The emotional geography of national anthems

The emotional geography of national anthems | 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 The emotional geography of national anthems Petri Toiviainen, Martín Hartmann, Friederike Koehler This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6318443/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 02 Jul, 2025 Read the published version in Scientific Reports → Version 1 posted 14 You are reading this latest preprint version Abstract National anthems serve as powerful symbols of national identity, often evoking strong emotional responses. While prior research has examined anthem lyrics, the emotional content of their musical features remains underexplored. This study employs computational modeling to predict the perceived emotional characteristics of 176 national anthems and investigates geographical and cultural variations. Using perceptual data from a prior study and musical features extracted with the MIR Toolbox, we trained LASSO regression models to predict eight emotional characteristics: Valence, Energy Arousal, Tension Arousal, Happiness, Sadness, Tenderness, Anger, and Fear. The predicted emotions were analyzed for continental differences, correlated with latitude and longitude, and compared to Hofstede’s cultural dimensions. The results revealed significant geographic trends, with Valence lower in the Americas and Energy Arousal higher near the equator. Fear and Tension Arousal were more pronounced in the Americas, while Happiness was highest in Oceania. Cultural analyses indicated that hierarchical societies exhibited more energetic anthems, individualistic cultures had less tense but more tender anthems, and indulgent societies expressed greater Fear. These findings highlight the role of musical features in shaping anthem emotions and underscore the potential of computational approaches for large-scale music-emotion research. Biological sciences/Psychology/Human behaviour Physical sciences/Mathematics and computing/Computer science National Anthems Music and Emotion Computational Music Analysis Geographical Patterns Cultural Dimensions Music Information Retrieval (MIR) Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1 INTRODUCTION National anthems represent an important national symbol 1 . Since the formation of nation-states, political leaders have consistently created and adopted national symbols (e.g., flags, anthems, mottos, currencies, constitutions, and holidays) to build and maintain national identity among the country’s population 2 . National anthems serve as official patriotic symbols (often seen as a musical counterpart of the flag), reflecting the nation’s identity and character including its mood, aspirations and goals as defined by leaders 3 . Their main function is to express a nation’s internal unity (establishing bonds and collective goals) and external uniqueness (distinguishing and confirming boundaries) 3 . Government elites thus strive to widely disseminate the national anthem among the population (e.g., incorporating the anthem into school curricula or official events and ceremonies) to legitimate formal authority (also termed banal nationalism 4 ). National anthems create a collective sentiment, which can be particularly observed in sport contexts, activating social identity and bonding. For instance, football teams that showed more passion during the singing of national anthems at UEFA Euro 2016 had a higher likelihood of success 5 . Correspondingly, music can convey national identity in two ways: from the inside-looking-in (sense of belonging and membership) and outside-looking-in (recognized by non-members) 6 . 1.1 Content and Music of National Anthems While the function of national anthems is similar across nations, their content and structure differs greatly among nations, representing different strategies to convey thoughts, emotions, messages, and goals 3 . A broad classification has been consolidated between honor anthems paying (religious) homage and revolutionary anthems 7 . Factors that influence the creation or adoption of an anthem are numerous, including a nation’s form of government, geographic location, socio-political events (e.g., wars, revolutions), economic aspects (e.g., world-system position, modernization) or the creative style during the anthem’s creation or adoption 1 . Most of previous research on national anthems, however, focused on sociological and linguistic analyses. For instance, a cross-cultural lyrics analysis identified diverse topics in national anthems, including ancestry and past, homeland, beauty, unity, victory, or freedom, showing weak correlations with societal features (e.g., age of country, gross domestic product) 8 . Further research found that the lyrical sentiment of national anthems varies by region: Latin and Mediterranean anthems are generally neutral, while Central and Western Asian, Germanic, and Slavic anthems have a more positive sentiment 9 . One content analysis even linked positive messages in anthems to lower suicide rates, while negative or conflicted themes were related to higher suicide rates 10 . However, a critical stance and controversies have emerged during the past decades regarding the use and content of national anthems promoting patriotism, propagandism and chauvinism 11 , especially in music education. Apart from the lyrical content, the musical structure of national anthems has mainly been investigated in the fields of historical musicology and ethnomusicology through case studies, for instance, on the anthem of Zimbabwe 12 . Earlier sociological work has linked symbolic musical codes in anthems with sociopolitical control and a nation’s world-system position at the time of the anthem’s creation or adoption 1 , 3 . However, a systematic and comprehensive investigation of objective musical features in a variety of anthems has received little academic attention yet, especially regarding the emotional content of the music. An earlier investigation of anthems of 18 European countries 13 revealed a positive association of the proportion of low notes in national anthems with students' perceptions of the anthems' gloominess and sadness as well as with national suicide rates. A recent initial study based on computational music analysis 14 points to possible links between certain musical characteristics (e.g., low pitch, high tempo, high beat) and positive social outcomes (e.g., high happiness and peace, low suicide rate). Although the main function of music lies in its potency to induce and affect emotions 15 , little is known about the emotions reflected in national anthems and potential underlying influencing factors, such as geographical location or cultural differences. 1.2 Music and Emotions The emotional characteristics of national anthems might be explained by a number of underlying factors. These include cultural movements, national tendencies and environmental factors such as geographic and climatic differences between countries. Before digging deeper into this issue—and particularly on the role of geography and cultural orientation upon emotions expressed by anthems, which is a central topic to this study—, it is necessary to introduce the main psychological and computational frameworks used in music and emotion research. Emotions in music can be experienced as a subjective response to music (i.e., felt or induced emotions) or be attributed to music (i.e., expressed or perceived emotions), although there might be a significant overlap in this classification 16 . The most prominent theoretical frameworks used in music and emotion research are discrete and dimensional models of emotions 17 . According to Ekman’s discrete or basic emotion model 18 , all emotions can be traced back to a small set of fundamental and inherent emotions (fear, anger, disgust, sadness, and happiness), with specific underlying neurophysiological systems. The two-dimensional circumplex model 19 , however, proposes that all emotions emerge from two independent fundamental dimensions, that is, valence (a pleasure–displeasure continuum) and arousal (activation–deactivation). Later work suggested an expansion into a three-dimensional model through dividing arousal into two separate dimensions: tension arousal and energy arousal 20 . Both main classes of models have been commonly applied to investigate emotions in music, while it has also been argued that it may not capture the complexity of emotions in an aesthetic context like music 21 . For instance, the basic emotion of disgust has often been modified to tenderness in the context of music research 17 . One of the main frameworks to explain music-evoked emotions (BRECVEMA) 22 suggests seven underlying mechanisms (brain stem reflex, rhythmic entrainment, evaluative conditioning, contagion, visual imagery, episodic memory, musical expectancy, and aesthetic judgment), with brain stem reflex, rhythmic entrainment, and musical expectancy being mostly dependent on the musical content. Apart from measuring emotions associated with music through directly asking individuals (self-reports), recent innovative approaches have included music emotion recognition (MER), that is, the computational task of automatically recognizing emotional content in music or emotions induced by music. MER is a high-level problem within the field of music information retrieval (MIR), an interdisciplinary area focusing on understanding and organizing music collections using computational techniques. MER has several applications, such as automatically categorizing music pieces based on emotions or recommending music tailored to a user’s mood 23 . MER systems can be built upon musical content and/or context, and include user factors such as demographic or situational information 24 . In a typical MER framework, signal processing techniques are used to extract emotionally relevant features from music excerpts. Audio-based features representing musical dimensions such as loudness, timbre, rhythm and harmony have been extensively studied in MER 25 . These features are paired with ground truth data—human annotations that describe perceived or induced emotions. A machine learning model is then trained on part of this annotated data set to recognize patterns, and its performance is evaluated on the remaining data. While previous research on national anthems has predominantly focused on the lyrical content and less on the emotional content as reflected in the music itself, MER offers great potential for an objective and comprehensive analysis of the emotions expressed in national anthems, elucidating certain patterns and providing insights into cross-cultural comparisons. 