refund airbnb, host, book, day, refund, covid, check 4.3% 12 Family-friendly accommodation house, family, kid, kitchen, year, enjoy, time, 4.0% 13 Provision of essentials and supplies provide, towel, kitchen, mask, sterilize, bathroom, clean 4.6% 14 Internet connectivity Wifi, internet, fast, strong, connection, problem, week 5.4% 15 Proximity to public transportation apartment, walk, bus, metro, train, nearby, stop 4.8% 16 Property maintenance Bedroom, aircond, maintain, floor, view, window, management 2.5% 17 Nature and outdoor experiences Island, tree, bird, garden, nature, enjoy, fresh, 3.0% 18 Security issues door, lock, window, key, break, open, knock 5.4% 19 Home-like experience home, feel, comfortable, warm, cozy, relax, lovely 4.2% 20 Apartment features apartment, bedroom, space, large, livingroom, space, bathroom 5.8% 21 Responsive communication helpful, friendly, responsive, quickly, communicative, question, answer 4.5% 22 Travel during COVID-19 pandemic, covid, stay, make, time, travel, area 5.0% 4.2 Topic interpretation Topic 1 is about the notion of hospitality during the coronavirus pandemic, highlighting the adaptability and resilience of hosts and guests. Terms like “hospitality,” “coronavirus,” “global,” “ongoing,” and “uncertain” suggest a focus on the impact of COVID-19 on the hospitality sector. Host names are often mentioned, indicating personal experiences and relationships formed during stays. The words “resource,” “appreciative,” and “survive” show the efforts and gratitude expressed during challenging times. This topic aligns with studies showing how the pandemic has forced the hospitality industry to innovate and adapt to new health and safety protocols (Gursoy & Chi, 2020). Topic 2 emphasizes the importance of flexibility amid travel restrictions due to COVID-19. Terms like “due,” “restriction,” “communication,” and “flexible” highlight the need for adaptability in booking and rescheduling. The frequent mention of “reschedule,” “guideline,” and “lockdown” points to the changing travel arrangement and the necessity for clear and supportive instructions from hosts. Sigala (2020) also suggests that effective communication and flexibility are critical for maintaining guest satisfaction during crises. Topic 3 captures the beach and resort vacation experience. Words like “beach,” “condo,” “pool,” “view,” “ocean,” and “resort” indicate a focus on seaside accommodations and activities. The presence of “snorkel,” “sunset,” and “beautiful” are related to outdoor activities. Topic 4 centers on the check-in and check-out processes, particularly emphasizing contactless and efficient experiences during COVID-19. Words like “checkin,” “checkout,” “process,” and “instruction” highlight the operational aspects of these procedures. The term “seamless” suggests minimizing friction and enhancing convenience for guests during this period. Topic 5 is related to cleanliness and safety measures during the pandemic. Terms like “clean,” “covid,” “precaution,” and “protocol” reflect a heightened emphasis on hygiene standards. The inclusion of “comfortable,” “convenient,” and “location” suggests that these measures contribute to an overall positive stay. Research has highlighted that cleanliness and perceived safety are pivotal factors influencing guest choices during the pandemic (Jiang & Wen, 2020). Topic 6 shows experiences in Airbnb listing located in the hotel, focusing on safety, service, and amenities. Words like “room,” “safe,” “hotel,” “staff,” and “service” indicate key aspects of hotel stays. The emphasis on “clean,” “private,” and “space” highlights concerns about safety and personal comfort, which have become particularly relevant during the pandemic. Topic 7 addresses common issues related to parking and noise during stays. Words like “park,” “car,” “street,” “noise,” and “hear” suggest that parking availability and noise levels are significant concerns for guests. The frequent mention of “night,” “people,” and “place” indicates these issues are often encountered in urban settings. Topic 8 focuses on extended stays and changes in travel plans due to COVID-19. Words like “stay,” “trip,” “plan,” “week,” “month,” and “future” indicate the length of stays and the impact of the pandemic on travel planning. The terms “covid,” “book,” and “extend” suggest that many guests had to adjust their travel arrangements. Extended stays have become more common as travelers seek longer-term accommodations during the pandemic (Cheung, 2024). Topic 9 highlights the importance of location convenience and proximity to attractions. Words like “perfect,” “walk,” “restaurant,” “distance,” “visit,” and “close” suggest that guests value being near dining and entertainment options. The mention of “quiet,” “neighborhood,” and “park” indicates a preference for peaceful and well-situated areas. Topic 10 addresses cleanliness and hygiene concerns, which are critical for guest satisfaction. Words like “clean,” “dirty,” “bed,” “bathroom,” “sheet,” and “floor” point to specific cleanliness issues. The frequent mention of “hair,” “stain,” “smell,” and “leave” suggests that guests often encounter unacceptable hygiene standards. Topic 11 focuses on booking, refund, and cancellation issues. Terms like “airbnb,” “host,” “book,” “refund,” “covid,” and “cancel” highlight the administrative and financial challenges faced by guests and hosts. The emphasis on “money,” “guest,” “pay,” and “message” points to the communication and financial aspects of managing bookings. Topic 12 highlights the appeal of Airbnb accommodations for families. Words like “family,” “kid,” “house,” “yard,” and “play” suggest that many reviews discuss family-friendly features. The presence of terms such as “kitchen,” “pool,” and “outdoor” indicates that amenities for cooking and outdoor activities are particularly valued. The emphasis on “space,” “group,” and “vacation” further underscores the preference for large, accommodating homes suitable for family gatherings. Topic 13 focuses on the availability of essential amenities in Airbnb rentals. Words like “provide,” “towel,” “kitchen,” “extra,” “supply,” and “clean” suggest that hosts frequently equip their properties with necessary items such as towels, kitchen supplies, and toiletries. The mention of “mask,” “soap,” and “handsanitizer” reflects the increased attention to hygiene during the COVID-19 pandemic. Topic 14 centers on the importance of reliable internet connectivity for guests who need to work remotely. Words like “work,” “wifi,” “internet,” “fast,” “connection,” and “remote” indicate that many reviews discuss the quality of the internet service. Terms like “issue,” “problem,” “fix,” and “resolve” suggest that internet problems can significantly impact the stay experience. The need for robust internet is critical for remote work, a trend that has been accelerated by the COVID-19 pandemic. Topic 15 highlights the importance of location and accessibility for Airbnb guests. Words like “walk,” “minute,” “station,” “restaurant,” “shop,” “airport,” and “location” emphasize the convenience of being close to public transport, dining, and shopping options. The frequent mention of transportation-related terms (“bus,” “taxi,” “metro,” “trainstation”) indicates that easy access to transit is a key factor in guest satisfaction. Topic 16 addresses the quality and management of rental units. Words like “unit,” “property,” “rental,” “bedroom,” “aircond,” “owner,” and “management” indicate a focus on the physical condition of the property and the role of property managers. Terms like “maintain,” “remodel,” and “manager” suggest that maintenance and managerial responsiveness are important aspects of the guest experience. Effective property management and maintenance are essential for ensuring guest satisfaction and comfort (Salvioni & Bosetti, 2014). Some guests complain about the lack of efficient solutions to solve equipment problems, such as some essential living equipment, such as aircond, causing dissatisfaction. It is important to check the functionality of essential equipment. Topic 17 captures the enjoyment of nature and local experiences during Airbnb stays. Words like “morning,” “night,” “coffee,” “day,” “island,” and “nature” suggest that guests appreciate being close to natural settings and local culture. The presence of terms like “hike,” “garden,” “bird,” “fruit,” and “beautiful” indicates that outdoor activities and scenic beauty are significant attractions. Many Airbnb users recommended the properties with these features. Topic 18 focuses on security and maintenance issues encountered by guests. Words like “door,” “lock,” “break,” “open,” “window,” and “key” highlight concerns related to security and access. The frequent mention of terms like “issue,” “problem,” “fix,” and “repair” suggests that maintenance problems can negatively impact the stay. Topic 19 emphasizes the importance of creating a home-like atmosphere for guests. Words like “home,” “feel,” “comfortable,” “beautiful,” “lovely,” “warm,” “cozy,” and “relax” suggest that guests value properties that offer a welcoming and comfortable environment. The terms “thoughtful,” “touch,” “care,” and “detail” indicate that small, considerate gestures by hosts enhance the overall experience. Providing a homely atmosphere can significantly increase guest satisfaction and loyalty (Tussyadiah & Zach, 2017). Topic 20 is related to the features and comfort of apartments. Words like “apartment,” “kitchen,” “bed,” “bedroom,” “space,” “bathroom,” “light,” and “large” highlight the physical aspects and amenities of the living space. The mention of “comfortable,” “quiet,” and “nice” suggests that these attributes contribute to a positive stay experience. Topic 21 highlights positive interactions with hosts. Words like “host,” “recommend,” “helpful,” “friendly,” “responsive,” “quick,” and “communicative” indicate that guests value hosts who are attentive and prompt in their communications. Terms like “super,” “amaze,” “fantastic,” and “wonderful” suggest that exceptional service from hosts greatly enhances the guest experience. Topic 22 focuses on travel experiences during the COVID-19 pandemic. Words like “pandemic,” “covid,” “stay,” “travel,” “safe,” “clean,” and “hygiene” indicate that guests are concerned with health and safety. In this unique period, they seek accommodations with strict cleaning protocols, spacious living spaces, and convenient geographical locations to accommodate potential long-term stays, home quarantines, or remote work. Guests expect a positive experience through friendly interactions with hosts and accommodations that meet their expectations. 4.3 Topic distribution analysis Figure 5 shows the distribution of topics between positive and negative sentiment reviews of Airbnb during this period, which provides insights into the factors that significantly influenced guest satisfaction and dissatisfaction. Key findings from the figure show that topics such as “Flexible booking during COVID-19”, “Cleanliness during COVID-19”, and “Contactless service” are predominantly associated with positive reviews. This suggests that during the pandemic, guests greatly appreciated hosts who effectively adapted to the new challenges by providing flexible booking options, ensuring high standards of cleanliness, and minimizing physical contact, which likely contributed to their sense of safety and well-being. The positive sentiment around “Neighborhood exploration”, “Family-friendly accommodation”, “Nature and outdoor experiences”, “Home-like experience”, and “Responsive communication” further indicates the importance of comfort, safety, and enriching local experiences. Guests valuing these aspects indicate that the ability to explore safe, family-friendly, and natural environments significantly contributed to their positive experiences. Conversely, topics on the right side of the distribution, such as “Dirty room”, “Booking and refund”, and “Security issues”, are predominantly linked with negative reviews. This suggests that inadequate cleanliness, difficulties in dealing with booking changes or cancellations, and perceived security issues were critical factors that led to guest dissatisfaction during the pandemic. The prominence of “Dirty room” as a negative sentiment topic highlights the heightened sensitivity towards hygiene and cleanliness during the COVID-19 period. “Booking and refund” issues likely reflect the frustration over the need for flexibility and understanding during uncertain times. “Security issues” further underlined guests’ concerns about the safety of their accommodation, which, if not adequately addressed, could significantly detract from their overall experience. 4.4 Topic correlation analysis Figure 6 shows the correlation network among key review topics on Airbnb during the COVID-19 pandemic, which reveals several salient patterns. “Cleanliness during COVID-19” demonstrates strong correlations with multiple topics such as “Safety during COVID-19”, “Provision of essentials and supplies”, “Pandemic hospitality”, and “Responsive communication”. This suggests that heightened cleanliness expectations are closely tied to broader safety concerns and reliability in communication during the pandemic. The cause-effect relationship here is likely driven by increased health anxieties, prompting guests to value and review hosts’ adherence to hygiene protocols and their ability to communicate effectively about these practices. “Flexible booking during COVID-19” and “Booking and refund” are moderately linked with “Responsive communication”. This reveals the importance of adaptability and solid communication in alleviating uncertainties related to travel plans during the pandemic. The relationship here can be attributed to the dynamic nature of travel restrictions and guests seeking assurances that their bookings could be adjusted or refunded as necessary due to varying COVID-19 circumstances. “Home-like experience” shows connections with “Neighborhood exploration”, “Nature and outdoor experiences”, and “Family-friendly accommodation”. These correlations indicate that during the pandemic, guests increasingly sought accommodations that provided a comfortable and homely environment and allowed for safe activities. This shift can be explained by the prolonged periods of confinement during lockdowns, which made travelers prioritize comfort and the ability to engage in activities within the local vicinity. “Extended stays during COVID-19” correlates with “Internet connectivity”, “Property maintenance”, and “Contactless service”. This pattern reflects the trend wherein longer stays, often for remote work purposes during the pandemic, increased guests’ dependence on stable internet connectivity and well-maintained properties, alongside a preference for minimization of direct contact. The cause-effect relationship here is likely due to the rise of remote work and extended digital nomadism during the pandemic, driving demand for reliable internet and amenities that support longer stays. Lastly, issues like “Parking & noise concerns” and “Security issues” exhibit weaker correlations, suggesting these factors remained relevant but secondary to cleanliness, communication, and adaptability emphasis during the pandemic. 