1.3 Emotions in Anthems: Geography and Cultural Differences Some of the similarities and differences in the emotional characteristics of national anthems might be explained through identifying geographical patterns. Geographical location often shapes the cultural, historical, and social context of a nation and the development of its identity. For instance, the history of a broader region, including events like wars, revolutions, and independence movements, often occurs in reciprocity with nearby countries and might impact the content and emotional tone of national anthems. Understanding the geographical context might help to interpret these historical influences and how they are reflected in the music. Furthermore, the location of a country and its surrounding landscape and resources are prominent themes in national anthems 8 , potentially influencing the music as well. As a recent example, Saudi Arabia, the largest petroleum exporter, is reportedly rearranging its national anthem with a Western composer, potentially incorporating new influences that reflect both its identity as a resource-rich nation and its increasing Westernization amid economic diversification efforts 26 , 27 . Variations in national anthems might also be linked with differences in cultural values and behaviors. A widely used framework to understand cultural differences (Hofstede’s cultural dimensions theory) 28 , 29 proposes six main cultural dimensions: Power Distance (solutions to the basic issue of human inequality), Individualism vs. Collectivism (integration of individuals in primary group), Motivation towards Achievement and Success , formerly Masculinity vs. Femininity (preference for achievement or cooperation), Uncertainty Avoidance (stress facing an unknown future), Long-Term vs. Short-Term Orientation (focus on future, present, or past), and Indulgence vs. Restraint (regulation of human desires of enjoyment). These dimensions have been consistently used to examine important differences among nations in terms of political and economic systems, business and management practices, and other societal variations 30 . However, to the best of our knowledge, no study has investigated these dimensions regarding differences in national anthems yet. 1.4 Research objectives Based on the lack of research on the emotions reflected in the music of national anthems and their possible links with geographical location and cultural differences, the main research objective of the study is to provide an overview of the emotional geography of national anthems based on computational modeling. Specifically, it aims to: analyze the emotional content of national anthems based on their musical features using computational modeling examine geographical patterns in the emotional characteristics of national anthems, focusing on both continents and specific coordinates (latitude and longitude). compare the predictability of emotion dimensions (Valence, Energy Arousal, Tension Arousal) and basic emotions (Happiness, Sadness, Tenderness, Anger, Fear) from musical features. explore how emotional expressions in national anthems vary globally and reflect broader cultural differences (Hofstede’s cultural dimensions) 2 METHODS We used computational modeling because it provides scalability and efficiency, allowing the analysis of a large number of anthems (176 in this study) in a time-effective manner, which would be challenging and resource-intensive with perceptual data collection. Moreover, computational modeling allowed us to concentrate on how the contribution of specific musical elements—such as timbral, rhythmic, and tonal characteristics—to emotional expression varies globally without interference from factors like lyrical content, cultural context, patriotic sentiment, or political associations. 2.1 Material The material used in the study comprised instrumental recordings of national anthems, sourced from the comprehensive database National Anthems.info 31 . A deliberate choice was made to include only those anthems presented in authentic instrumental form, thereby excluding any renditions played using MIDI instruments to ensure the acoustic consistency and authenticity of the sample. As a result, a total of 176 anthems were selected for further analysis. This selection process involved choosing the most recent anthem for countries with a history of multiple anthems, aligning with our focus on contemporary national identity as reflected in these musical symbols. The final dataset exhibits a global representation, with anthems from Europe (43), Asia (40), Africa (50), the Americas (35), and Oceania (8), ensuring a broad cultural and geographical scope for the analysis. The average length of the anthem recordings was 83.7 seconds, ranging from 31.8 seconds (Estonia) to 270.0 seconds (Uruguay). The list of countries included in the study is detailed in Supplementary Table 1. 2.2 Emotion modelling As we were interested in how the contribution of specific musical elements—such as timbral, rhythmic, and tonal characteristics—to emotional expression varies globally, computational modeling allowed us to focus on these features without interference from factors like lyrical content, cultural context, patriotic sentiment, or political associations. To this end, we combined perceptual data from the emotion database of Eerola 32 , which includes musical excerpts with diverse emotional content, with musical features extracted by music information retrieval techniques. To ensure the models' generalizability, we applied cross-validation and regularization, minimizing overfitting and enhancing their applicability to unseen data. Finally, we used the resulting models to predict the perceived emotional content of the national anthems, linking musical characteristics with emotional perception in a data-driven and systematic manner. 2.2.1 Musical feature extraction The modelling of emotional content was grounded in the analysis of 360 film soundtrack excerpts, using the emotion database of Eerola 32 . This database comprises ratings of eight emotion characteristics provided by 116 participants. The emotion characteristics were based on two models: a dimensional model encompassing valence, energy arousal, and tension arousal, and a basic emotions model including happiness, sadness, tenderness, anger, and fear. Utilizing the MIR Toolbox 33 , we extracted 65 musical features, representing dynamic, rhythmic, spectral, timbral, and tonal content, from each movie soundtrack excerpt. The extracted features are shown in Fig. 1 . The analysis adopted a bag of frames approach, calculating both means and standard deviations of the features, thus capturing the dynamic range and variability within each excerpt. Features with a skewed distribution were Box-Cox transformed. 2.2.2 LASSO regression To link the extracted musical features to the emotion ratings, we employed least absolute shrinkage and selection operator (LASSO) regression 34 . This method was chosen for its effectiveness in handling multicollinearity and selecting relevant predictors in datasets with many variables. LASSO, or Least Absolute Shrinkage and Selection Operator, is an iterative regression technique that imposes a penalty on the absolute size of regression coefficients, effectively reducing less relevant predictors to zero and retaining only the most influential ones. This approach enhances the generalizability of the model by focusing on a parsimonious set of predictors that are robust across different datasets. We treated each emotion characteristic as a dependent variable in separate models and assessed the predictive power of the musical features through cross-validation, using an 80/20 random split into training and testing sets across 100 runs to ensure robust findings. We determined the optimal regularization parameter by minimizing the prediction error for the test data, based on the average model accuracy measured by the adjusted coefficient of determination across these runs. Finally, we retrained the models using the full dataset and the identified optimal regularization parameters to enhance predictive accuracy.. 2.3 Emotion prediction After training the eight LASSO models, we extracted the 60 musical features illustrated in Supplementary Fig. 1 for all 176 national anthems. Using these trained models, we predicted the eight perceived emotion characteristics for each anthem. 2.4 Emotions of anthems and geographical variation Using the predicted emotional characteristics, we subsequently analyzed their regional differences through a comparison between individual countries as well as across continents using one-way ANOVAs. We further examined global patterns by performing correlation analyses between the predicted emotional characteristics and geographical coordinates, including latitude and longitude. The geographical centroids of countries were sourced from the Climate Data Toolbox 35 . It is important to recognize the distinct characteristics of latitude and longitude as geographical variables. Latitude is linear and directly linked to the Earth's geography, representing the distance north or south from the equator. Accordingly, we used latitude values, \(\:\phi\:\) , as well as their absolute values, \(\:\left|\phi\:\right|\) , directly in the correlation analysis. Longitude, by contrast, is cyclical, reflecting the Earth's rotation and requiring periodic wrapping to ensure continuity. Additionally, the origin of longitude at the Greenwich Meridian is a cultural convention established during the International Meridian Conference of 1884, rather than a natural reference point. Consequently, for longitude ( \(\:\lambda\:\) ), we set its values to increase from west to east, applied the sine transformation, calculated as \(\:sin(\lambda\:-\delta\:)\) , and determined the value of the longitude offset, \(\:\stackrel{\sim}{\delta\:}\) , that maximized the absolute value of correlation. In other words, for each emotion variable, the reference meridian is shifted so as to optimize the strength of the relationship (regardless of its direction) between monotonically increasing longitude and the emotion variable. 2.5 Emotions of anthems and cultural dimensions To explore the influence of societal values and behaviours on the emotional content of national anthems, we conducted correlation analyses between the predicted emotional characteristics and Hofstede’s 28 , 29 six cultural dimensions. The availability of data varied across the six scales: PDI, IDV, MAS, and UAI included data for 66 countries, while LTO and IVR included data for 89 countries. The countries associated with each scale are listed in Supplementary Table 1. 3 RESULTS The prediction accuracies of the models used to describe the emotion characteristics of each anthem were moderate to moderately high, as shown in Supplementary Table 2. This table presents the regularization parameter and coefficient of determination for the optimal LASSO model corresponding to each emotion characteristic. Emotion dimensions (Valence, Energy Arousal, and Tension Arousal) were predicted with greater accuracy than basic emotions due to their broader and more generalized representation of emotional content. The model coefficients are illustrated in Supplementary Fig. 1. 3.1 Emotions of anthems per country 3.1.1 Emotion dimensions Figure 1 displays a scatter plot of the Valence and Energy Arousal of the 176 national anthems, as predicted by the respective LASSO models. As can be seen, most of the anthems are located in the quadrant characterized by positive Valence and high Energy Arousal, indicating that happiness is the predominant basic emotion they convey. Figure 2 presents Valence, Energy Arousal, and Tension Arousal of anthems displayed on a world map. In this and all subsequent maps, the values are scaled such that the minimum value is displayed as blue and the maximum value as red. While there is a great extent of local variation in the emotional content, some geographical trends can be observed. For instance, Valence tends to be more negative in the Americas compared to other regions. Energy Arousal appears to be higher in countries situated close to the equator, while many countries in Southern Africa and South Asia, as well as Australia exhibit lower Tension Arousal, indicating a calmer emotional tone in the anthems from these regions. Table 1 provides an overview of the countries with the three lowest and three highest values for each emotion dimension. Table 1 Anthems with lowest and highest values of each emotion dimension. Emotion dimension Lowest Highest Valence Pakistan, Tanzania, Malaysia Comoros, Jamaica, Mozambique Energy Arousal Jamaica, Kenya, Mozambique South Korea, China, Paraguay Tension Arousal Jamaica, Comoros, Nicaragua Malaysia, Pakistan, Indonesia 3.1.2 Basic emotions Figure 3 . Strength of Happiness, Tenderness, Sadness, Anger, and Fear of anthems per country. For each emotion, the values have been scaled to cover the entire range of colours. Similar to the emotion dimensions, there is a significant degree of local variation. We encourage the reader to explore the general trends for each emotion independently. Meanwhile, Table 2 provides an overview of the countries with the three lowest and three highest intensities for each emotion. Table 2 Anthems with lowest and highest strength of each basic emotion. Basic emotion Lowest Highest Happiness Israel, Liechtenstein, Jamaica Western Sahara, China, Dominica Tenderness Nigeria, Qatar, US Virgin Islands Japan, Cambodia, Netherlands Sadness Lebanon, China, Rwanda Japan, Israel, Liechtenstein Anger Cambodia, Japan, Netherlands Qatar, Sudan, Nigeria Fear Niger, Kenya, China Qatar, Jamaica, Liechtenstein 3.2 Emotions of anthems per continent To examine broader geographic patterns, we conducted statistical analyses to compare the emotional content across continents. 3.2.1 Emotion dimensions We conducted a series of one-way ANOVAs to examine differences in Valence, Energy Arousal, and Tension Arousal of anthems across continents. The results revealed significant continental differences for all three dimensions. Valence exhibited a significant effect of continent ( F (4, 170) = 5.87, p < .001), with means ranging from 4.39 to 5.00. Energy Arousal also showed significant differences ( F (4, 170) = 2.48, p = .046), with means between 4.54 and 4.93. Tension Arousal demonstrated a strong continental effect as well ( F (4, 170) = 4.65, p = .001), with means varying from 3.51 to 4.05. Next, we conducted post hoc tests using pairwise t -tests with false discovery rate (FDR) correction to account for multiple comparisons. Figure 4 presents violin plots for each of the emotion dimensions, illustrating the distribution of scores, and displays pairwise significant differences identified through FDR-adjusted q values (< .05). Most notably, Valence in the Americas was significantly more negative than in all other continents, reflecting a distinct emotional tone in anthems from this region. Additionally, Tension Arousal in the Americas was higher compared to Europe, Africa, and Oceania, suggesting that anthems from the Americas convey a heightened sense of urgency or intensity relative to those from these other continents. 3.2.2 Basic emotions For the basic emotions, the one-way ANOVAs revealed significant differences across continents for Fear ( F (4, 170) = 5.23, p < .001), Happiness ( F (4, 170) = 3.31, p = .012), Sadness ( F (4, 170) = 2.47, p = .047), and Tenderness ( F (4, 170) = 3.01, p = .020), while no significant differences were found for Anger ( F (4, 170) = 1.36, p = .250). Again, post hoc tests were conducted using pairwise t -tests with false discovery rate (FDR) correction to account for multiple comparisons. Figure 5 presents violin plots for each of the five basic emotions, illustrating the distribution of scores and highlighting pairwise significant differences identified through FDR-adjusted p values (< .05). It can be observed that Fear was significantly higher in the Americas than in all other continents, while Happiness was significantly higher in Oceania than in the Americas and Europe. 3.3 Emotions of anthems and location on the globe To gain a more detailed understanding of the relationship between the emotional content of anthems and geographical location, we conducted correlation analyses between the predicted perceived emotions of the anthems and the latitudes and longitudes of the geographical centroids of the respective countries. 3.3.1 Emotion dimensions Figure 6 .a summarizes the observed geographical patterns in the emotional dimensions, highlighting how these emotional characteristics vary along latitudinal and longitudinal gradients. The correlations between the emotion dimensions and the geographical coordinates of the countries, along with the optimal longitude offsets, are displayed in Table 3 . Table 3 Correlation coefficients between the intensity of the three emotion dimensions in the anthems and latitude ( \(\:{r}_{\phi\:}\) ), absolute value of latitude ( \(\:{r}_{\left|\phi\:\right|}\) ), and sine-transformed longitude ( \(\:{r}_{sin(\lambda\:-\stackrel{\sim}{\delta\:})}\) ) of the countries' geographical centroids, as well as the optimal longitude offset ( \(\:\stackrel{\sim}{\delta\:}\) ). **p < .01, ***p < .001, two-tailed. Emotion dimension \(\:{r}_{\phi\:}\) \(\:{r}_{\left|\phi\:\right|}\) \(\:{r}_{sin(\lambda\:-\stackrel{\sim}{\delta\:})}\) \(\:\stackrel{\sim}{\delta\:}\) Valence − .012 .051 .252*** -7° Energy arousal − .142 − .217** − .060 0° Tension arousal − .020 − .075 − .208** 1° For Valence, the significant positive correlation with sine-transformed longitude (r = .252, p < .001) and an optimal longitude offset of -7° suggests that Valence tends to increase when moving towards the east from the longitude 7°W, and vice versa. Energy Arousal, with a significant negative correlation to absolute latitude (r = − .217, p < .01), indicates that anthems from countries closer to the equator exhibit higher Energy Arousal. Finally, Tension Arousal, which correlates negatively with sine-transformed longitude (r = − .208, p < .01) at an optimal offset of 1°, implies a regional increase in Tension Arousal when moving towards the west from the longitude 1°. It is notable that the optimal longitude offset values are close to zero for all three emotion dimensions. 3.3.2 Basic emotions Figure 6 .b summarizes the observed geographical patterns in basic emotions. Table 4 shows the correlations between the strength of basic emotions and the geographical coordinates of the countries. Table 4 Correlation coefficients between the strength of basic emotions in the anthems and latitude ( \(\:{r}_{\phi\:}\) ), absolute value of latitude ( \(\:{r}_{\left|\phi\:\right|}\) ), and sine-transformed longitude ( \(\:{r}_{sin(\lambda\:-\stackrel{\sim}{\delta\:})}\) ) of the countries' geographical centroids, as well as the optimal longitude offset ( \(\:\stackrel{\sim}{\delta\:}\) ). *p < .05, **p < .01, ***p < .001, two-tailed. Basic emotion \(\:{r}_{\phi\:}\) \(\:{r}_{\left|\phi\:\right|}\) \(\:{r}_{sin(\lambda\:-\stackrel{\sim}{\delta\:})}\) \(\:\stackrel{\sim}{\delta\:}\) Happiness − .219** − .178* .065 -79° Tenderness .027 .114 .099 -32° Sadness .249*** .247*** .134* 33° Anger − .011 − .090 − .155* -11° Fear .017 .034 − .242*** 4° As can be seen, Happiness shows a significant negative correlation with latitude (r φ = − .219, p < .01) and absolute latitude (r |φ| = − .178, p < .05), suggesting that Happiness is stronger closer to the equator and in the south. Sadness, on the other hand, exhibits significant positive correlations with both latitude (r φ = .249, p < .001) and absolute latitude (r |φ| = .247, p < .001), indicating that higher levels of Sadness are associated with regions farther from the equator and in the north. Additionally, Sadness has a weaker, but significant, positive correlation with sine-transformed longitude ( \(\:{r}_{sin(\lambda\:-\stackrel{\sim}{\delta\:})}\) = .134, p < .05) with an optimal offset of 33°E, indicating that Sadness tends to increase slightly when moving eastward from this longitude. Both Fear ( \(\:{r}_{sin(\lambda\:-\stackrel{\sim}{\delta\:})}\) = − .242, p < .001) and Anger ( \(\:{r}_{sin(\lambda\:-\stackrel{\sim}{\delta\:})}\) = − .155, p < .05) show significant negative correlations with sine-transformed longitude, with optimal offsets \(\:\stackrel{\sim}{\delta\:}\:\) close to 0°, implying that these emotional qualities tend to become stronger when moving towards west from the zero meridian. By contrast, Tenderness does not exhibit significant correlations with any geographical coordinate, suggesting a lack of strong geographical patterns for this emotion. These findings indicate that Sadness and Happiness are primarily associated with latitude, while Fear and Anger display notable longitudinal patterns. 3.4 Anthem emotions and cultural dimensions Power Distance (PDI) showed a moderate positive correlation with Energy Arousal ( r (64) = .516, p < .001), indicating that countries with hierarchical structures, where unequal power distribution is more accepted, tend to have more energetic anthems. Individualism vs. Collectivism (IDV) exhibited negative correlations with both Energy Arousal ( r (64) = − .373, p < .05) and Tension Arousal ( r (64) = − .369, p < .05), suggesting that individualistic societies have less energetic and less tense anthems. Additionally, IDV correlated positively with Tenderness ( r (64) = .383, p < .05), implying that these societies favor anthems with gentler emotional tones. Finally, Indulgence vs. Restraint (IVR) correlated positively with Fear ( r (87) = .374, p < .01), indicating that indulgent societies, which emphasize enjoyment and gratification of desires, tend to have anthems expressing higher levels of fear. 4 DISCUSSION In this study, we explored the possible connections between the emotional content of instrumental renditions of 176 national anthems and their geographical location. To achieve this goal, we extracted a set of acoustic features describing expressed emotions by the anthems and three geodetic features based on latitude and longitude of the geometric center of the countries. We expected to observe geographical trends, assuming that climatic and biogeographic factors influence the emotional content of national anthems—just as they do with other national symbols like flags, coats of arms, and birds, as well as national identity components such as language, history, ancestry, culture, and cuisine. 