4.5 Topic trend analysis Based on Fig. 7 , we found that several common attributes directly linked to COVID-19 have shown significant trends in the Airbnb market, reflecting the evolving expectations and behaviors of users during and after the pandemic. Among these, topics like “contactless service,” “cleanliness during COVID-19,” and “extended stays during COVID-19” have exhibited growing prominence, shaping the future of Airbnb services. One of the notable shifts has been the increasing popularity of “contactless service.” Initially driven by health concerns, this topic steadily gained traction during the pandemic as users appreciated the safety and convenience it provided. Even after the pandemic subsided, the demand for contactless service has persisted, now primarily fueled by its perceived convenience rather than solely by safety concerns. This evolution highlights how a pandemic-era innovation has transitioned into a standard expectation, with Airbnb users continuing to favor self-service options that reduce the need for direct interaction while streamlining their experience. Similarly, “cleanliness during COVID-19” has remained a central focus for both Airbnb hosts and users, even beyond the formal end of the pandemic. During the height of COVID-19, stringent hygiene protocols were introduced in response to health regulations, embedding cleanliness as a standard procedure across the platform. However, our topic modeling analysis indicates that the pandemic has elevated user perceptions of cleanliness to encompass not just physical tidiness but also disinfection and sanitization practices. This shift suggests that cleanliness, now intertwined with safety concerns, may have a lasting impact on guest behavior, as users continue to prioritize hygiene standards in their evaluations of accommodations. This focus on room cleanliness is further supported by the prevalence of the “dirty room” topic in our analysis, which remains a major source of dissatisfaction, underscoring the heightened emphasis on maintaining a hygienic environment. The pandemic also fostered a notable increase in “extended stays,” a trend that has persisted in the post-pandemic landscape. Many users shifted towards longer-term trips during COVID-19, likely driven by the flexibility of remote work and the desire for more isolated, stable environments. Our findings indicate that this shift has continued, with more guests opting for extended stays in Airbnb accommodations, marking a sustained change in travel preferences that could reshape the future landscape of the platform. “Provision of essentials and supplies” has demonstrated remarkable stability throughout the pandemic, though there have been notable shifts in the types of items appreciated by users. During COVID-19, guests valued essentials like grocery items, sanitizers, and personal protective equipment, which were crucial to their sense of safety. Post-pandemic, however, the focus has shifted towards amenities more commonly associated with traditional hotels, such as fresh linens, toiletries, and room service. Despite this shift, self-protection items like hand sanitizers and disinfectant wipes continue to be highly valued, reflecting lingering health concerns and a cautious approach to travel. This evolution suggests that while user expectations are blending the comforts of home with the conveniences of hotel-like services, health and hygiene remain at the forefront of their concerns. Another topic that has exhibited interesting dynamics is “responsive communication.” During the height of the pandemic, the rise of contactless services reduced the need for direct communication between guests and hosts, resulting in a decline in the emphasis on responsiveness. However, since June 2023, there has been a resurgence in the importance of responsive communication, suggesting that as travel normalizes, guests are increasingly seeking a more personal touch alongside the convenience of self-service options. This shift reflects a growing demand for a balance between the efficiency of contactless services and the reassurance provided by effective communication with hosts. “Proximity to public transportation” saw significantly less emphasis during the early stages of COVID-19 as health concerns led travelers to favor private transportation or accommodations in less populated areas. As the pandemic waned, however, interest in proximity to public transportation gradually increased, coinciding with a broader societal return to normalcy and an easing of health-related fears. Nevertheless, our analysis reveals that despite the uptick in interest, public transportation has not regained its pre-pandemic levels of importance. Instead, many travelers continue to favor alternatives such as ridesharing services and private vehicle rentals, suggesting a lasting shift in transportation preferences shaped by the pandemic. This ongoing trend indicates that flexibility, convenience, and perceived safety have become more influential factors in travelers’ decisions, requiring both hosts and transport providers to adapt to these evolving expectations. Finally, the topics of “Flexible booking during COVID-19” and “Booking and refund” reveal a lasting pattern that reflects the pandemic’s long-term impact on traveler behavior. Prior to 2021, these topics were frequently discussed as users sought reassurance amidst the uncertainties of rapidly changing travel restrictions and health guidelines. Flexible booking options became crucial during this period, offering much-needed security for travelers. Even as the pandemic has subsided, flexible booking policies have remained a stable presence in user discussions, indicating that many now perceive these policies as standard practice. However, there appears to be a gap between the lingering demand for flexibility and the level of accommodation currently provided by hosts. While Airbnb still offers flexible options, they do not always match the extensive policies implemented during the height of COVID-19, leading to some misalignment between user expectations and available offerings. 5. Conclusions and implications 5.1 Conclusion This study examines the shifts in Airbnb users’ evaluation of accommodation services during and after the COVID-19 pandemic. Our findings reveal that the pandemic led to a clear shift among Airbnb users from prioritizing hedonic values to emphasizing personal safety. This change has established lasting effects on how users evaluate the relative importance of accommodation service attributes and their overall expectations. Although many common attributes that have been reported previously were identified in this study, we found that the value assigned to these attributes varied significantly from previous research. For instance, location-related factors, which were once highlighted primarily for their convenience and accessibility (Ding et al., 2020; Guttentag et al., 2018), are also valued for the sense of separation and independence they provide from the outside world (Wong et al., 2023). This shift shows a broader trend in consumer behavior where safety and wellness have become important considerations in travel and accommodation choices (Kim et al., 2022). As noted in previous research, consumers’ perceptions of value are heavily influenced by their immediate context (Kwortnik & Thompson, 2009), and the pandemic has dramatically altered this context. Given these shifts, it is crucial to reevaluate the perceived value of accommodation attributes in light of significant changes in the external environment. The lasting impact of COVID-19 is particularly evident in the evaluation of cleanliness. Currently, Airbnb users expect not only that hosts maintain the physical appearance and condition of their properties but also that they implement safety measures to mitigate health risks. In fact, the expectation extends to daily supplies, with guests desiring health products that exceed their previous expectations. This transformation reflects how COVID-19 has changed certain guest behavior over the long term (Watson & Popescu, 2021). The heightened focus on hygiene aligns with existing research that highlights the significance of perceived safety in influencing guest satisfaction and loyalty during crises (Paulose & Shakeel, 2022). The COVID-19 pandemic has reshaped perceptions of cleanliness within the Airbnb ecosystem. This shift extends far beyond traditional notions of tidiness, transforming cleanliness into a visible, dynamic metric of trust between hosts and guests. In the post-pandemic period, cleanliness is no longer an implied expectation, but an active expression of care and responsibility. Airbnb users now anticipate safety measures, including the provision of disinfectants and hygiene products, signaling a significant elevation of standards in guest experience. This study conceptualizes cleanliness not merely as an operational criterion but as a relational tool. It serves to negotiate perceived safety and reassure travelers in an era of uncertainty. Furthermore, this research challenges the notion that crises only temporarily influence guest behavior. By illustrating how Airbnb users have integrated health-focused cleanliness into their long-term evaluations, the findings reveal a long-term restructuring of hospitality norms. This aligns with Paulose and Shakeel’s (2022) assertion that perceived safety is central to guest satisfaction during crises. However, it also broadens the discourse by suggesting that cleanliness, as redefined in the post-COVID era, now functions as an indicator of qualities increasingly valued by guests. Temporary policies implemented during COVID-19 have led to lasting changes in Airbnb users’ expectations, particularly regarding booking flexibility and refund policies. During the pandemic, many travelers faced significant disruptions that necessitated more accommodating booking arrangements. As a response, Airbnb hosts adapted their policies to offer greater flexibility, such as allowing last-minute cancellations and offering full refunds under certain conditions. This demand for flexible booking options, prominently highlighted in guest reviews, signifies a shift toward guest-centric practices that address the uncertainties of post-pandemic travel. Travelers now prioritize flexibility as a key factor when choosing accommodations (Nicolau et al., 2024). This shift suggests that hosts will need to develop policies that not only meet current expectations but are also sustainable in the long-term. The expectation for adaptability is crucial for competitive differentiation in the hospitality industry (Buhalis & Leung, 2018). The lasting effects of the COVID-19 pandemic also include certain solutions provided by Airbnb hosts that cater to the changing preferences of guests. One such solution is the implementation of contactless services, which became a norm during the pandemic and continues to be widely adopted in 2024. Many Airbnb users still express a preference for these types of services, reflecting the enduring impact of COVID-19 on guest expectations. This shift towards contactless experiences resonates with research emphasizing the role of digital solutions in enhancing guest experiences (Maitra, 2021). The adoption of contactless services not only addresses health and safety concerns but also aligns with the growing demand for convenient and streamlined experiences facilitated by technology (Yağmur et al., 2024). The pandemic has accelerated the integration of digital solutions into the hospitality industry, with guests becoming increasingly accustomed to features such as mobile check-in, keyless entry, and virtual concierge services (Ludin et al., 2022). 5.2 Theoretical Implications This study contributes to the existing hospitality literature on guest behavior in several important ways. First, by analyzing Airbnb user reviews during and more than one year after the COVID-19 pandemic, we illuminate the attributes that have become increasingly vital to guests in short-term rentals. Specifically, this research identifies safety measures, flexible policies, and enhanced service attributes emphasized during COVID-19 as essential elements that reflect the pandemic’s lasting impact on guest expectations. By integrating these findings into the existing hospitality literature, we enrich the theoretical framework concerning guest expectations in non-traditional lodging contexts. The findings highlight how the pandemic has reshaped guest priorities, necessitating a reevaluation of service standards and operational practices within the industry. This aligns with the principles of SET, which posits that customers evaluate their experiences based on perceived benefits and costs. In this context, the heightened emphasis on safety and flexibility can be viewed as a response to guests’ evolving expectations, where the perceived benefits—such as health security and reassurance—outweigh the costs associated with choosing specific accommodations. Thus, our research not only addresses a gap in the literature but also sets a foundation for future studies exploring how shifts in expectations can ultimately influence guest satisfaction in lodging environments. Moreover, these findings confirm the relevance of established attributes (Ding, 2020; Teixeira, 2023; Xu, 2020) while simultaneously introducing new dimensions that have emerged due to the global health crisis. Second, by analyzing Airbnb user reviews from mid-2020 to mid-2024, this study provides a dynamic perspective on how the importance of various attributes has evolved throughout the pandemic. This provides valuable insights into which aspects of the Airbnb experience have gained or lost significance as the pandemic progressed and its effects persisted. By exploring changes in guest preferences over time, this research contributes to our understanding of guest behaviors in uncertain conditions, adding depth to ongoing discussions in the literature about the socio-economic impacts of pandemics (Smart et al., 2021; Yang & Roehl, 2024). 