4.1 Summary of results Most national anthems across the studied countries conveyed some degree of Happiness (Fig. 1 ). However, a cross-continental comparison revealed notable differences in emotional expression (Figs. 2 – 5 ). Valence was significantly more negative in the Americas than in other continents. Tension Arousal was also higher in the Americas compared to Europe, Africa, and Oceania. Additionally, Fear levels were significantly higher in the Americas than in all other regions, while Happiness was more pronounced in Oceania than in the Americas and Europe. Latitudinal trends revealed that Energy Arousal was stronger in countries closer to the equator. Happiness was also more prominent near the equator and in the southern regions, while Sadness was more prevalent farther from the equator and in the north. Longitudinally, Valence increased eastward, whereas Tension Arousal and Anger intensified westward. Similarly, Sadness rose in an eastward direction, whereas Fear increased toward the west. When examining emotions in relation to Hofstede’s cultural dimensions 28 , 29 , countries with hierarchical power structures tended to have more energetic anthems. In contrast, individualistic societies favored anthems that were less tense, less energetic, and more tender. Additionally, indulgent societies tended to have anthems that expressed higher levels of Fear. 4.2 General discussion One of the clearest results of this study was that anthems of American countries exhibit higher levels of Fear, greater Tension Arousal, and more negative Valence compared to those from other regions. Analyzing countries with extreme values for key predictors of these emotional features further supports this pattern. For instance, Chile’s and Nicaragua’s anthems feature frequent harmonic changes, Jamaica’s and Aruba’s have low pulse clarity, and Antigua and Barbuda’s anthem has high spectral energy in center frequencies, all of which are strong predictors of Fear. Anthems from Barbados and Dominica are very high in roughness, with their instrumentation dominated by brass and percussive elements, resulting in high Tension Arousal and low Valence. Brazil and Costa Rica exhibit very low key clarity, which refers to how clearly a musical piece establishes a tonal center, and corresponds to high Fear and Tension Arousal. While these musical features highlight the emotional qualities of anthems, their interpretation can be informed by the broader historical and cultural context. The native music of many of these nations is characterized by pentatonic scales, as seen in Andean cultures 36 , yet postcolonial anthems are heavily influenced by—or even composed by—European musicians of the 19th century. The rich rhythmic and tonal palette of Italian opera and other Romantic music might partly explain the emotional qualities of these anthems. At the same time, these musical choices reflect a decolonization process largely led by elites of mixed European and non-European ancestry, rather than by the broader colonized population 37 , 38 . In line with this interpretation, another study suggested that the language of an anthem—specifically Spanish—is a significant factor contributing to the bellicose nature of anthems 39 , which may also explain some of the heightened emotional intensity observed. Another key finding was that anthems from Oceania expressed higher levels of Happiness compared to those from the Americas and Europe. An examination based on mode, a key predictor of Happiness, shows that Fiji is the most major anthem in the dataset. It has also been observed that most Oceanic anthems are uplifting church hymns or patriotic songs from their current or former colonizing countries 31 , whereas European and American anthems are more diverse in that they can be persistently major (e.g., Germany, Bolivia) or have passages in minor mode (e.g., Argentina, France). However, this finding should be interpreted with caution due to the small sample size (8 countries) from Oceania. Regarding latitudinal trends, we found that anthems from countries closer to the equator exhibit higher levels of Happiness and Energy Arousal, along with lower levels of Sadness. Similarly, countries in southern regions tend to have higher Happiness and lower Sadness. The similarities in correlations between these emotions and both latitude and absolute latitude may stem from the uneven distribution of countries between the Northern and Southern Hemispheres. Examples of this relationship include, Niger, Libya, and Western Sahara, which display the highest pulse clarity in the dataset—a positive predictor of Happiness and Energy Arousal and a negative predictor of Sadness. Additionally, Zimbabwe's anthem scores exceptionally high in major mode, while New Zealand’s high spectral irregularity is associated with Happiness. Meanwhile, Brazil and Chile's frequent harmonic changes correspond to lower Sadness. Some of these regions, particularly those near the tropics, may benefit from more pleasant climates that enhance mood, encourage outdoor activities, and promote a more active, social lifestyle 40 . We found an Eastward increase in Valence and a decrease in Tension Arousal and Anger. This pattern is exemplified by the Japanese anthem, which, with its longer notes and minimal percussion, shows decreased spectral roughness (indicating high Valence and low Tension Arousal) and low spectral flux (linked to low Anger). Like Vanuatu's anthem, it also has very high key clarity (corresponding to high Valence and low Tension Arousal). Compared to the 19th-century Latin American epic anthems, more Eastern anthems 43 (from Europe, Asia, Africa, and Oceania) are more varied in age and are often better characterized as odes, marches, and fanfares, with higher key clarity and more consistent tempo. However, interpreting these results is challenging and requires further investigation. Our results show an eastward increase in Sadness. For instance, Eastern Hemisphere countries such as Japan and Cambodia head the list of countries having the lowest harmonic change, with this feature being a negative predictor of Sadness. Contributing to this result might be the use of anhemitonic musical scales in some East Asian anthems, such as the pentatonic scale in China and the yo scale in Japan, reflecting the traditional music of these countries 44 . The lack of half steps results in a more uncertain mode than the clearly major mode of other anthems. Also, Japan’s anthem ranks amongst the lowest in rhythmic clarity, a feature that contributes negatively to Sadness. It should be noted that these results have the lowest magnitude and do not align with other findings. Hofstede’s cultural dimensions can help further explain the connection between the emotions expressed in anthems and geographic coordinates. Specifically, countries with high Power Distance and collectivist tendencies tend to have more energetic anthems, reflecting a cultural emphasis on collective identity and hierarchical social structures. For instance, Ecuador and Panama—highly collectivistic and power-distant countries—feature anthems with clear rhythmic periodicity, frequent percussion, and high spectral entropy, flux, and brightness. These elements contribute to a more dynamic and forceful anthem, potentially reinforcing collective identity through energetic symbolism. Conversely, countries such as Denmark and New Zealand, which score high in Individualism and low in Power Distance, tend to have more subdued anthems. Their anthems are cantabile, with longer notes and less frequent percussion, creating a less energetic and tense atmosphere. These anthems could be seen as a reflection of individualistic values and egalitarian social structures, where personal autonomy is emphasized and hierarchical power distribution is minimized. It is worth noting that Power Distance is moderately negatively correlated with Individualism (r = -0.60, p < .001), linking to the concept of ‘vertical collectivism’ 45 . Another relevant cultural dimension was Indulgence, which significantly correlated with the expression of Fear in national anthems. More broadly, Indulgence is negatively linked with Long-Term Orientation (r=-.45, p < .001) and with the Human Development Index (r = -0.31, p < .001), suggesting that countries with higher indulgence scores tend to experience greater political, economic, or social instability 46 . This may help explain why nations such as Venezuela, Mexico, Puerto Rico, and El Salvador—where Indulgence and short-term orientation are high—also emphasize emotional intensity in their anthems. These anthems often feature more harmonic changes—a positive predictor of Fear—and increased chromaticism. In contrast, more stable countries like Sweden, which rank low in Indulgence, tend to have anthems with greater key clarity, which is a negative predictor of Fear. 4.3 Limitations While the study provides valuable insights into the emotional characteristics of national anthems, several limitations should be considered. First, our results should be interpreted merely as associations without implying any causality, and might be confounded by various unconsidered variables. Second, the study operates on the assumption that emotions modeled from musical features are universally perceived, which may overlook cultural differences in emotional interpretation, such as those found with regard to self-perceived well-being 47 or to the perception of mode in chords and melodies 48 . Additionally, the emotion modeling relies on ratings derived from a separate film soundtrack dataset, which may not perfectly align with the emotional characteristics of national anthems. Another limitation lies in the extracted musical features, as they may not capture all aspects of music that contribute to perceived emotions. Further, traditional Music Information Retrieval (MIR) techniques typically fail to describe certain musical elements, such as subtle variations in articulation, thematic development, gradual structural changes, and cross-modal associations. Apart from methodological limitations, cross-cultural comparisons face several challenges, including ethnocentrism of the researchers. The research team is composed predominantly of individuals who are White and influenced by Western academic traditions. We recognize that our cultural, academic, and socioeconomic backgrounds may shape our perspectives and interpretations, potentially introducing biases into our analysis. Further, with our results, we do not aim to make unwarranted overly simplified claims about specific nations or cultures contributing to stereotypes or prejudice. Especially the complexity of the concept of culture presents difficulties for cultural research, as culture is a multifaceted and nuanced construct with a constantly evolving nature. Accordingly, Hofstede’s theory has received criticism as well, regarding its overgeneralization and limited scope, static nature and Western bias 49 . Furthermore, the history, creation and adoption of national anthems vary greatly between countries, influenced by historical context, political elites, and cultural diversity within nations. Additionally, while this analysis focused on the emotions reflected in an important national symbol, national anthems do not necessarily reflect the population’s current (or former) sentiment and identification with their national anthems. For instance, a marginalized group in Israeli society appears to view the anthem more negatively compared to mainstream Israelis 50 . Further, singing the national anthem has sparked protests in some countries and criticisms related to (forced) patriotism 51 . 4.4 Implications and future directions Since the aim of this study was to uncover associations using computational modeling, it can serve as a starting point for future research. Based on our computational findings, further studies might now include the viewpoints of specific nations to integrate the voice of the culture and nation itself. For instance, the sentiment toward one’s own national anthem could vary substantially among its own population (ranging from nationalism and pride over indifference to discontent and rage). It might therefore be worthwhile to conduct studies based on self-report data about the sound and emotions directly evoked by the anthem. Further, experimental research could manipulate the musical features of anthems to examine their direct emotional impact. In addition, based on the cultural (and often historical) overlap and similarity within regions of different nations, it might be interesting to focus on the investigation of anthems in specific continents or regions. Furthermore, future research could further explore emotions expressed by national anthems by integrating musical analysis and lyric analysis through advanced computational methods such as latent semantic analysis (LSA), deep learning, and multimodal processing, allowing for deeper insights into the interplay between musical structure and lyrical semantics 52 , 53 . Cross-cultural perception studies could investigate how individuals from different cultural backgrounds interpret and emotionally respond to different musical anthems, considering factors such as familiarity with the music and sentiment toward the countries. Further, temporal and historical analysis could provide insights into how political, social, and cultural shifts might have influenced the emotional content of national anthems and other politically significant music, examining e.g. how harmonic choices and rhythmic structures relate to musical trends and historical events. Our study also has valuable implications for broader intercultural music research. While national anthems often evoke strong feelings of patriotism and unity, they can also be critiqued for promoting a nationalized worldview overshadowing global interconnectedness, and for legitimizing power and authority over a nation’s people. On the other hand, cultural music in general is deeply embedded in a nation's heritage, helping to preserve traditions and deepen a sense of community. Since the creation and adoption of national anthems are influenced by a variety of sociological, political and historical factors 3 , folk and traditional music might be more representative of the population itself in a nation. MER could therefore be used as a computational approach to examining and comparing musical differences in folk and traditional music as one possible reflection of a nation. Declarations AUTHOR CONTRIBUTIONS Petri Toiviainen: Conceptualization, Methodology, Software, Formal analysis, Investigation, Data Curation, Writing - Original Draft, Writing - Review & Editing, Visualization, Supervision, Project administration, Funding acquisition. Martín Hartmann: Conceptualization, Writing - Original Draft, Writing - Review & Editing, Visualization. Friederike Koehler: Conceptualization, Writing - Original Draft, Writing - Review & Editing. DATA AVAILABILITY The recordings of the national anthems were retrieved from https://nationalanthems.info/. Predicted emotional characteristics for each country’s anthem are available at https://osf.io/wz65t/files/osfstorage?view_only=77e7a80975774522879a67ad4b1cdfb4 Scripts used for the analysis are available from the first author upon request. FUNDING The research was funded by the Research Council of Finland’s Centre of Excellence Programme (project number 346210). COMPETING INTERESTS The authors declare no competing interests. References Cerulo, K. A. Symbols and the world system: National anthems and flags. Sociol. Forum . 8 , 243–271 (1993). Kolstø, P. National symbols as signs of unity and division. Ethn. Racial Stud. 29 , 676–701 (2006). Cerulo, K. A. Sociopolitical Control and the Structure of National Symbols: An Empirical Analysis of National Anthems. Soc. Forces . 68 , 76–99 (1989). Billig, M. Banal Nationalism (SAGE, 1995). Slater, M. J., Haslam, S. A. & Steffens, N. K. Singing it for ‘us’: Team passion displayed during national anthems is associated with subsequent success. Eur. J. Sport Sci. 18 , 541–549 (2018). Sanchez Adorno, S. Anthems in Middle Childhood: Negotiating Musical Experiences and National Identities. in Music, Words, and Nationalism: National Anthems and Songs in the Modern Era (eds Moreno-Luzón, J. & Nagore-Ferrer, M.) 229–253 (Springer International Publishing, Cham, doi: 10.1007/978-3-031-41644-6_11 . (2024). Nagore-Ferrer, M. National Anthems in the Nineteenth Century: Honour Anthems versus Revolutionary Anthems. in Music, Words, and Nationalism: National Anthems and Songs in the Modern Era (eds Moreno-Luzón, J. & Nagore-Ferrer, M.) 33–56 (Springer International Publishing, Cham, doi: 10.1007/978-3-031-41644-6_3 . (2024). Silaghi-Dumitrescu, R. Topics in National Anthems. J. Lang. Lit. 20 , 288 (2020). Silaghi-Dumitrescu, R. Trends in the texts of national anthems: A comparative study. Heliyon 9 , e19105 (2023). Voros, V. et al. Word Use and Content Analysis of the First Verses of Six National Anthems: A Transcultural Aspect of Suicidal Behaviour. Psychiatr Danub . 28 , 82–85 (2016). Kyridis, A. et al. Nationalism through state constructed national symbols. The case of the national anthems. Int. J. Interdiscip Soc. Sci. 4 , 1–14 (2009). Mutemererwa, S., Chamisa, V. & Chambwera, G. The national anthem: A mirror image of the Zimbabwean identity? Muziki 10 , 52–61 (2013). Lester, D. & Gunn, J. F. National anthems and suicide rates. Psychol. Rep. 108 , 43–44 (2011). Hasan, S. M. R. et al. Analyzing Musical Characteristics of National Anthems in Relation to Global Indices. Preprint at (2024). https://doi.org/10.48550/arXiv.2404.03606 Juslin, P. N. & Västfjäll, D. Emotional responses to music: the need to consider underlying mechanisms. Behav. Brain Sci. 31, 559–575; discussion 575–621 (2008). Evans, P. & Schubert, E. Relationships between expressed and felt emotions in music. Music Sci. 12 , 75–99 (2008). Eerola, T. & Vuoskoski, J. K. A comparison of the discrete and dimensional models of emotion in music. Psychol. Music . 39 , 18–49 (2011). Ekman, P. Are there basic emotions? Psychol. Rev. 99 , 550–553 (1992). Russell, J. A. A circumplex model of affect. J. Pers. Soc. Psychol. 39 , 1161–1178 (1980). Schimmack, U. & Grob, A. Dimensional models of core affect: A quantitative comparison by means of structural equation modeling. Eur. J. Personal . 14 , 325–345 (2000). Zentner, M., Grandjean, D. & Scherer, K. R. Emotions evoked by the sound of music: characterization, classification, and measurement. Emot. Wash. DC . 8 , 494–521 (2008). Juslin, P. N. From everyday emotions to aesthetic emotions: Towards a unified theory of musical emotions. Phys. Life Rev. 10 , 235–266 (2013). Gómez-Cañón, J. S. et al. Music Emotion Recognition: Toward new, robust standards in personalized and context-sensitive applications. IEEE Signal. Process. Mag . 38 , 106–114 (2021). Schedl, M., Gómez, E. & Urbano, J. Music Information Retrieval: Recent Developments and Applications. Found. Trends® Inf. Retr. 8 , 127–261 (2014). Yang, Y. H. & Chen, H. H. Machine Recognition of Music Emotion: A Review. ACM Trans. Intell. Syst. Technol. 3 (1–40), 30 (2012). Mouriquand, D. Is Oscar-winning composer Hans Zimmer reimagining Saudi Arabia’s national anthem? Euronews (2025). Agence France-Presse. Saudi Arabia asks Hans Zimmer to rework national anthem. Guardian (2025). Hofstede, G. Culture’s Consequences: International Differences in Work-Related Values (SAGE, 1980). Hofstede, G. Dimensionalizing Cultures: The Hofstede Model in Context. Online Read. Psychol. Cult. 2 , (2011). Minkov, M. & Kaasa, A. Do dimensions of culture exist objectively? A validation of the revised Minkov-Hofstede model of culture with World Values Survey items and scores for 102 countries. J. Int. Manag . 28 , 100971 (2022). National Anthems.info. WWW (n.d.). Eerola, T. Music and emotion stimulus sets consisting of film soundtracks. OSF (2019). Lartillot, O., Toiviainen, P. & Eerola, T. A Matlab Toolbox for Music Information Retrieval. in Data analysis, machine learning and applications vol. 35 261–268Springer, (2008). Tibshirani, R. Regression shrinkage and selection via the Lasso. J. R Stat. Soc. Ser. B-Methodol . 58 , 267–288 (1996). Greene, C. A. et al. The Climate Data Toolbox for MATLAB. Geochem. Geophys. Geosyst. 20 , 3774–3781 (2019). Juhász, Z. Revealing Footprints of Ancient Sources in Recent Eurasian and American Folk Music Cultures Using PCA of the Culture-Dependent Moment Vectors of Shared Melody Types. Music Sci. 7 , 20592043241228982 (2024). de Alva, J. J. K. Colonialism and postcolonialism as (Latin) American mirages. Colon Lat Am. Rev. 1 , 3–23 (1992). Muyolema, A. Colonialismo y representación: Hacia una relectura de los discursos latinoamericanista, indigenista, y clasistaétnico en los Andes del siglo XX. (PhD dissertation. Department of Hispanic Languages and Literatures …. Mayo-Harp M. I. National anthems and identities: the role of national anthems in the formation process of national identities (Simon Fraser University, 2001). Keller, M. C. et al. A Warm Heart and a Clear Head: The Contingent Effects of Weather on Mood and Cognition. Psychol. Sci. 16 , 724–731 (2005). Denissen, J. J., Butalid, L., Penke, L. & Van Aken, M. A. The effects of weather on daily mood: a multilevel approach. Emotion 8 , 662 (2008). Lawrance, E. L., Thompson, R., Le Vay, N., Page, J., Jennings, N. & L. & The Impact of Climate Change on Mental Health and Emotional Wellbeing: A Narrative Review of Current Evidence, and its Implications. Int. Rev. Psychiatry . 34 , 443–498 (2022). Cosgrove, S. Kimigayo and the Meiji Musical Aesthetic: A Comparative Study of Fenton and Hayashi’s Anthems. J. Joshibi Univ. Art Des. 25–34 (2023). Liu, H., Jiang, K., Gamboa, H., Xue, T. & Schultz, T. Bell Shape Embodying Zhongyong: The Pitch Histogram of Traditional Chinese Anhemitonic Pentatonic Folk Songs. Appl. Sci. 12 , 8343 (2022). Triandis, H. C. & Gelfand, M. J. Converging measurement of horizontal and vertical individualism and collectivism. J. Pers. Soc. Psychol. 74 , 118 (1998). Gaygısız, E. How are cultural dimensions and governance quality related to socioeconomic development? J. Socio-Econ . 47 , 170–179 (2013). Ayuso-Mateos, J. L. et al. Multi-Country Evaluation of Affective Experience: Validation of an Abbreviated Version of the Day Reconstruction Method in Seven Countries. PLOS ONE . 8 , e61534 (2013). Smit, E. A., Milne, A. J., Sarvasy, H. S. & Dean, R. T. Emotional responses in Papua New Guinea show negligible evidence for a universal effect of major versus minor music. PLOS ONE . 17 , e0269597 (2022). Signorini, P., Wiesemes, R. & Murphy, R. Developing alternative frameworks for exploring intercultural learning: a critique of Hofstede’s cultural difference model. Teach. High. Educ. 14 , 253–264 (2009). Gilboa, A. & Bodner, E. What are your thoughts when the national anthem is playing? An empirical exploration. Psychol. Music . 37 , 459–484 (2009). Steinfeld, J. Blurring the lines: National anthems are back in fashion. Why and where are people being forced to sing against their will? Index. Censorsh. 46 , 114–116 (2017). Greer, T., Singla, K., Ma, B. & Narayanan, S. Learning Shared Vector Representations of Lyrics and Chords in Music. in ICASSP –2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) 3951–3955 (2019). (2019). 10.1109/ICASSP.2019.8683735 Saari, P. & Eerola, T. Semantic Computing of Moods Based on Tags in Social Media of Music. IEEE Trans. Knowl. Data Eng. 26 , 2548–2560 (2014). 