5.3 Practical Implications The findings of this study have several implications for practitioners within the Airbnb sector, offering insights that can support the sustainable growth of P2P businesses in the accommodation sector. First, it is crucial for Airbnb hosts to prioritize cleanliness and sanitation protocols to meet rising guest expectations despite the ending of COVID-19. This study indicates that guests have shown a strong preference for accommodations with established health measures, implementing comprehensive cleaning practices and following guidelines from health organizations will be vital. Hosts are encouraged to communicate these protocols clearly in their listings to build trust and foster positive reviews. Second, this shift highlights the importance for Airbnb hosts to promote their attributes from multiple perspectives to effectively meet the evolving needs of guests. When advertising their listings, hosts should focus on the specific values most favored by potential guests rather than relying on overly general expectations. This means clearly communicating features such as enhanced cleanliness protocols, flexible booking options, and contactless services. Moreover, providers should consider segmenting their marketing strategies to address the diverse preferences of different guest demographics. For instance, families may prioritize amenities like kitchen facilities and spacious accommodations, while young travelers may value proximity to nightlife and local attractions. By tailoring their messaging to highlight the unique benefits that align with guests’ specific desires, hosts can better capture attention and engagement, ultimately leading to a more personalized booking experience. Third, the importance of flexibility in booking policies should be emphasized. This study revealed a strong preference among guests for the ability to easily modify or cancel their reservations in post COVID-19. To cater to this demand, hosts should consider adopting more accommodating cancellation policies and clearly communicating these options in their listings. Additionally, hosts should provide explanations when they are unable to offer the same level of flexibility as before, helping guests understand the constraints while maintaining a positive experience. Fourth, effective communication remains critical in managing guest expectations and satisfaction. Hosts are encouraged to maintain open lines of communication with potential and current guests regarding any changes to policies, services, or available amenities. Prompt responses to inquiries and proactive updates can significantly enhance the guest experience and mitigate potential dissatisfaction related to changes influenced by the pandemic. Fifth, the enhancement of home-like features within properties can cater to the sustained guest preference for comfort and familiarity. Airbnb hosts may benefit from investing in amenities that enhance the home experience. This can cater to longer stays as travelers seek the comforts of “home away from home.” Finally, these findings hold implications for policymakers and the broader research community. Airbnb management should consider supporting initiatives that enhance safety in short-term rental properties, ensuring clear guidelines are established and communicated to both hosts and guests. 6. Limitations and future research Several limitations of this study need to be addressed. First, this study only focused on Airbnb reviews written in English, which may overlook important perspectives from non-English-speaking users. Future research might conduct comparative studies that include reviews in multiple languages to provide cross-cultural insights into Airbnb user behavior during the COVID-19 pandemic. Second, this study analyzed user reviews from mid- 2020 to mid-2024, comprising the responses during the pandemic and the initial recovery phase that followed. However, this timeframe may not fully reflect long-term changes in guest behavior as the situation continues to evolve. Considering that COVID cases are still happening widely (Colarossi, 2024), future studies should extend this analysis beyond the peak of the pandemic to explore whether the shifts in guest preferences observed during this period are temporary or indicative of lasting changes in the industry. 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The transformative virtual experience paradigm: the case of Airbnb’s online experience. International Journal of Contemporary Hospitality Management , 35 (4), 1398-1422. World Health Organization. (2020). Coronavirus. Coronavirus prevention. Retrieved 11 September 2024 from: https://www.who.int/westernpacific/health-topics/coronavirus. Wu, X., Han, X., & Moon, H. (2023). Host-guest interactions in peer-to-peer accommodation: Scale development and its influence on guests’ value co-creation behaviors. International Journal of Hospitality Management , 110, 103447. Xiang, D., Jiao, G., Sun, B., Peng, C., & Ran, Y. (2022). Prosumer-to-customer exchange in the sharing economy: Evidence from the P2P accommodation context. Journal of Business Research , 145, 426-441. Xie, K. L., & Kwok, L. (2017). The effects of Airbnb’s price positioning on hotel performance. International Journal of Hospitality Management , 67, 174-184. Xu, X. (2020). How do consumers in the sharing economy value sharing? Evidence from online reviews. Decision Support Systems , 128 (71872200), 113162. Yağmur, Y., Demirel, A., & Kılıç, G. D. (2024). Top quality hotel managers’ perspectives on smart technologies: an exploratory study. Journal of Hospitality and Tourism Insights , 7 (3), 1501-1531002E Yang, Y., Li, H., & Roehl, W. S. (2024). COVID-19 pandemic and hotel property performance. International Journal of Contemporary Hospitality Management , 36 (1), 71-90. Yang, Y., Lin, M. S., & Magnini, V. P. (2024). Do guests care more about hotel cleanliness during COVID-19? Understanding factors associated with cleanliness importance of hotel guests. International Journal of Contemporary Hospitality Management , 36 (1), 239-258. Zhang, M., Geng, R., Huang, Y., & Ren, S. (2021). Terminator or accelerator? Lessons from the peer-to-peer accommodation hosts in China in responses to COVID-19. International Journal of Hospitality Management , 92 , 102760. Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5660543","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":400153214,"identity":"7f4cd621-54dc-4ec6-82ef-0147f2d2decd","order_by":0,"name":"Kai Ding","email":"","orcid":"","institution":"Ningbo University of Finance and Economics","correspondingAuthor":false,"prefix":"","firstName":"Kai","middleName":"","lastName":"Ding","suffix":""},{"id":400153215,"identity":"634f583c-f573-475f-a789-b2dadd90a499","order_by":1,"name":"Le Li","email":"","orcid":"","institution":"Universiti Putra Malaysia","correspondingAuthor":false,"prefix":"","firstName":"Le","middleName":"","lastName":"Li","suffix":""},{"id":400153218,"identity":"428fda6b-0814-457d-b211-41a9fdc2db66","order_by":2,"name":"Rongteng (Renata) Zhang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/ElEQVRIiWNgGAWjYFADCRBRAcTMzA2EVR+AazkD0sJIihbGNhCLgBbz9t7Drz9U3LGbP7v52cOv82qj+duBWn5UbMOpRebMuTSLA2eeJTfOOWZuLLvteO6Mw4wNjD1nbuPUIiGRY2ZwsO1wMrNEgpm05LZjuQ1ALcyMbYS0/DuczCaR/k1acs6x3PlEaDF+cLDhsB0PUK/kx4aa3A0EtfCcMWM4c+xwAlBvmTTDsQO5G4FaDuL1C3uP8YeKmsP28jPSt0n+qKnLnXf+8MEHPypwawECNlCMJDYACWYehsNgoQP41IMUfgAS9iAW4w+GOgKKR8EoGAWjYCQCAGiAXzMjYb2HAAAAAElFTkSuQmCC","orcid":"","institution":"Xiamen University Malaysia","correspondingAuthor":true,"prefix":"","firstName":"Rongteng","middleName":"(Renata)","lastName":"Zhang","suffix":""},{"id":400153222,"identity":"9d332a23-5149-41b3-8ecc-b248e12a60e6","order_by":3,"name":"Yuhua Chen","email":"","orcid":"","institution":"Universiti Putra Malaysia","correspondingAuthor":false,"prefix":"","firstName":"Yuhua","middleName":"","lastName":"Chen","suffix":""}],"badges":[],"createdAt":"2024-12-17 09:53:25","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5660543/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5660543/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1057/s41599-025-05153-8","type":"published","date":"2025-06-08T15:57:32+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":73659891,"identity":"ba0f0238-8d67-492d-9d24-dd835debeab9","added_by":"auto","created_at":"2025-01-13 10:56:59","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":162855,"visible":true,"origin":"","legend":"\u003cp\u003eSTM model 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topics\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-5660543/v1/0e4c6fd1d4734d86be2313f4.png"},{"id":73659894,"identity":"384c8e7a-33dd-4893-91a5-1c3b2935f9f9","added_by":"auto","created_at":"2025-01-13 10:56:59","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":41112,"visible":true,"origin":"","legend":"\u003cp\u003eTopic model evaluation\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-5660543/v1/feb491fcf7fff37cc8985709.png"},{"id":73659897,"identity":"c9465e3d-0642-427a-a5c5-4545557a9651","added_by":"auto","created_at":"2025-01-13 10:56:59","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":34041,"visible":true,"origin":"","legend":"\u003cp\u003eTopic distribution by sentiment\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-5660543/v1/6a985f5c2813e665df1324a4.png"},{"id":73659910,"identity":"e0976974-18d8-42d4-9505-12b1b8640940","added_by":"auto","created_at":"2025-01-13 10:56:59","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":190742,"visible":true,"origin":"","legend":"\u003cp\u003eTopic correlation network\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-5660543/v1/bd1c39527e949a3d060b9771.png"},{"id":73659915,"identity":"9cbe3455-01ee-49bb-b3ce-d4e1cfa9608b","added_by":"auto","created_at":"2025-01-13 10:57:00","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":787331,"visible":true,"origin":"","legend":"\u003cp\u003eTopic evolving trend\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-5660543/v1/1ad761f4c04b9f238b0196a0.png"},{"id":84243055,"identity":"ba3af475-f417-442c-8c78-de46703b72b9","added_by":"auto","created_at":"2025-06-09 16:12:24","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2322771,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5660543/v1/99cfacaf-6623-4fe6-871b-3fe3f2852486.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"From Crisis to Change: Analyzing the Lasting Influence of COVID-19 on Airbnb Users through Structural Topic Modeling","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe COVID-19 pandemic has still left an indelible mark on the global travel and accommodation sectors, forcing profound changes in guest behavior and service expectations (Srivastava \u0026amp; Kumar, 2021). As a key player in the sharing economy, Airbnb has been particularly affected, with significant reductions in travel activity and a shift in guest priorities. The global pandemic, which primarily spreads through respiratory droplets, necessitated the implementation of physical distancing, heightened hygiene practices, and reduced in-person interactions (World Health Organization, 2020). Due to the close interactions between guests and hosts in peer-to-peer (P2P) accommodations, and the importance of social engagement as a key factor in choosing P2P accommodations over traditional hotels (e.g., Filieri et al., 2023; Cheng and Jin, 2019), platforms like Airbnb were particularly hard-hit by the pandemic (Abril, 2020). A global leader in P2P accommodation sharing, Airbnb faced significant disruption during the pandemic, which reshaped the market and guest behavior. Despite an initial revenue decline in 2020, these shifts redefined the accommodation landscape in the post-pandemic era, compelling providers to adjust their offerings to meet new guest expectations (Nuttah et al., 2024).\u003c/p\u003e \u003cp\u003eExisting studies primarily focus on the immediate effects of COVID-19 on Airbnb or examine its impact during the pandemic (Gy\u0026oacute;di, 2022; Jang \u0026amp; Kim, 2022; Buzzacchi et al., 2023). Academically, while existing studies provide valuable insights into the short-term disruptions caused by COVID-19, they offer limited understanding of the behavioral shifts that unfolded throughout the pandemic and beyond, particularly as global conditions and travel restrictions evolved. This gap leaves open questions about the durability of such changes, including whether adaptations in guest preferences, such as safety concerns, persisted after the World Health Organization (WHO) declared the pandemic\u0026rsquo;s end. Practically, understanding these long-term trends is crucial for Airbnb and other accommodation providers to refine their business strategies. As the industry recovers and guest behavior stabilizes, insights into lasting changes can help platforms better anticipate and meet the needs of travelers in a post-pandemic world, ensuring their competitiveness in a transformed marketplace. As travel restrictions eased and the industry began to recover, however, there remained considerable uncertainty surrounding the long-term shifts in guest behavior (Braje et al., 2022; Buzzacchi et al., 2023). Therefore, there is a significant gap in understanding how these behaviors have evolved throughout the entirety of the pandemic and whether they have persisted in the post-pandemic era.\u003c/p\u003e \u003cp\u003eIn this study, we address the gap in understanding the long-term shifts in guest behavior in P2P accommodation. By utilizing Structural Topic Modeling (STM), we analyze a large dataset of Airbnb reviews to uncover emerging themes and patterns that reflect the long-term effects of the pandemic on guest expectations and preferences. In the following sections, we first review the literature on the impact of COVID-19 on P2P accommodation and STM. We then describe our research methodology, including the use of text analytics to analyze guest reviews. Our findings are presented in detail, followed by a discussion of their implications for both academic researchers and practitioners. Finally, we conclude by highlighting the contributions of this study to the ongoing discourse on the transformation of P2P accommodations in a post-pandemic world.\u003c/p\u003e"},{"header":"2. Literature review","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Theoretical foundation\u003c/h2\u003e \u003cp\u003eIn examining the changes in consumer behavior brought about by the COVID-19 pandemic, various theories offer valuable perspectives to understand the driving forces behind these shifts. One such perspective is provided by the Health Belief Model, which suggests that consumers\u0026rsquo; choices in hospitality and tourism are heavily influenced by their perceptions of health risks and the benefits they associate with safer alternatives (Mirakzadeh et al., 2021; Naseer et al., 2022). Building on this, the Theory of Planned Behavior further elaborates on how attitudes towards safety, subjective norms, and perceived behavioral control collectively shape the intention to choose specific accommodations (Huang et al., 2020; Tajeddini et al., 2021). Moreover, the Technology Acceptance Model offers insights into the adoption of digital platforms, such as Airbnb, emphasizing how perceived usefulness\u0026mdash;particularly in terms of contactless stays and flexible policies\u0026mdash;drives consumer preferences (Jung et al., 2021).