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-6318443","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":440265446,"identity":"6327c7b0-6c12-41e5-86be-dabc81563682","order_by":0,"name":"Petri Toiviainen","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA90lEQVRIiWNgGAWjYLCCBCBmY2BsADP4GQ4ASRsJPOqZQVoMEFokG0Ba0ghoYQBqgQMDkA6GNNwazNn7j314wPBHjk8iufHDwx02ecYHz5hJMCRY4NRi2XOYeQbQYcZsEonNEoln0orNDoC14HaYwY1kZpBfEtt4DjZIJLYdTtwG0sL4A4+W+4/hWpp/JLb9T9zcQNAWZqgW9sY2oC0HEjcwENBi2ZNszJBgYGzMBtRikdiWnDjjwLFiiwQ8WszZDz5m/FEhJyffzP745s82u8T+GYc33viQUIfbYUgkFEicMADHKX4tKIC//QEeDaNgFIyCUTACAQBV2lGQDGbOoAAAAABJRU5ErkJggg==","orcid":"","institution":"University of Jyväskylä","correspondingAuthor":true,"prefix":"","firstName":"Petri","middleName":"","lastName":"Toiviainen","suffix":""},{"id":440265447,"identity":"1e57fc54-b8be-496b-afda-1bf4ba90f80f","order_by":1,"name":"Martín Hartmann","email":"","orcid":"","institution":"University of Jyväskylä","correspondingAuthor":false,"prefix":"","firstName":"Martín","middleName":"","lastName":"Hartmann","suffix":""},{"id":440265448,"identity":"5f414e54-abca-4c86-a509-0e49d7867ac0","order_by":2,"name":"Friederike Koehler","email":"","orcid":"","institution":"University of Jyväskylä","correspondingAuthor":false,"prefix":"","firstName":"Friederike","middleName":"","lastName":"Koehler","suffix":""}],"badges":[],"createdAt":"2025-03-27 08:23:41","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6318443/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6318443/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-08956-6","type":"published","date":"2025-07-02T15:57:11+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":80255598,"identity":"f8080988-4766-478f-bf89-c4420cfe4a09","added_by":"auto","created_at":"2025-04-09 18:57:33","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":847827,"visible":true,"origin":"","legend":"\u003cp\u003eScatter plot of Valence and Energy Arousal of the anthems.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6318443/v1/d4df017fac8d56afe28a72a1.png"},{"id":80256123,"identity":"ce99bf44-4b4f-4e69-b4b1-8b07179b2815","added_by":"auto","created_at":"2025-04-09 19:05:33","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":588303,"visible":true,"origin":"","legend":"\u003cp\u003eEmotional content of anthems for Valence, Energy Arousal, and Tension Arousal. For each emotion, the values have been scaled to cover the entire range of colours.\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6318443/v1/99521ab6611299a5b27e87f6.jpeg"},{"id":80256124,"identity":"a2386299-311e-40d6-afa9-66d8c0f4e2d4","added_by":"auto","created_at":"2025-04-09 19:05:33","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":952806,"visible":true,"origin":"","legend":"\u003cp\u003eStrength of Happiness, Tenderness, Sadness, Anger, and Fear of anthems per country. For each emotion, the values have been scaled to cover the entire range of colours.\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6318443/v1/3be351950e078ccf58864cf4.jpeg"},{"id":80255601,"identity":"c8d4ec5a-bcba-4756-8cd7-d9c3752c2690","added_by":"auto","created_at":"2025-04-09 18:57:33","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":243512,"visible":true,"origin":"","legend":"\u003cp\u003eViolin plots of emotion dimensions per continent. EU = Europe, AS = Asia, AF = Africa, AM =Americas, OC = Oceania. Significant differences obtained from pairwise t tests (FDR corrected) are indicated with solid and dotted line segments for adjusted p thresholds of .05 and .01, respectively.\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6318443/v1/37a883b7fdff4f53afcf1466.jpeg"},{"id":80255603,"identity":"23eab7da-98e1-471f-ad46-810169f1c9c2","added_by":"auto","created_at":"2025-04-09 18:57:33","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":344230,"visible":true,"origin":"","legend":"\u003cp\u003eViolin plots of basic emotions per continent. EU = Europe, AS = Asia, AF = Africa, AM =Americas, OC = Oceania. Significant differences obtained from pairwise t tests (FDR corrected) are indicated with solid and dotted line segments for adjusted p thresholds of .05 and .01, respectively.\u003c/p\u003e","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6318443/v1/210b6aec2880f94c26b0f42b.jpeg"},{"id":80256125,"identity":"2936605d-afae-4468-88c7-92bd5c769fa6","added_by":"auto","created_at":"2025-04-09 19:05:33","extension":"jpeg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":360105,"visible":true,"origin":"","legend":"\u003cp\u003eGeographical trends in emotional dimensions and basic emotions in national anthems. (a) Emotional dimensions: Valence tends to increase eastward, Energy Arousal is stronger near the equator, and Tension Arousal increases westward. (b) Basic emotions: Sadness increases northward, eastward, and moving away from the equator, Happiness is stronger near the equator and in the southern hemisphere, and Fear and Anger are more prominent westward. Arrows indicate the direction of change, with blue representing lower values and red representing higher values.\u003c/p\u003e","description":"","filename":"floatimage6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6318443/v1/2e2cd91b0ed7de5f267f01a1.jpeg"},{"id":86178923,"identity":"2bbad42a-181f-44c4-9813-28b8c6cd3174","added_by":"auto","created_at":"2025-07-07 16:11:26","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4282098,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6318443/v1/15d29e83-3700-4d80-8f84-e6cb99c103a9.pdf"},{"id":80256425,"identity":"ebd38b83-696c-476c-afbc-61f24553f8e5","added_by":"auto","created_at":"2025-04-09 19:13:33","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":468136,"visible":true,"origin":"","legend":"","description":"","filename":"SUPPLEMENTARYINFORMATION.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6318443/v1/792c5cf100eb54b40a461931.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The emotional geography of national anthems","fulltext":[{"header":"1 INTRODUCTION","content":"\u003cp\u003eNational anthems represent an important national symbol\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Since the formation of nation-states, political leaders have consistently created and adopted national symbols (e.g., flags, anthems, mottos, currencies, constitutions, and holidays) to build and maintain national identity among the country\u0026rsquo;s population\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. National anthems serve as official patriotic symbols (often seen as a musical counterpart of the flag), reflecting the nation\u0026rsquo;s identity and character including its mood, aspirations and goals as defined by leaders\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. Their main function is to express a nation\u0026rsquo;s internal unity (establishing bonds and collective goals) and external uniqueness (distinguishing and confirming boundaries)\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. Government elites thus strive to widely disseminate the national anthem among the population (e.g., incorporating the anthem into school curricula or official events and ceremonies) to legitimate formal authority (also termed banal nationalism\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e). National anthems create a collective sentiment, which can be particularly observed in sport contexts, activating social identity and bonding. For instance, football teams that showed more passion during the singing of national anthems at UEFA Euro 2016 had a higher likelihood of success\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Correspondingly, music can convey national identity in two ways: from the inside-looking-in (sense of belonging and membership) and outside-looking-in (recognized by non-members)\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cdiv id=\"Sec2\" class=\"Section2\"\u003e \u003ch2\u003e1.1 Content and Music of National Anthems\u003c/h2\u003e \u003cp\u003eWhile the function of national anthems is similar across nations, their content and structure differs greatly among nations, representing different strategies to convey thoughts, emotions, messages, and goals\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. A broad classification has been consolidated between honor anthems paying (religious) homage and revolutionary anthems\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. Factors that influence the creation or adoption of an anthem are numerous, including a nation\u0026rsquo;s form of government, geographic location, socio-political events (e.g., wars, revolutions), economic aspects (e.g., world-system position, modernization) or the creative style during the anthem\u0026rsquo;s creation or adoption\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Most of previous research on national anthems, however, focused on sociological and linguistic analyses. For instance, a cross-cultural lyrics analysis identified diverse topics in national anthems, including ancestry and past, homeland, beauty, unity, victory, or freedom, showing weak correlations with societal features (e.g., age of country, gross domestic product)\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. Further research found that the lyrical sentiment of national anthems varies by region: Latin and Mediterranean anthems are generally neutral, while Central and Western Asian, Germanic, and Slavic anthems have a more positive sentiment\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. One content analysis even linked positive messages in anthems to lower suicide rates, while negative or conflicted themes were related to higher suicide rates\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. However, a critical stance and controversies have emerged during the past decades regarding the use and content of national anthems promoting patriotism, propagandism and chauvinism\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e, especially in music education.\u003c/p\u003e \u003cp\u003eApart from the lyrical content, the musical structure of national anthems has mainly been investigated in the fields of historical musicology and ethnomusicology through case studies, for instance, on the anthem of Zimbabwe\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. Earlier sociological work has linked symbolic musical codes in anthems with sociopolitical control and a nation\u0026rsquo;s world-system position at the time of the anthem\u0026rsquo;s creation or adoption\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. However, a systematic and comprehensive investigation of objective musical features in a variety of anthems has received little academic attention yet, especially regarding the emotional content of the music. An earlier investigation of anthems of 18 European countries\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e revealed a positive association of the proportion of low notes in national anthems with students' perceptions of the anthems' gloominess and sadness as well as with national suicide rates. A recent initial study based on computational music analysis\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e points to possible links between certain musical characteristics (e.g., low pitch, high tempo, high beat) and positive social outcomes (e.g., high happiness and peace, low suicide rate). Although the main function of music lies in its potency to induce and affect emotions\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e, little is known about the emotions reflected in national anthems and potential underlying influencing factors, such as geographical location or cultural differences.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e1.2 Music and Emotions\u003c/h2\u003e \u003cp\u003eThe emotional characteristics of national anthems might be explained by a number of underlying factors. These include cultural movements, national tendencies and environmental factors such as geographic and climatic differences between countries. Before digging deeper into this issue\u0026mdash;and particularly on the role of geography and cultural orientation upon emotions expressed by anthems, which is a central topic to this study\u0026mdash;, it is necessary to introduce the main psychological and computational frameworks used in music and emotion research.