\u003c/p\u003e \u003cp\u003eIn this study, we mainly draw on Social Exchange Theory (SET), which suggests that individuals engage in interactions based on a cost-benefit analysis and the expectation of reciprocal benefits, and has been widely applied in the hospitality sector (Khan \u0026amp; Hefny, 2019; Priporas et al., 2017; Wang et al., 2022). Applied to the P2P accommodation context, SET helps explain how guests re-evaluated their choices during and after the pandemic, weighing factors such as safety, flexibility, and perceived risk against the benefits of travel and accommodation experiences. As the pandemic heightened concerns over health and safety, guests likely made decisions based on the perceived \u0026ldquo;cost\u0026rdquo; of potential exposure to COVID-19, altering their behavior towards preferences for accommodations that prioritized hygiene and safety protocols. SET can offer robust frameworks for analyzing the pandemic-induced changes in consumer behavior within the P2P accommodation sector, providing both explanatory power and theoretical insights into these long-term shifts.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Impact of COVID-19 on P2P Accommodations\u003c/h2\u003e \u003cp\u003eThe COVID-19 pandemic brought unprecedented disruptions to the travel and hospitality sectors, deeply impacting P2P accommodations like Airbnb. This section explores how the pandemic has influenced the preferences of Airbnb users, drawing insights from various studies that examine the evolving dynamics of P2P accommodation during and after the pandemic. The pandemic\u0026rsquo;s impact on the accommodation-sharing sector revealed a paradox in which the sector\u0026rsquo;s strengths, such as flexibility and personal interaction, became vulnerabilities amidst the crisis (Gerwe, 2021). Studies have highlighted how hosts\u0026rsquo; responses to the pandemic varied significantly. Farmaki et al. (2020) interviewed hosts and found that reactions ranged from temporarily halting operations to adopting safety protocols and emphasizing remote hosting options. In the Chinese context, Zhang et al. (2021) found that the pandemic acted as an accelerator for some hosts to embrace the original ethos of the sharing economy, emphasizing personal, experiential hosting rather than a purely commercial focus.\u003c/p\u003e \u003cp\u003eAirbnb users\u0026rsquo; preferences have experienced significant changes due to the COVID-19 pandemic. One of the shifts has been the increased preference for entire flat rentals over shared accommodations and traditional hotels. Bresciani et al. (2021) found that, in response to physical distancing concerns, guests showed a clear preference for renting entire flats, driven by the desire for greater privacy and control over their accommodation environment. Nicolau et al. (2023) also found that travelers prefer Airbnb entire flats/apartments during periods of rising pandemic risk, validating a preference for Airbnb in high-risk scenarios. This preference for privacy is closely linked to changes in locational choices. Turk and Sap (2021) noted that while the physical attributes of listings remained largely consistent with pre-COVID trends, there were significant shifts in spatial preferences. These shifts can be attributed to the pandemic\u0026rsquo;s impact on travel restrictions and heightened safety concerns, prompting travelers to seek out accommodations in less crowded or more isolated locations. Trust also emerged as a crucial factor influencing traveler behavior in the post-pandemic period. Braje et al. (2022) observed that trust in both the platforms and the hosts played a more significant role in determining repurchase intentions among short-term rental users. This finding underscores the increasing importance of safety and reliability in accommodation choices during uncertain times. Hygiene and cleanliness, in particular, have become paramount concerns for travelers. Godovykh et al. (2023) highlighted that transparency around cleanliness measures significantly boosted guest trust and positively influenced their behavioral intentions. Similarly, Kim et al. (2022) found a substantial shift in consumer preferences, with cleanliness overtaking location as the primary consideration during the pandemic.\u003c/p\u003e \u003cp\u003eMore recent studies continue to deepen our understanding of these shifts. Chen et al. (2023) explored continuous sharing behavior (CSB) among providers and found that positive feedback significantly influenced providers\u0026rsquo; willingness to continue sharing, with intrinsic motivation playing a key mediating role. Meanwhile, FB Teixeira (2023) systematically investigated the reasons why guests opt for or reject P2P hospitality, highlighting utilitarian factors such as feeling welcomed by hosts and comfort in the neighborhood as primary motivators, while also noting safety concerns as an important consideration. These insights suggest that while traditional motivators for P2P accommodation remain relevant, the pandemic has introduced new priorities, particularly around safety and trust. In the post-pandemic context, Sahadev et al. (2023) revealed that user-generated content, specifically ratings and sentiments, had significant positive effects on occupancy rates, demonstrating the continued importance of guest feedback in driving demand. Furthermore, Wu et al. (2023) examined host-guest interactions, emphasizing that such interactions, particularly for sociable guests, played a critical role in fostering commercial friendships and co-creating value. This research highlights the evolving nature of host-guest dynamics, suggesting that while safety and trust are paramount, the interpersonal elements of the P2P accommodation experience still hold significant value for certain segments of the market.\u003c/p\u003e \u003cp\u003eDespite significant advances in understanding the key attributes valued by Airbnb users, a crucial gap remains in the literature regarding the long-term effects of the COVID-19 pandemic on consumer preferences. While numerous studies have focused on guest behavior either during or immediately after the pandemic (Braje et al., 2022; Turk and Sap, 2021), there is a need for longitudinal studies to track the long-term recovery and adaptation of the Airbnb market, as the immediate post-pandemic period may not fully reflect enduring changes. Existing studies often fail to provide a comprehensive perspective that tracks how guest preferences have changed from pre-pandemic conditions, through various stages of the pandemic, and into the post-pandemic era following the WHO declaration of the pandemic\u0026rsquo;s end. It remains unclear whether the observed shifts in guest behavior represent long-term, structural changes or merely short-term adaptations in response to the crisis. The inherently dynamic nature of the P2P accommodation sector further justify the importance of this research gap.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Structural topic model\u003c/h2\u003e \u003cp\u003eTo explore the evolving preferences of Airbnb users amidst the progression of the pandemic, we employed structural topic modeling (STM) to analyze Airbnb reviews. STM, a contemporary addition to the suite of topic modeling algorithms developed in recent decades, examines the observed words in a text corpus to uncover latent topics or themes. It uses the Bayesian generative topic model which assumes each topic as a distribution over words, and each document as a mixture of topics (Blei, 2012; Roberts et al., 2014). This research employed STM for two main reasons. Firstly, STM is a mixed membership model, allowing documents to cover multiple topics\u0026mdash;an ideal characteristic for analyzing accommodation reviews, which often contain various preference traits. Secondly, STM enables the incorporation of document-level covariates in the analysis. Given the study\u0026rsquo;s focus on identifying shifts in Airbnb users\u0026rsquo; preferences during the pandemic, factors such as the date of the reviewer\u0026rsquo;s stay were crucial to include. Additionally, review extremity, distinguishing between positive and negative reviews, could also be considered as a covariate within STM (Roberts et al., 2016).\u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the framework of STM for analyzing textual data, allowing for the integration of covariates that influence both the prevalence and content of topics. In the context of this study, we leverage STM to analyze Airbnb reviews and track changing user preferences during and after the COVID-19 pandemic, incorporating review date and sentence polarity as covariates. The model begins by using these covariates to influence the document-topic proportions (\u003cem\u003eθ\u003c/em\u003e), which dictate the distribution of topics within each review. Similarly, sentence polarity (positive or negative sentiment) affects how users express their preferences, with more negative sentiment possibly amplifying discussions around negative experiences during the pandemic. Per-word topic assignments (\u003cem\u003ez\u003c/em\u003e) assign each word in a review to a specific topic, thereby identifying how individual words relate to broader themes. Topic word distributions (\u003cem\u003eβ\u003c/em\u003e) specify the probability of words occurring in each topic. These distributions are influenced by content covariates (\u003cem\u003eY\u003c/em\u003e), allowing the language used in each topic to evolve based on the covariates.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"3. Methodology","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Data collection\u003c/h2\u003e \u003cp\u003eIn this study, we collected data from InsideAirbnb.com, a platform that openly shares Airbnb data scraped from the official Airbnb site. We focused on textual reviews collected between May 2020 and May 2024 from four major cities: New York, London, Los Angeles, and Barcelona, which are popular tourist destinations that experienced pronounced impacts from COVID-19, making them particularly relevant for our analysis. This timeframe captures critical stages of the pandemic, from the initial outbreak through various responses, including lockdowns and the gradual reopening of economies. It allows for longitudinal analysis of guest sentiment, helping to identify trends in guest preferences and behaviors during the crisis and recovery phases. In addition, these cities were selected to enhance the generalizability of our findings, as they are among the top destinations for Airbnb listings worldwide and boast a significant number of English-language reviews. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e provides the methodological framework of this research.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Data pre-processing\u003c/h2\u003e \u003cp\u003eFor the topic modeling analysis of Airbnb online reviews, we followed a structured text pre-processing procedure in line with previous research (Ding et al., 2024; Xu, 2020). (1) We began by filtering the data to include only English-language reviews using the \u0026ldquo;textcat\u0026rdquo; package from R programming. (2) Next, the text was cleaned by converting all content to lowercase, removing punctuation and non-alphabetic characters, and eliminating common stopwords (e.g., is, a, and the). (3) Subsequently, we applied tokenization to break down the text into individual words, followed by lemmatization to reduce words to their root forms. Lemmatization converts words like \u0026lsquo;running\u0026rsquo; or \u0026lsquo;ran\u0026rsquo; to their base form \u0026lsquo;run,\u0026rsquo; which allows for a more accurate interpretation of the underlying content. (4) Since topic modeling tends to perform poorly with a short text, reviews containing fewer than six words were excluded. Additionally, uncommon words that appeared in less than 2% of the initial corpus were removed (Korfiatis et al., 2019). The final dataset comprises 461,509 reviews across 18,465 listed properties. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e provides a summary of the review statistics, categorized by city.\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\u003eSummary statistics of review sample by cities\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYear\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFrequency\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePercentage\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNew York\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18,345\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.97%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28,245\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.12%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25,432\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.51%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40,789\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.83%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24,754\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.37%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLondon\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12,874\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.79%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32,039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.94%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21,901\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.75%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30,432\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.59%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17,584\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.81%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLos Angeles\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13,123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.84%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25,678\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.56%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32,345\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.99%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30,672\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.64%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19,021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.12%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBarcelona\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9,032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.96%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18,456\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.00%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22,567\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.89%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26,875\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.83%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11,345\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.46%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e461,509\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e100%\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=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Covariate set up\u003c/h2\u003e \u003cp\u003eTo gain an understanding of Airbnb user sentiment and how it evolves over time, we incorporate the polarity of each sentence as well as the review date into the model to analyze their influence on topic prevalence. To capture the sentiment of user reviews, we use the \u0026ldquo;Sentimentr\u0026rdquo; package in R, a tool that employs Bayesian classifiers to categorize each sentence based on its polarity.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Determining the topic number\u003c/h2\u003e \u003cp\u003eThe most important task in applying topic modeling is determining the appropriate number of topics. Although there is no universally correct number, statistical metrics can guide the selection process. In this study, we approach topic selection in two phases. The first phase involves using four criteria\u0026mdash;Held-Out likelihood, residuals, semantic coherence, and lower bound\u0026mdash;to narrow down the candidate number of topics. We specifically selected statistical metrics that are sensitive to changes in the number of topics. In the second phase, we assess the performance of individual topics across different topic solutions using semantic coherence and exclusivity. Semantic coherence is an indicator of topic quality, proposed by Mimno et al. (2011). When the most probable words in each topic frequently co-occur, the semantic coherence of that topic is maximized. Let \u003cem\u003eD\u003c/em\u003e(\u003cem\u003ev\u003c/em\u003e,\u003cem\u003ev\u0026prime;\u003c/em\u003e) represent the co-occurrence frequency of words \u003cem\u003ev\u003c/em\u003e and \u003cem\u003ev\u0026prime;\u003c/em\u003e in a specific document. For the \u003cem\u003eM\u003c/em\u003e high-frequency words in topic \u003cem\u003ek\u003c/em\u003e, the semantic coherence of topic \u003cem\u003ek\u003c/em\u003e can be expressed as:\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\:{C}_{k}=\\sum\\:_{i=2}^{M}\\:\\sum\\:_{j=1}^{i-1}\\:\\text{l}\\text{n}\\left(\\frac{D\\left({v}_{i},{v}_{j}\\right)+1}{D\\left({v}_{j}\\right)}\\right)$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eHowever, with a smaller number of topics, the topics generated are often dominated by common words, leading to an overestimation of the semantic coherence function. Therefore, it is necessary to introduce the topic exclusivity indicator, FREX, which balances word frequency by calculating the weighted harmonic mean of word frequency and exclusivity, thereby improving the model\u0026rsquo;s quality. The FREX calculation formula is:\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$$\\:{\\text{F}\\text{R}\\text{E}\\text{X}}_{k,v}={\\left(\\frac{\\omega\\:}{\\text{F}\\left({\\beta\\:}_{k,v}/\\sum\\:_{j=1}^{K}\\:{\\beta\\:}_{j,v}\\right)}+\\frac{1-\\omega\\:}{{ECDF}\\left({\\beta\\:}_{k,v}\\right)}\\right)}^{-1}\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere \u003cem\u003eECDF\u003c/em\u003e is the empirical cumulative distribution function, and \u003cem\u003ew\u003c/em\u003e is the pre-set probability.\u003c/p\u003e \u003cp\u003eAs demonstrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Held-Out likelihood, residuals, and semantic coherence show greater sensitivity to variations in the number of topics within the range of 5 to 40. After analyzing these three metrics, we determined that a candidate range of 20 to 25 topics was most appropriate. Subsequently, we evaluated the individual performance of topics within this range and found that the model with 22 topics exhibited optimal performance in both statistical metrics shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. Additionally, a qualitative examination of the content revealed that the topics had minimal overlap, and the majority were intuitively interpretable. Therefore, we selected the 22-topic solution for this study.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Results and discussion","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Identification of topics in reviews\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents the results of the topic modeling analysis, where each topic is assigned a unique label based on the examination of the most frequently occurring words and the representative reviews associated with each topic. The labeling process aims to capture the essence of each topic by interpreting the key terms and the context in which they appear in the reviews, allowing for a logical categorization. Some topics labeled with \u0026ldquo;COVID-19\u0026rdquo; or \u0026ldquo;Pandemic\u0026rdquo; continue to be addressed in discussions, even after the pandemic has officially ended. This persistence can be attributed to the fact that certain keywords and phrases associated with these topics remain relevant to Airbnb users. As travelers reflect on their experiences during the pandemic, they continue to discuss elements such as health and safety measures, cleanliness standards, and preferences shaped by recent travel restrictions. The topic proportion for each identified topic represents the relative prevalence or importance of that topic within the entire corpus of reviews. Higher proportions indicate topics that are more commonly discussed by Airbnb users, suggesting these themes are more central to the overall guest experience or concern in the given context. Conversely, topics with lower proportions may highlight niche issues or aspects that are discussed less frequently but are still significant within the dataset. Each topic is interpreted in the following subsections, where a more detailed analysis is provided.\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\u003eTopic summary\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTopic #\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTopic label\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTop words\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTopic Prop. (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePandemic hospitality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ehospitality, coronavirus, global, ongoing, amidst, uncertain, survive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.3%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFlexible booking during COVID-19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ecommunication, book, restriction, change, situation, flexible, reschedule\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.8%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOcean view\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ebeach, condo, pool, view, ocean, chair, resort\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.1%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eContactless service\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003echeckin, process, checkout, contactless, instruction, clear, seamless\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.7%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e5\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCleanliness during COVID-19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ecovid, clean, easy, check, convenient, precaution, protocol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.6%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e6\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSafety during COVID-19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003esafe, clean, secure, cleanliness, share, private, service\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.4%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e7\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eParking \u0026amp; noise concerns\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003epark, street, car, spot, find, noise, walk\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.2%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e8\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExtended stays during COVID-19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003estay, covid, extend, trip, time, plan, long\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.8%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e9\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNeighborhood exploration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ewalk, distance, close, location, area\u003c/p\u003e \u003cp\u003ecity, town\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.9%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e10\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDirty room\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eclean, dirty, place, bed, bathroom, sheet, floor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.0%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e11\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBooking and refund\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eairbnb, host, book, day, refund, covid, check\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.3%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e12\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFamily-friendly accommodation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ehouse, family, kid, kitchen, year, enjoy, time,\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.0%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e13\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProvision of essentials and supplies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eprovide, towel, kitchen, mask, sterilize, bathroom, clean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.6%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e14\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInternet connectivity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWifi, internet, fast, strong, connection, problem, week\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.4%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e15\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProximity to public transportation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eapartment, walk, bus, metro, train, nearby, stop\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.8%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e16\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProperty maintenance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBedroom, aircond, maintain, floor, view, window, management\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.5%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e17\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNature and outdoor experiences\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIsland, tree, bird, garden, nature, enjoy, fresh,\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.0%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e18\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSecurity issues\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003edoor, lock, window, key, break, open, knock\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.4%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e19\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHome-like experience\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ehome, feel, comfortable, warm, cozy, relax, lovely\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.2%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e20\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eApartment features\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eapartment, bedroom, space, large, livingroom, space, bathroom\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.8%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e21\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eResponsive communication\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ehelpful, friendly, responsive, quickly, communicative, question, answer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.5%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e22\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTravel during COVID-19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003epandemic, covid, stay, make, time, travel, area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.0%\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=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Topic interpretation\u003c/h2\u003e \u003cp\u003eTopic 1 is about the notion of hospitality during the coronavirus pandemic, highlighting the adaptability and resilience of hosts and guests. Terms like \u0026ldquo;hospitality,\u0026rdquo; \u0026ldquo;coronavirus,\u0026rdquo; \u0026ldquo;global,\u0026rdquo; \u0026ldquo;ongoing,\u0026rdquo; and \u0026ldquo;uncertain\u0026rdquo; suggest a focus on the impact of COVID-19 on the hospitality sector. Host names are often mentioned, indicating personal experiences and relationships formed during stays. The words \u0026ldquo;resource,\u0026rdquo; \u0026ldquo;appreciative,\u0026rdquo; and \u0026ldquo;survive\u0026rdquo; show the efforts and gratitude expressed during challenging times. This topic aligns with studies showing how the pandemic has forced the hospitality industry to innovate and adapt to new health and safety protocols (Gursoy \u0026amp; Chi, 2020).\u003c/p\u003e \u003cp\u003eTopic 2 emphasizes the importance of flexibility amid travel restrictions due to COVID-19. Terms like \u0026ldquo;due,\u0026rdquo; \u0026ldquo;restriction,\u0026rdquo; \u0026ldquo;communication,\u0026rdquo; and \u0026ldquo;flexible\u0026rdquo; highlight the need for adaptability in booking and rescheduling. The frequent mention of \u0026ldquo;reschedule,\u0026rdquo; \u0026ldquo;guideline,\u0026rdquo; and \u0026ldquo;lockdown\u0026rdquo; points to the changing travel arrangement and the necessity for clear and supportive instructions from hosts. Sigala (2020) also suggests that effective communication and flexibility are critical for maintaining guest satisfaction during crises.\u003c/p\u003e \u003cp\u003eTopic 3 captures the beach and resort vacation experience. Words like \u0026ldquo;beach,\u0026rdquo; \u0026ldquo;condo,\u0026rdquo; \u0026ldquo;pool,\u0026rdquo; \u0026ldquo;view,\u0026rdquo; \u0026ldquo;ocean,\u0026rdquo; and \u0026ldquo;resort\u0026rdquo; indicate a focus on seaside accommodations and activities. The presence of \u0026ldquo;snorkel,\u0026rdquo; \u0026ldquo;sunset,\u0026rdquo; and \u0026ldquo;beautiful\u0026rdquo; are related to outdoor activities.\u003c/p\u003e \u003cp\u003eTopic 4 centers on the check-in and check-out processes, particularly emphasizing contactless and efficient experiences during COVID-19. Words like \u0026ldquo;checkin,\u0026rdquo; \u0026ldquo;checkout,\u0026rdquo; \u0026ldquo;process,\u0026rdquo; and \u0026ldquo;instruction\u0026rdquo; highlight the operational aspects of these procedures. The term \u0026ldquo;seamless\u0026rdquo; suggests minimizing friction and enhancing convenience for guests during this period.