\u003c/p\u003e \u003cp\u003eEmotions in music can be experienced as a subjective response to music (i.e., felt or induced emotions) or be attributed to music (i.e., expressed or perceived emotions), although there might be a significant overlap in this classification\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. The most prominent theoretical frameworks used in music and emotion research are discrete and dimensional models of emotions\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. According to Ekman\u0026rsquo;s discrete or basic emotion model\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e, all emotions can be traced back to a small set of fundamental and inherent emotions (fear, anger, disgust, sadness, and happiness), with specific underlying neurophysiological systems. The two-dimensional circumplex model\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e, however, proposes that all emotions emerge from two independent fundamental dimensions, that is, valence (a pleasure\u0026ndash;displeasure continuum) and arousal (activation\u0026ndash;deactivation). Later work suggested an expansion into a three-dimensional model through dividing arousal into two separate dimensions: tension arousal and energy arousal\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. Both main classes of models have been commonly applied to investigate emotions in music, while it has also been argued that it may not capture the complexity of emotions in an aesthetic context like music\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. For instance, the basic emotion of disgust has often been modified to tenderness in the context of music research\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. One of the main frameworks to explain music-evoked emotions (BRECVEMA)\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e suggests seven underlying mechanisms (brain stem reflex, rhythmic entrainment, evaluative conditioning, contagion, visual imagery, episodic memory, musical expectancy, and aesthetic judgment), with brain stem reflex, rhythmic entrainment, and musical expectancy being mostly dependent on the musical content.\u003c/p\u003e \u003cp\u003eApart from measuring emotions associated with music through directly asking individuals (self-reports), recent innovative approaches have included music emotion recognition (MER), that is, the computational task of automatically recognizing emotional content in music or emotions induced by music. MER is a high-level problem within the field of music information retrieval (MIR), an interdisciplinary area focusing on understanding and organizing music collections using computational techniques. MER has several applications, such as automatically categorizing music pieces based on emotions or recommending music tailored to a user\u0026rsquo;s mood \u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. MER systems can be built upon musical content and/or context, and include user factors such as demographic or situational information \u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. In a typical MER framework, signal processing techniques are used to extract emotionally relevant features from music excerpts. Audio-based features representing musical dimensions such as loudness, timbre, rhythm and harmony have been extensively studied in MER \u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. These features are paired with ground truth data\u0026mdash;human annotations that describe perceived or induced emotions. A machine learning model is then trained on part of this annotated data set to recognize patterns, and its performance is evaluated on the remaining data. While previous research on national anthems has predominantly focused on the lyrical content and less on the emotional content as reflected in the music itself, MER offers great potential for an objective and comprehensive analysis of the emotions expressed in national anthems, elucidating certain patterns and providing insights into cross-cultural comparisons.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e1.3 Emotions in Anthems: Geography and Cultural Differences\u003c/h2\u003e \u003cp\u003eSome of the similarities and differences in the emotional characteristics of national anthems might be explained through identifying geographical patterns. Geographical location often shapes the cultural, historical, and social context of a nation and the development of its identity. For instance, the history of a broader region, including events like wars, revolutions, and independence movements, often occurs in reciprocity with nearby countries and might impact the content and emotional tone of national anthems. Understanding the geographical context might help to interpret these historical influences and how they are reflected in the music. Furthermore, the location of a country and its surrounding landscape and resources are prominent themes in national anthems\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e, potentially influencing the music as well. As a recent example, Saudi Arabia, the largest petroleum exporter, is reportedly rearranging its national anthem with a Western composer, potentially incorporating new influences that reflect both its identity as a resource-rich nation and its increasing Westernization amid economic diversification efforts \u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e,\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eVariations in national anthems might also be linked with differences in cultural values and behaviors. A widely used framework to understand cultural differences (Hofstede\u0026rsquo;s cultural dimensions theory)\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e,\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e proposes six main cultural dimensions: \u003cem\u003ePower Distance\u003c/em\u003e (solutions to the basic issue of human inequality), \u003cem\u003eIndividualism vs. Collectivism\u003c/em\u003e (integration of individuals in primary group), \u003cem\u003eMotivation towards Achievement and Success\u003c/em\u003e, formerly \u003cem\u003eMasculinity vs. Femininity\u003c/em\u003e (preference for achievement or cooperation), \u003cem\u003eUncertainty Avoidance\u003c/em\u003e (stress facing an unknown future), \u003cem\u003eLong-Term vs. Short-Term Orientation\u003c/em\u003e (focus on future, present, or past), and \u003cem\u003eIndulgence vs. Restraint\u003c/em\u003e (regulation of human desires of enjoyment). These dimensions have been consistently used to examine important differences among nations in terms of political and economic systems, business and management practices, and other societal variations\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. However, to the best of our knowledge, no study has investigated these dimensions regarding differences in national anthems yet.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e1.4 Research objectives\u003c/h2\u003e \u003cp\u003eBased on the lack of research on the emotions reflected in the music of national anthems and their possible links with geographical location and cultural differences, the main research objective of the study is to provide an overview of the emotional geography of national anthems based on computational modeling. Specifically, it aims to:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eanalyze the emotional content of national anthems based on their musical features using computational modeling\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eexamine geographical patterns in the emotional characteristics of national anthems, focusing on both continents and specific coordinates (latitude and longitude).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003ecompare the predictability of emotion dimensions (Valence, Energy Arousal, Tension Arousal) and basic emotions (Happiness, Sadness, Tenderness, Anger, Fear) from musical features.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eexplore how emotional expressions in national anthems vary globally and reflect broader cultural differences (Hofstede\u0026rsquo;s cultural dimensions)\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"2 METHODS","content":"\u003cp\u003eWe used computational modeling because it provides scalability and efficiency, allowing the analysis of a large number of anthems (176 in this study) in a time-effective manner, which would be challenging and resource-intensive with perceptual data collection. Moreover, computational modeling allowed us to concentrate on how the contribution of specific musical elements\u0026mdash;such as timbral, rhythmic, and tonal characteristics\u0026mdash;to emotional expression varies globally without interference from factors like lyrical content, cultural context, patriotic sentiment, or political associations.\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Material\u003c/h2\u003e \u003cp\u003eThe material used in the study comprised instrumental recordings of national anthems, sourced from the comprehensive database \u003cem\u003eNational Anthems.info\u003c/em\u003e\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. A deliberate choice was made to include only those anthems presented in authentic instrumental form, thereby excluding any renditions played using MIDI instruments to ensure the acoustic consistency and authenticity of the sample. As a result, a total of 176 anthems were selected for further analysis. This selection process involved choosing the most recent anthem for countries with a history of multiple anthems, aligning with our focus on contemporary national identity as reflected in these musical symbols. The final dataset exhibits a global representation, with anthems from Europe (43), Asia (40), Africa (50), the Americas (35), and Oceania (8), ensuring a broad cultural and geographical scope for the analysis. The average length of the anthem recordings was 83.7 seconds, ranging from 31.8 seconds (Estonia) to 270.0 seconds (Uruguay). The list of countries included in the study is detailed in Supplementary Table\u0026nbsp;1.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Emotion modelling\u003c/h2\u003e \u003cp\u003eAs we were interested in how the contribution of specific musical elements\u0026mdash;such as timbral, rhythmic, and tonal characteristics\u0026mdash;to emotional expression varies globally, computational modeling allowed us to focus on these features without interference from factors like lyrical content, cultural context, patriotic sentiment, or political associations. To this end, we combined perceptual data from the emotion database of Eerola\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e, which includes musical excerpts with diverse emotional content, with musical features extracted by music information retrieval techniques. To ensure the models' generalizability, we applied cross-validation and regularization, minimizing overfitting and enhancing their applicability to unseen data. Finally, we used the resulting models to predict the perceived emotional content of the national anthems, linking musical characteristics with emotional perception in a data-driven and systematic manner.