\u003c/p\u003e \u003cp\u003eTopic 5 is related to cleanliness and safety measures during the pandemic. Terms like \u0026ldquo;clean,\u0026rdquo; \u0026ldquo;covid,\u0026rdquo; \u0026ldquo;precaution,\u0026rdquo; and \u0026ldquo;protocol\u0026rdquo; reflect a heightened emphasis on hygiene standards. The inclusion of \u0026ldquo;comfortable,\u0026rdquo; \u0026ldquo;convenient,\u0026rdquo; and \u0026ldquo;location\u0026rdquo; suggests that these measures contribute to an overall positive stay. Research has highlighted that cleanliness and perceived safety are pivotal factors influencing guest choices during the pandemic (Jiang \u0026amp; Wen, 2020).\u003c/p\u003e \u003cp\u003eTopic 6 shows experiences in Airbnb listing located in the hotel, focusing on safety, service, and amenities. Words like \u0026ldquo;room,\u0026rdquo; \u0026ldquo;safe,\u0026rdquo; \u0026ldquo;hotel,\u0026rdquo; \u0026ldquo;staff,\u0026rdquo; and \u0026ldquo;service\u0026rdquo; indicate key aspects of hotel stays. The emphasis on \u0026ldquo;clean,\u0026rdquo; \u0026ldquo;private,\u0026rdquo; and \u0026ldquo;space\u0026rdquo; highlights concerns about safety and personal comfort, which have become particularly relevant during the pandemic.\u003c/p\u003e \u003cp\u003eTopic 7 addresses common issues related to parking and noise during stays. Words like \u0026ldquo;park,\u0026rdquo; \u0026ldquo;car,\u0026rdquo; \u0026ldquo;street,\u0026rdquo; \u0026ldquo;noise,\u0026rdquo; and \u0026ldquo;hear\u0026rdquo; suggest that parking availability and noise levels are significant concerns for guests. The frequent mention of \u0026ldquo;night,\u0026rdquo; \u0026ldquo;people,\u0026rdquo; and \u0026ldquo;place\u0026rdquo; indicates these issues are often encountered in urban settings.\u003c/p\u003e \u003cp\u003eTopic 8 focuses on extended stays and changes in travel plans due to COVID-19. Words like \u0026ldquo;stay,\u0026rdquo; \u0026ldquo;trip,\u0026rdquo; \u0026ldquo;plan,\u0026rdquo; \u0026ldquo;week,\u0026rdquo; \u0026ldquo;month,\u0026rdquo; and \u0026ldquo;future\u0026rdquo; indicate the length of stays and the impact of the pandemic on travel planning. The terms \u0026ldquo;covid,\u0026rdquo; \u0026ldquo;book,\u0026rdquo; and \u0026ldquo;extend\u0026rdquo; suggest that many guests had to adjust their travel arrangements. Extended stays have become more common as travelers seek longer-term accommodations during the pandemic (Cheung, 2024).\u003c/p\u003e \u003cp\u003eTopic 9 highlights the importance of location convenience and proximity to attractions. Words like \u0026ldquo;perfect,\u0026rdquo; \u0026ldquo;walk,\u0026rdquo; \u0026ldquo;restaurant,\u0026rdquo; \u0026ldquo;distance,\u0026rdquo; \u0026ldquo;visit,\u0026rdquo; and \u0026ldquo;close\u0026rdquo; suggest that guests value being near dining and entertainment options. The mention of \u0026ldquo;quiet,\u0026rdquo; \u0026ldquo;neighborhood,\u0026rdquo; and \u0026ldquo;park\u0026rdquo; indicates a preference for peaceful and well-situated areas.\u003c/p\u003e \u003cp\u003eTopic 10 addresses cleanliness and hygiene concerns, which are critical for guest satisfaction. Words like \u0026ldquo;clean,\u0026rdquo; \u0026ldquo;dirty,\u0026rdquo; \u0026ldquo;bed,\u0026rdquo; \u0026ldquo;bathroom,\u0026rdquo; \u0026ldquo;sheet,\u0026rdquo; and \u0026ldquo;floor\u0026rdquo; point to specific cleanliness issues. The frequent mention of \u0026ldquo;hair,\u0026rdquo; \u0026ldquo;stain,\u0026rdquo; \u0026ldquo;smell,\u0026rdquo; and \u0026ldquo;leave\u0026rdquo; suggests that guests often encounter unacceptable hygiene standards.\u003c/p\u003e \u003cp\u003eTopic 11 focuses on booking, refund, and cancellation issues. Terms like \u0026ldquo;airbnb,\u0026rdquo; \u0026ldquo;host,\u0026rdquo; \u0026ldquo;book,\u0026rdquo; \u0026ldquo;refund,\u0026rdquo; \u0026ldquo;covid,\u0026rdquo; and \u0026ldquo;cancel\u0026rdquo; highlight the administrative and financial challenges faced by guests and hosts. The emphasis on \u0026ldquo;money,\u0026rdquo; \u0026ldquo;guest,\u0026rdquo; \u0026ldquo;pay,\u0026rdquo; and \u0026ldquo;message\u0026rdquo; points to the communication and financial aspects of managing bookings.\u003c/p\u003e \u003cp\u003eTopic 12 highlights the appeal of Airbnb accommodations for families. Words like \u0026ldquo;family,\u0026rdquo; \u0026ldquo;kid,\u0026rdquo; \u0026ldquo;house,\u0026rdquo; \u0026ldquo;yard,\u0026rdquo; and \u0026ldquo;play\u0026rdquo; suggest that many reviews discuss family-friendly features. The presence of terms such as \u0026ldquo;kitchen,\u0026rdquo; \u0026ldquo;pool,\u0026rdquo; and \u0026ldquo;outdoor\u0026rdquo; indicates that amenities for cooking and outdoor activities are particularly valued. The emphasis on \u0026ldquo;space,\u0026rdquo; \u0026ldquo;group,\u0026rdquo; and \u0026ldquo;vacation\u0026rdquo; further underscores the preference for large, accommodating homes suitable for family gatherings.\u003c/p\u003e \u003cp\u003eTopic 13 focuses on the availability of essential amenities in Airbnb rentals. Words like \u0026ldquo;provide,\u0026rdquo; \u0026ldquo;towel,\u0026rdquo; \u0026ldquo;kitchen,\u0026rdquo; \u0026ldquo;extra,\u0026rdquo; \u0026ldquo;supply,\u0026rdquo; and \u0026ldquo;clean\u0026rdquo; suggest that hosts frequently equip their properties with necessary items such as towels, kitchen supplies, and toiletries. The mention of \u0026ldquo;mask,\u0026rdquo; \u0026ldquo;soap,\u0026rdquo; and \u0026ldquo;handsanitizer\u0026rdquo; reflects the increased attention to hygiene during the COVID-19 pandemic.\u003c/p\u003e \u003cp\u003eTopic 14 centers on the importance of reliable internet connectivity for guests who need to work remotely. Words like \u0026ldquo;work,\u0026rdquo; \u0026ldquo;wifi,\u0026rdquo; \u0026ldquo;internet,\u0026rdquo; \u0026ldquo;fast,\u0026rdquo; \u0026ldquo;connection,\u0026rdquo; and \u0026ldquo;remote\u0026rdquo; indicate that many reviews discuss the quality of the internet service. Terms like \u0026ldquo;issue,\u0026rdquo; \u0026ldquo;problem,\u0026rdquo; \u0026ldquo;fix,\u0026rdquo; and \u0026ldquo;resolve\u0026rdquo; suggest that internet problems can significantly impact the stay experience. The need for robust internet is critical for remote work, a trend that has been accelerated by the COVID-19 pandemic.\u003c/p\u003e \u003cp\u003eTopic 15 highlights the importance of location and accessibility for Airbnb guests. Words like \u0026ldquo;walk,\u0026rdquo; \u0026ldquo;minute,\u0026rdquo; \u0026ldquo;station,\u0026rdquo; \u0026ldquo;restaurant,\u0026rdquo; \u0026ldquo;shop,\u0026rdquo; \u0026ldquo;airport,\u0026rdquo; and \u0026ldquo;location\u0026rdquo; emphasize the convenience of being close to public transport, dining, and shopping options. The frequent mention of transportation-related terms (\u0026ldquo;bus,\u0026rdquo; \u0026ldquo;taxi,\u0026rdquo; \u0026ldquo;metro,\u0026rdquo; \u0026ldquo;trainstation\u0026rdquo;) indicates that easy access to transit is a key factor in guest satisfaction.\u003c/p\u003e \u003cp\u003eTopic 16 addresses the quality and management of rental units. Words like \u0026ldquo;unit,\u0026rdquo; \u0026ldquo;property,\u0026rdquo; \u0026ldquo;rental,\u0026rdquo; \u0026ldquo;bedroom,\u0026rdquo; \u0026ldquo;aircond,\u0026rdquo; \u0026ldquo;owner,\u0026rdquo; and \u0026ldquo;management\u0026rdquo; indicate a focus on the physical condition of the property and the role of property managers. Terms like \u0026ldquo;maintain,\u0026rdquo; \u0026ldquo;remodel,\u0026rdquo; and \u0026ldquo;manager\u0026rdquo; suggest that maintenance and managerial responsiveness are important aspects of the guest experience. Effective property management and maintenance are essential for ensuring guest satisfaction and comfort (Salvioni \u0026amp; Bosetti, 2014). Some guests complain about the lack of efficient solutions to solve equipment problems, such as some essential living equipment, such as aircond, causing dissatisfaction. It is important to check the functionality of essential equipment.\u003c/p\u003e \u003cp\u003eTopic 17 captures the enjoyment of nature and local experiences during Airbnb stays. Words like \u0026ldquo;morning,\u0026rdquo; \u0026ldquo;night,\u0026rdquo; \u0026ldquo;coffee,\u0026rdquo; \u0026ldquo;day,\u0026rdquo; \u0026ldquo;island,\u0026rdquo; and \u0026ldquo;nature\u0026rdquo; suggest that guests appreciate being close to natural settings and local culture. The presence of terms like \u0026ldquo;hike,\u0026rdquo; \u0026ldquo;garden,\u0026rdquo; \u0026ldquo;bird,\u0026rdquo; \u0026ldquo;fruit,\u0026rdquo; and \u0026ldquo;beautiful\u0026rdquo; indicates that outdoor activities and scenic beauty are significant attractions. Many Airbnb users recommended the properties with these features.\u003c/p\u003e \u003cp\u003eTopic 18 focuses on security and maintenance issues encountered by guests. Words like \u0026ldquo;door,\u0026rdquo; \u0026ldquo;lock,\u0026rdquo; \u0026ldquo;break,\u0026rdquo; \u0026ldquo;open,\u0026rdquo; \u0026ldquo;window,\u0026rdquo; and \u0026ldquo;key\u0026rdquo; highlight concerns related to security and access. The frequent mention of terms like \u0026ldquo;issue,\u0026rdquo; \u0026ldquo;problem,\u0026rdquo; \u0026ldquo;fix,\u0026rdquo; and \u0026ldquo;repair\u0026rdquo; suggests that maintenance problems can negatively impact the stay.\u003c/p\u003e \u003cp\u003eTopic 19 emphasizes the importance of creating a home-like atmosphere for guests. Words like \u0026ldquo;home,\u0026rdquo; \u0026ldquo;feel,\u0026rdquo; \u0026ldquo;comfortable,\u0026rdquo; \u0026ldquo;beautiful,\u0026rdquo; \u0026ldquo;lovely,\u0026rdquo; \u0026ldquo;warm,\u0026rdquo; \u0026ldquo;cozy,\u0026rdquo; and \u0026ldquo;relax\u0026rdquo; suggest that guests value properties that offer a welcoming and comfortable environment. The terms \u0026ldquo;thoughtful,\u0026rdquo; \u0026ldquo;touch,\u0026rdquo; \u0026ldquo;care,\u0026rdquo; and \u0026ldquo;detail\u0026rdquo; indicate that small, considerate gestures by hosts enhance the overall experience. Providing a homely atmosphere can significantly increase guest satisfaction and loyalty (Tussyadiah \u0026amp; Zach, 2017).\u003c/p\u003e \u003cp\u003eTopic 20 is related to the features and comfort of apartments. Words like \u0026ldquo;apartment,\u0026rdquo; \u0026ldquo;kitchen,\u0026rdquo; \u0026ldquo;bed,\u0026rdquo; \u0026ldquo;bedroom,\u0026rdquo; \u0026ldquo;space,\u0026rdquo; \u0026ldquo;bathroom,\u0026rdquo; \u0026ldquo;light,\u0026rdquo; and \u0026ldquo;large\u0026rdquo; highlight the physical aspects and amenities of the living space. The mention of \u0026ldquo;comfortable,\u0026rdquo; \u0026ldquo;quiet,\u0026rdquo; and \u0026ldquo;nice\u0026rdquo; suggests that these attributes contribute to a positive stay experience. Topic 21 highlights positive interactions with hosts. Words like \u0026ldquo;host,\u0026rdquo; \u0026ldquo;recommend,\u0026rdquo; \u0026ldquo;helpful,\u0026rdquo; \u0026ldquo;friendly,\u0026rdquo; \u0026ldquo;responsive,\u0026rdquo; \u0026ldquo;quick,\u0026rdquo; and \u0026ldquo;communicative\u0026rdquo; indicate that guests value hosts who are attentive and prompt in their communications. Terms like \u0026ldquo;super,\u0026rdquo; \u0026ldquo;amaze,\u0026rdquo; \u0026ldquo;fantastic,\u0026rdquo; and \u0026ldquo;wonderful\u0026rdquo; suggest that exceptional service from hosts greatly enhances the guest experience.\u003c/p\u003e \u003cp\u003eTopic 22 focuses on travel experiences during the COVID-19 pandemic. Words like \u0026ldquo;pandemic,\u0026rdquo; \u0026ldquo;covid,\u0026rdquo; \u0026ldquo;stay,\u0026rdquo; \u0026ldquo;travel,\u0026rdquo; \u0026ldquo;safe,\u0026rdquo; \u0026ldquo;clean,\u0026rdquo; and \u0026ldquo;hygiene\u0026rdquo; indicate that guests are concerned with health and safety. In this unique period, they seek accommodations with strict cleaning protocols, spacious living spaces, and convenient geographical locations to accommodate potential long-term stays, home quarantines, or remote work. Guests expect a positive experience through friendly interactions with hosts and accommodations that meet their expectations.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Topic distribution analysis\u003c/h2\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e shows the distribution of topics between positive and negative sentiment reviews of Airbnb during this period, which provides insights into the factors that significantly influenced guest satisfaction and dissatisfaction. Key findings from the figure show that topics such as \u0026ldquo;Flexible booking during COVID-19\u0026rdquo;, \u0026ldquo;Cleanliness during COVID-19\u0026rdquo;, and \u0026ldquo;Contactless service\u0026rdquo; are predominantly associated with positive reviews. This suggests that during the pandemic, guests greatly appreciated hosts who effectively adapted to the new challenges by providing flexible booking options, ensuring high standards of cleanliness, and minimizing physical contact, which likely contributed to their sense of safety and well-being. The positive sentiment around \u0026ldquo;Neighborhood exploration\u0026rdquo;, \u0026ldquo;Family-friendly accommodation\u0026rdquo;, \u0026ldquo;Nature and outdoor experiences\u0026rdquo;, \u0026ldquo;Home-like experience\u0026rdquo;, and \u0026ldquo;Responsive communication\u0026rdquo; further indicates the importance of comfort, safety, and enriching local experiences. Guests valuing these aspects indicate that the ability to explore safe, family-friendly, and natural environments significantly contributed to their positive experiences. Conversely, topics on the right side of the distribution, such as \u0026ldquo;Dirty room\u0026rdquo;, \u0026ldquo;Booking and refund\u0026rdquo;, and \u0026ldquo;Security issues\u0026rdquo;, are predominantly linked with negative reviews. This suggests that inadequate cleanliness, difficulties in dealing with booking changes or cancellations, and perceived security issues were critical factors that led to guest dissatisfaction during the pandemic. The prominence of \u0026ldquo;Dirty room\u0026rdquo; as a negative sentiment topic highlights the heightened sensitivity towards hygiene and cleanliness during the COVID-19 period. \u0026ldquo;Booking and refund\u0026rdquo; issues likely reflect the frustration over the need for flexibility and understanding during uncertain times. \u0026ldquo;Security issues\u0026rdquo; further underlined guests\u0026rsquo; concerns about the safety of their accommodation, which, if not adequately addressed, could significantly detract from their overall experience.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e4.4 Topic correlation analysis\u003c/h2\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e shows the correlation network among key review topics on Airbnb during the COVID-19 pandemic, which reveals several salient patterns. \u0026ldquo;Cleanliness during COVID-19\u0026rdquo; demonstrates strong correlations with multiple topics such as \u0026ldquo;Safety during COVID-19\u0026rdquo;, \u0026ldquo;Provision of essentials and supplies\u0026rdquo;, \u0026ldquo;Pandemic hospitality\u0026rdquo;, and \u0026ldquo;Responsive communication\u0026rdquo;. This suggests that heightened cleanliness expectations are closely tied to broader safety concerns and reliability in communication during the pandemic. The cause-effect relationship here is likely driven by increased health anxieties, prompting guests to value and review hosts\u0026rsquo; adherence to hygiene protocols and their ability to communicate effectively about these practices. \u0026ldquo;Flexible booking during COVID-19\u0026rdquo; and \u0026ldquo;Booking and refund\u0026rdquo; are moderately linked with \u0026ldquo;Responsive communication\u0026rdquo;. This reveals the importance of adaptability and solid communication in alleviating uncertainties related to travel plans during the pandemic. The relationship here can be attributed to the dynamic nature of travel restrictions and guests seeking assurances that their bookings could be adjusted or refunded as necessary due to varying COVID-19 circumstances.\u003c/p\u003e \u003cp\u003e\u0026ldquo;Home-like experience\u0026rdquo; shows connections with \u0026ldquo;Neighborhood exploration\u0026rdquo;, \u0026ldquo;Nature and outdoor experiences\u0026rdquo;, and \u0026ldquo;Family-friendly accommodation\u0026rdquo;. These correlations indicate that during the pandemic, guests increasingly sought accommodations that provided a comfortable and homely environment and allowed for safe activities. This shift can be explained by the prolonged periods of confinement during lockdowns, which made travelers prioritize comfort and the ability to engage in activities within the local vicinity. \u0026ldquo;Extended stays during COVID-19\u0026rdquo; correlates with \u0026ldquo;Internet connectivity\u0026rdquo;, \u0026ldquo;Property maintenance\u0026rdquo;, and \u0026ldquo;Contactless service\u0026rdquo;. This pattern reflects the trend wherein longer stays, often for remote work purposes during the pandemic, increased guests\u0026rsquo; dependence on stable internet connectivity and well-maintained properties, alongside a preference for minimization of direct contact. The cause-effect relationship here is likely due to the rise of remote work and extended digital nomadism during the pandemic, driving demand for reliable internet and amenities that support longer stays. Lastly, issues like \u0026ldquo;Parking \u0026amp; noise concerns\u0026rdquo; and \u0026ldquo;Security issues\u0026rdquo; exhibit weaker correlations, suggesting these factors remained relevant but secondary to cleanliness, communication, and adaptability emphasis during the pandemic.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e4.5 Topic trend analysis\u003c/h2\u003e \u003cp\u003eBased on Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e, we found that several common attributes directly linked to COVID-19 have shown significant trends in the Airbnb market, reflecting the evolving expectations and behaviors of users during and after the pandemic. Among these, topics like \u0026ldquo;contactless service,\u0026rdquo; \u0026ldquo;cleanliness during COVID-19,\u0026rdquo; and \u0026ldquo;extended stays during COVID-19\u0026rdquo; have exhibited growing prominence, shaping the future of Airbnb services. One of the notable shifts has been the increasing popularity of \u0026ldquo;contactless service.\u0026rdquo; Initially driven by health concerns, this topic steadily gained traction during the pandemic as users appreciated the safety and convenience it provided. Even after the pandemic subsided, the demand for contactless service has persisted, now primarily fueled by its perceived convenience rather than solely by safety concerns. This evolution highlights how a pandemic-era innovation has transitioned into a standard expectation, with Airbnb users continuing to favor self-service options that reduce the need for direct interaction while streamlining their experience. Similarly, \u0026ldquo;cleanliness during COVID-19\u0026rdquo; has remained a central focus for both Airbnb hosts and users, even beyond the formal end of the pandemic. During the height of COVID-19, stringent hygiene protocols were introduced in response to health regulations, embedding cleanliness as a standard procedure across the platform. However, our topic modeling analysis indicates that the pandemic has elevated user perceptions of cleanliness to encompass not just physical tidiness but also disinfection and sanitization practices. This shift suggests that cleanliness, now intertwined with safety concerns, may have a lasting impact on guest behavior, as users continue to prioritize hygiene standards in their evaluations of accommodations. This focus on room cleanliness is further supported by the prevalence of the \u0026ldquo;dirty room\u0026rdquo; topic in our analysis, which remains a major source of dissatisfaction, underscoring the heightened emphasis on maintaining a hygienic environment.\u003c/p\u003e \u003cp\u003eThe pandemic also fostered a notable increase in \u0026ldquo;extended stays,\u0026rdquo; a trend that has persisted in the post-pandemic landscape. Many users shifted towards longer-term trips during COVID-19, likely driven by the flexibility of remote work and the desire for more isolated, stable environments. Our findings indicate that this shift has continued, with more guests opting for extended stays in Airbnb accommodations, marking a sustained change in travel preferences that could reshape the future landscape of the platform. \u0026ldquo;Provision of essentials and supplies\u0026rdquo; has demonstrated remarkable stability throughout the pandemic, though there have been notable shifts in the types of items appreciated by users. During COVID-19, guests valued essentials like grocery items, sanitizers, and personal protective equipment, which were crucial to their sense of safety. Post-pandemic, however, the focus has shifted towards amenities more commonly associated with traditional hotels, such as fresh linens, toiletries, and room service. Despite this shift, self-protection items like hand sanitizers and disinfectant wipes continue to be highly valued, reflecting lingering health concerns and a cautious approach to travel. This evolution suggests that while user expectations are blending the comforts of home with the conveniences of hotel-like services, health and hygiene remain at the forefront of their concerns.\u003c/p\u003e \u003cp\u003eAnother topic that has exhibited interesting dynamics is \u0026ldquo;responsive communication.\u0026rdquo; During the height of the pandemic, the rise of contactless services reduced the need for direct communication between guests and hosts, resulting in a decline in the emphasis on responsiveness. However, since June 2023, there has been a resurgence in the importance of responsive communication, suggesting that as travel normalizes, guests are increasingly seeking a more personal touch alongside the convenience of self-service options. This shift reflects a growing demand for a balance between the efficiency of contactless services and the reassurance provided by effective communication with hosts. \u0026ldquo;Proximity to public transportation\u0026rdquo; saw significantly less emphasis during the early stages of COVID-19 as health concerns led travelers to favor private transportation or accommodations in less populated areas. As the pandemic waned, however, interest in proximity to public transportation gradually increased, coinciding with a broader societal return to normalcy and an easing of health-related fears. Nevertheless, our analysis reveals that despite the uptick in interest, public transportation has not regained its pre-pandemic levels of importance. Instead, many travelers continue to favor alternatives such as ridesharing services and private vehicle rentals, suggesting a lasting shift in transportation preferences shaped by the pandemic. This ongoing trend indicates that flexibility, convenience, and perceived safety have become more influential factors in travelers\u0026rsquo; decisions, requiring both hosts and transport providers to adapt to these evolving expectations.\u003c/p\u003e \u003cp\u003eFinally, the topics of \u0026ldquo;Flexible booking during COVID-19\u0026rdquo; and \u0026ldquo;Booking and refund\u0026rdquo; reveal a lasting pattern that reflects the pandemic\u0026rsquo;s long-term impact on traveler behavior. Prior to 2021, these topics were frequently discussed as users sought reassurance amidst the uncertainties of rapidly changing travel restrictions and health guidelines. Flexible booking options became crucial during this period, offering much-needed security for travelers. Even as the pandemic has subsided, flexible booking policies have remained a stable presence in user discussions, indicating that many now perceive these policies as standard practice. However, there appears to be a gap between the lingering demand for flexibility and the level of accommodation currently provided by hosts. While Airbnb still offers flexible options, they do not always match the extensive policies implemented during the height of COVID-19, leading to some misalignment between user expectations and available offerings.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"5. Conclusions and implications","content":"\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e5.1 Conclusion\u003c/h2\u003e \u003cp\u003eThis study examines the shifts in Airbnb users\u0026rsquo; evaluation of accommodation services during and after the COVID-19 pandemic. Our findings reveal that the pandemic led to a clear shift among Airbnb users from prioritizing hedonic values to emphasizing personal safety. This change has established lasting effects on how users evaluate the relative importance of accommodation service attributes and their overall expectations.\u003c/p\u003e \u003cp\u003eAlthough many common attributes that have been reported previously were identified in this study, we found that the value assigned to these attributes varied significantly from previous research. For instance, location-related factors, which were once highlighted primarily for their convenience and accessibility (Ding et al., 2020; Guttentag et al., 2018), are also valued for the sense of separation and independence they provide from the outside world (Wong et al., 2023). This shift shows a broader trend in consumer behavior where safety and wellness have become important considerations in travel and accommodation choices (Kim et al., 2022). As noted in previous research, consumers\u0026rsquo; perceptions of value are heavily influenced by their immediate context (Kwortnik \u0026amp; Thompson, 2009), and the pandemic has dramatically altered this context. Given these shifts, it is crucial to reevaluate the perceived value of accommodation attributes in light of significant changes in the external environment.\u003c/p\u003e \u003cp\u003eThe lasting impact of COVID-19 is particularly evident in the evaluation of cleanliness. Currently, Airbnb users expect not only that hosts maintain the physical appearance and condition of their properties but also that they implement safety measures to mitigate health risks. In fact, the expectation extends to daily supplies, with guests desiring health products that exceed their previous expectations. This transformation reflects how COVID-19 has changed certain guest behavior over the long term (Watson \u0026amp; Popescu, 2021). The heightened focus on hygiene aligns with existing research that highlights the significance of perceived safety in influencing guest satisfaction and loyalty during crises (Paulose \u0026amp; Shakeel, 2022).\u003c/p\u003e \u003cp\u003eThe COVID-19 pandemic has reshaped perceptions of cleanliness within the Airbnb ecosystem. This shift extends far beyond traditional notions of tidiness, transforming cleanliness into a visible, dynamic metric of trust between hosts and guests. In the post-pandemic period, cleanliness is no longer an implied expectation, but an active expression of care and responsibility. Airbnb users now anticipate safety measures, including the provision of disinfectants and hygiene products, signaling a significant elevation of standards in guest experience. This study conceptualizes cleanliness not merely as an operational criterion but as a relational tool. It serves to negotiate perceived safety and reassure travelers in an era of uncertainty. Furthermore, this research challenges the notion that crises only temporarily influence guest behavior. By illustrating how Airbnb users have integrated health-focused cleanliness into their long-term evaluations, the findings reveal a long-term restructuring of hospitality norms. This aligns with Paulose and Shakeel\u0026rsquo;s (2022) assertion that perceived safety is central to guest satisfaction during crises. However, it also broadens the discourse by suggesting that cleanliness, as redefined in the post-COVID era, now functions as an indicator of qualities increasingly valued by guests.\u003c/p\u003e \u003cp\u003eTemporary policies implemented during COVID-19 have led to lasting changes in Airbnb users\u0026rsquo; expectations, particularly regarding booking flexibility and refund policies. During the pandemic, many travelers faced significant disruptions that necessitated more accommodating booking arrangements. As a response, Airbnb hosts adapted their policies to offer greater flexibility, such as allowing last-minute cancellations and offering full refunds under certain conditions. This demand for flexible booking options, prominently highlighted in guest reviews, signifies a shift toward guest-centric practices that address the uncertainties of post-pandemic travel. Travelers now prioritize flexibility as a key factor when choosing accommodations (Nicolau et al., 2024). This shift suggests that hosts will need to develop policies that not only meet current expectations but are also sustainable in the long-term. The expectation for adaptability is crucial for competitive differentiation in the hospitality industry (Buhalis \u0026amp; Leung, 2018).\u003c/p\u003e \u003cp\u003eThe lasting effects of the COVID-19 pandemic also include certain solutions provided by Airbnb hosts that cater to the changing preferences of guests. One such solution is the implementation of contactless services, which became a norm during the pandemic and continues to be widely adopted in 2024. Many Airbnb users still express a preference for these types of services, reflecting the enduring impact of COVID-19 on guest expectations. This shift towards contactless experiences resonates with research emphasizing the role of digital solutions in enhancing guest experiences (Maitra, 2021). The adoption of contactless services not only addresses health and safety concerns but also aligns with the growing demand for convenient and streamlined experiences facilitated by technology (Yağmur et al., 2024). The pandemic has accelerated the integration of digital solutions into the hospitality industry, with guests becoming increasingly accustomed to features such as mobile check-in, keyless entry, and virtual concierge services (Ludin et al., 2022).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e5.2 Theoretical Implications\u003c/h2\u003e \u003cp\u003eThis study contributes to the existing hospitality literature on guest behavior in several important ways. First, by analyzing Airbnb user reviews during and more than one year after the COVID-19 pandemic, we illuminate the attributes that have become increasingly vital to guests in short-term rentals. Specifically, this research identifies safety measures, flexible policies, and enhanced service attributes emphasized during COVID-19 as essential elements that reflect the pandemic\u0026rsquo;s lasting impact on guest expectations. By integrating these findings into the existing hospitality literature, we enrich the theoretical framework concerning guest expectations in non-traditional lodging contexts. The findings highlight how the pandemic has reshaped guest priorities, necessitating a reevaluation of service standards and operational practices within the industry. This aligns with the principles of SET, which posits that customers evaluate their experiences based on perceived benefits and costs. In this context, the heightened emphasis on safety and flexibility can be viewed as a response to guests\u0026rsquo; evolving expectations, where the perceived benefits\u0026mdash;such as health security and reassurance\u0026mdash;outweigh the costs associated with choosing specific accommodations. Thus, our research not only addresses a gap in the literature but also sets a foundation for future studies exploring how shifts in expectations can ultimately influence guest satisfaction in lodging environments.\u003c/p\u003e \u003cp\u003eMoreover, these findings confirm the relevance of established attributes (Ding, 2020; Teixeira, 2023; Xu, 2020) while simultaneously introducing new dimensions that have emerged due to the global health crisis. Second, by analyzing Airbnb user reviews from mid-2020 to mid-2024, this study provides a dynamic perspective on how the importance of various attributes has evolved throughout the pandemic. This provides valuable insights into which aspects of the Airbnb experience have gained or lost significance as the pandemic progressed and its effects persisted. By exploring changes in guest preferences over time, this research contributes to our understanding of guest behaviors in uncertain conditions, adding depth to ongoing discussions in the literature about the socio-economic impacts of pandemics (Smart et al., 2021; Yang \u0026amp; Roehl, 2024).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e5.3 Practical Implications\u003c/h2\u003e \u003cp\u003eThe findings of this study have several implications for practitioners within the Airbnb sector, offering insights that can support the sustainable growth of P2P businesses in the accommodation sector. First, it is crucial for Airbnb hosts to prioritize cleanliness and sanitation protocols to meet rising guest expectations despite the ending of COVID-19. This study indicates that guests have shown a strong preference for accommodations with established health measures, implementing comprehensive cleaning practices and following guidelines from health organizations will be vital. Hosts are encouraged to communicate these protocols clearly in their listings to build trust and foster positive reviews. Second, this shift highlights the importance for Airbnb hosts to promote their attributes from multiple perspectives to effectively meet the evolving needs of guests. When advertising their listings, hosts should focus on the specific values most favored by potential guests rather than relying on overly general expectations. This means clearly communicating features such as enhanced cleanliness protocols, flexible booking options, and contactless services. Moreover, providers should consider segmenting their marketing strategies to address the diverse preferences of different guest demographics. For instance, families may prioritize amenities like kitchen facilities and spacious accommodations, while young travelers may value proximity to nightlife and local attractions. By tailoring their messaging to highlight the unique benefits that align with guests\u0026rsquo; specific desires, hosts can better capture attention and engagement, ultimately leading to a more personalized booking experience.\u003c/p\u003e \u003cp\u003eThird, the importance of flexibility in booking policies should be emphasized. This study revealed a strong preference among guests for the ability to easily modify or cancel their reservations in post COVID-19. To cater to this demand, hosts should consider adopting more accommodating cancellation policies and clearly communicating these options in their listings. Additionally, hosts should provide explanations when they are unable to offer the same level of flexibility as before, helping guests understand the constraints while maintaining a positive experience. Fourth, effective communication remains critical in managing guest expectations and satisfaction. Hosts are encouraged to maintain open lines of communication with potential and current guests regarding any changes to policies, services, or available amenities. Prompt responses to inquiries and proactive updates can significantly enhance the guest experience and mitigate potential dissatisfaction related to changes influenced by the pandemic. Fifth, the enhancement of home-like features within properties can cater to the sustained guest preference for comfort and familiarity. Airbnb hosts may benefit from investing in amenities that enhance the home experience. This can cater to longer stays as travelers seek the comforts of \u0026ldquo;home away from home.\u0026rdquo; Finally, these findings hold implications for policymakers and the broader research community. Airbnb management should consider supporting initiatives that enhance safety in short-term rental properties, ensuring clear guidelines are established and communicated to both hosts and guests.\u003c/p\u003e \u003c/div\u003e"},{"header":"6. Limitations and future research","content":"\u003cp\u003eSeveral limitations of this study need to be addressed. First, this study only focused on Airbnb reviews written in English, which may overlook important perspectives from non-English-speaking users. Future research might conduct comparative studies that include reviews in multiple languages to provide cross-cultural insights into Airbnb user behavior during the COVID-19 pandemic. Second, this study analyzed user reviews from mid- 2020 to mid-2024, comprising the responses during the pandemic and the initial recovery phase that followed. However, this timeframe may not fully reflect long-term changes in guest behavior as the situation continues to evolve. Considering that COVID cases are still happening widely (Colarossi, 2024), future studies should extend this analysis beyond the peak of the pandemic to explore whether the shifts in guest preferences observed during this period are temporary or indicative of lasting changes in the industry. Third, a notable limitation of the topic modeling approach lies in its reliance on the quality of the input data and the chosen model parameters. Despite thorough pre-processing, some level of noise that can affect the results is often unavoidable. As a result, the generated topics may occasionally fail to accurately capture meaningful themes or may miss important guest insights. Future research could mitigate this limitation by validating STM results with other qualitative methods, such as in-depth interviews, to enhance the analysis.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eKai Ding, Le Li, and Rongteng (Renata) Zhang wrote the main manuscript text and Yuhua Chen prepared figures. All authors reviewed the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eTaulli, T. (2020). How Airbnb Beat The Covid-19 Virus. Forbes, November 21, 2020. Retrieved 10 September 2024 from: https://www.forbes.com/sites/tomtaulli/2020/11/21/how-airbnb-beat-the-covid-19-virus/\u003c/li\u003e\n\u003cli\u003eAbril, D. (2020). Airbnb\u0026rsquo;s IPO filing reveals huge COVID impact. Fortune. November 17, 2020. 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Retrieved 11 September 2024 from: https://www.who.int/westernpacific/health-topics/coronavirus.\u003c/li\u003e\n\u003cli\u003eWu, X., Han, X., \u0026amp; Moon, H. (2023). Host-guest interactions in peer-to-peer accommodation: Scale development and its influence on guests\u0026rsquo; value co-creation behaviors. \u003cem\u003eInternational Journal of Hospitality Management\u003c/em\u003e, 110, 103447.\u003c/li\u003e\n\u003cli\u003eXiang, D., Jiao, G., Sun, B., Peng, C., \u0026amp; Ran, Y. (2022). Prosumer-to-customer exchange in the sharing economy: Evidence from the P2P accommodation context. \u003cem\u003eJournal of Business Research\u003c/em\u003e, 145, 426-441.\u003c/li\u003e\n\u003cli\u003eXie, K. L., \u0026amp; Kwok, L. (2017). The effects of Airbnb\u0026rsquo;s price positioning on hotel performance. \u003cem\u003eInternational Journal of Hospitality Management\u003c/em\u003e, 67, 174-184.\u003c/li\u003e\n\u003cli\u003eXu, X. (2020). 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Understanding factors associated with cleanliness importance of hotel guests. \u003cem\u003eInternational Journal of Contemporary Hospitality Management\u003c/em\u003e, \u003cem\u003e36\u003c/em\u003e(1), 239-258.\u003c/li\u003e\n\u003cli\u003eZhang, M., Geng, R., Huang, Y., \u0026amp; Ren, S. (2021). Terminator or accelerator? Lessons from the peer-to-peer accommodation hosts in China in responses to COVID-19. \u003cem\u003eInternational Journal of Hospitality Management\u003c/em\u003e, \u003cem\u003e92\u003c/em\u003e, 102760.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"humanities-and-social-sciences-communications","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"palcomms","sideBox":"Learn more about [Humanities \u0026 Social Sciences Communications](http://www.nature.com/palcomms/)","snPcode":"41599","submissionUrl":"https://submission.springernature.com/new-submission/41599/3","title":"Humanities and Social Sciences Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Text analytics, Longitudinal research, Airbnb reviews, Structural topic modeling, Guest priority, COVID-19","lastPublishedDoi":"10.21203/rs.3.rs-5660543/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5660543/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eA key challenge for the peer-to-peer (P2P) accommodation industry is keeping pace with the evolving expectations and behavior of guests over time, shaped by diverse experiences and shifting preferences. This study utilizes advanced text analytics to examine the lasting impact of COVID-19 on Airbnb users\u0026rsquo; priorities regarding accommodation attributes, both during the pandemic and in the year that followed. Employing a longitudinal research design, we analyze a dataset of 461,509 reviews from 18,465 listed properties across four major cities in different countries (i.e., the United Kingdom, the United States, Spain) known for their Airbnb presence. Our findings highlight that the most significant and enduring impact of the pandemic on guest behavior is an increased prioritization of health-related features. Although certain attributes that were previously valued remain relevant, there has been a marked transition in user perceptions; specifically, hedonic and aesthetic values have diminished in importance relative to health-centric considerations. Furthermore, the policies and practices adopted during the pandemic reveal additional dimensions of its lasting influence, shaping guest expectations and preferences. Noteworthy among these are enhanced booking and cancellation flexibility, as well as the implementation of contactless services and the provision of protective equipment. This research contributes to understanding how crises can reshape guest priorities within the context of sharing economy accommodations, offering valuable insights for both academic researchers and practitioners.\u003c/p\u003e","manuscriptTitle":"From Crisis to Change: Analyzing the Lasting Influence of COVID-19 on Airbnb Users through Structural Topic Modeling","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-01-13 10:56:54","doi":"10.21203/rs.3.rs-5660543/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-02-06T12:13:07+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-02-04T20:11:37+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-01-25T04:34:30+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"240788230521650440963960929506930880216","date":"2025-01-18T04:34:00+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"257348858846433372959800214811811507869","date":"2025-01-14T20:15:58+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-01-14T12:37:13+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-01-09T15:00:07+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-01-09T14:55:43+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-01-09T14:55:35+00:00","index":"","fulltext":""},{"type":"submitted","content":"Humanities and Social Sciences Communications","date":"2024-12-17T09:47:08+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"humanities-and-social-sciences-communications","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"palcomms","sideBox":"Learn more about [Humanities \u0026 Social Sciences Communications](http://www.nature.com/palcomms/)","snPcode":"41599","submissionUrl":"https://submission.springernature.com/new-submission/41599/3","title":"Humanities and Social Sciences Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"291663e8-dfaf-48b5-bb1f-434fe12a2245","owner":[],"postedDate":"January 13th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":42648451,"name":"Business and commerce/Business and management"},{"id":42648452,"name":"Business and commerce/Information systems and information technology"},{"id":42648453,"name":"Social science/Business and management"}],"tags":[],"updatedAt":"2025-06-09T16:09:45+00:00","versionOfRecord":{"articleIdentity":"rs-5660543","link":"https://doi.org/10.1057/s41599-025-05153-8","journal":{"identity":"humanities-and-social-sciences-communications","isVorOnly":false,"title":"Humanities and Social Sciences Communications"},"publishedOn":"2025-06-08 15:57:32","publishedOnDateReadable":"June 8th, 2025"},"versionCreatedAt":"2025-01-13 10:56:54","video":"","vorDoi":"10.1057/s41599-025-05153-8","vorDoiUrl":"https://doi.org/10.1057/s41599-025-05153-8","workflowStages":[]},"version":"v1","identity":"rs-5660543","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5660543","identity":"rs-5660543","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}