\u003c/p\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e2.2.1 Musical feature extraction\u003c/h2\u003e \u003cp\u003eThe modelling of emotional content was grounded in the analysis of 360 film soundtrack excerpts, using the emotion database of Eerola\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. This database comprises ratings of eight emotion characteristics provided by 116 participants. The emotion characteristics were based on two models: a dimensional model encompassing valence, energy arousal, and tension arousal, and a basic emotions model including happiness, sadness, tenderness, anger, and fear. Utilizing the MIR Toolbox\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e, we extracted 65 musical features, representing dynamic, rhythmic, spectral, timbral, and tonal content, from each movie soundtrack excerpt. The extracted features are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The analysis adopted a bag of frames approach, calculating both means and standard deviations of the features, thus capturing the dynamic range and variability within each excerpt. Features with a skewed distribution were Box-Cox transformed.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e2.2.2 LASSO regression\u003c/h2\u003e \u003cp\u003eTo link the extracted musical features to the emotion ratings, we employed least absolute shrinkage and selection operator (LASSO) regression\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. This method was chosen for its effectiveness in handling multicollinearity and selecting relevant predictors in datasets with many variables. LASSO, or Least Absolute Shrinkage and Selection Operator, is an iterative regression technique that imposes a penalty on the absolute size of regression coefficients, effectively reducing less relevant predictors to zero and retaining only the most influential ones. This approach enhances the generalizability of the model by focusing on a parsimonious set of predictors that are robust across different datasets.\u003c/p\u003e \u003cp\u003eWe treated each emotion characteristic as a dependent variable in separate models and assessed the predictive power of the musical features through cross-validation, using an 80/20 random split into training and testing sets across 100 runs to ensure robust findings. We determined the optimal regularization parameter by minimizing the prediction error for the test data, based on the average model accuracy measured by the adjusted coefficient of determination across these runs. Finally, we retrained the models using the full dataset and the identified optimal regularization parameters to enhance predictive accuracy..\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Emotion prediction\u003c/h2\u003e \u003cp\u003eAfter training the eight LASSO models, we extracted the 60 musical features illustrated in Supplementary Fig.\u0026nbsp;1 for all 176 national anthems. Using these trained models, we predicted the eight perceived emotion characteristics for each anthem.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Emotions of anthems and geographical variation\u003c/h2\u003e \u003cp\u003eUsing the predicted emotional characteristics, we subsequently analyzed their regional differences through a comparison between individual countries as well as across continents using one-way ANOVAs. We further examined global patterns by performing correlation analyses between the predicted emotional characteristics and geographical coordinates, including latitude and longitude. The geographical centroids of countries were sourced from the \u003cem\u003eClimate Data Toolbox\u003c/em\u003e\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIt is important to recognize the distinct characteristics of latitude and longitude as geographical variables. Latitude is linear and directly linked to the Earth's geography, representing the distance north or south from the equator. Accordingly, we used latitude values, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\phi\\:\\)\u003c/span\u003e\u003c/span\u003e, as well as their absolute values, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\left|\\phi\\:\\right|\\)\u003c/span\u003e\u003c/span\u003e, directly in the correlation analysis. Longitude, by contrast, is cyclical, reflecting the Earth's rotation and requiring periodic wrapping to ensure continuity. Additionally, the origin of longitude at the Greenwich Meridian is a cultural convention established during the International Meridian Conference of 1884, rather than a natural reference point. Consequently, for longitude (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\lambda\\:\\)\u003c/span\u003e\u003c/span\u003e), we set its values to increase from west to east, applied the sine transformation, calculated as \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:sin(\\lambda\\:-\\delta\\:)\\)\u003c/span\u003e\u003c/span\u003e, and determined the value of the longitude offset, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\stackrel{\\sim}{\\delta\\:}\\)\u003c/span\u003e\u003c/span\u003e, that maximized the absolute value of correlation. In other words, for each emotion variable, the reference meridian is shifted so as to optimize the strength of the relationship (regardless of its direction) between monotonically increasing longitude and the emotion variable.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Emotions of anthems and cultural dimensions\u003c/h2\u003e \u003cp\u003eTo explore the influence of societal values and behaviours on the emotional content of national anthems, we conducted correlation analyses between the predicted emotional characteristics and Hofstede\u0026rsquo;s\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e,\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e six cultural dimensions. The availability of data varied across the six scales: PDI, IDV, MAS, and UAI included data for 66 countries, while LTO and IVR included data for 89 countries. The countries associated with each scale are listed in Supplementary Table\u0026nbsp;1.\u003c/p\u003e \u003c/div\u003e"},{"header":"3 RESULTS","content":"\u003cp\u003eThe prediction accuracies of the models used to describe the emotion characteristics of each anthem were moderate to moderately high, as shown in Supplementary Table\u0026nbsp;2. This table presents the regularization parameter and coefficient of determination for the optimal LASSO model corresponding to each emotion characteristic. Emotion dimensions (Valence, Energy Arousal, and Tension Arousal) were predicted with greater accuracy than basic emotions due to their broader and more generalized representation of emotional content. The model coefficients are illustrated in Supplementary Fig.\u0026nbsp;1.\u003c/p\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Emotions of anthems per country\u003c/h2\u003e \u003cdiv id=\"Sec16\" class=\"Section3\"\u003e \u003ch2\u003e3.1.1 Emotion dimensions\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e displays a scatter plot of the Valence and Energy Arousal of the 176 national anthems, as predicted by the respective LASSO models.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAs can be seen, most of the anthems are located in the quadrant characterized by positive Valence and high Energy Arousal, indicating that happiness is the predominant basic emotion they convey.\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents Valence, Energy Arousal, and Tension Arousal of anthems displayed on a world map. In this and all subsequent maps, the values are scaled such that the minimum value is displayed as blue and the maximum value as red.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWhile there is a great extent of local variation in the emotional content, some geographical trends can be observed. For instance, Valence tends to be more negative in the Americas compared to other regions. Energy Arousal appears to be higher in countries situated close to the equator, while many countries in Southern Africa and South Asia, as well as Australia exhibit lower Tension Arousal, indicating a calmer emotional tone in the anthems from these regions. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e provides an overview of the countries with the three lowest and three highest values for each emotion dimension.\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\u003eAnthems with lowest and highest values of each emotion dimension.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEmotion dimension\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLowest\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHighest\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eValence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePakistan, Tanzania, Malaysia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eComoros, Jamaica, Mozambique\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEnergy Arousal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJamaica, Kenya, Mozambique\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSouth Korea, China, Paraguay\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTension Arousal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJamaica, Comoros, Nicaragua\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMalaysia, Pakistan, Indonesia\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=\"Sec17\" class=\"Section3\"\u003e \u003ch2\u003e3.1.2 Basic emotions\u003c/h2\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Strength of Happiness, Tenderness, Sadness, Anger, and Fear of anthems per country. For each emotion, the values have been scaled to cover the entire range of colours.\u003c/p\u003e \u003cp\u003eSimilar to the emotion dimensions, there is a significant degree of local variation. We encourage the reader to explore the general trends for each emotion independently.\u003c/p\u003e \u003cp\u003eMeanwhile, Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e provides an overview of the countries with the three lowest and three highest intensities for each emotion.\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\u003eAnthems with lowest and highest strength of each basic emotion.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBasic emotion\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLowest\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHighest\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHappiness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIsrael, Liechtenstein, Jamaica\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWestern Sahara, China, Dominica\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTenderness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNigeria, Qatar, US Virgin Islands\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eJapan, Cambodia, Netherlands\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSadness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLebanon, China, Rwanda\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eJapan, Israel, Liechtenstein\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnger\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCambodia, Japan, Netherlands\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQatar, Sudan, Nigeria\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFear\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNiger, Kenya, China\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQatar, Jamaica, Liechtenstein\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 \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Emotions of anthems per continent\u003c/h2\u003e \u003cp\u003eTo examine broader geographic patterns, we conducted statistical analyses to compare the emotional content across continents.\u003c/p\u003e \u003cdiv id=\"Sec19\" class=\"Section3\"\u003e \u003ch2\u003e3.2.1 Emotion dimensions\u003c/h2\u003e \u003cp\u003eWe conducted a series of one-way ANOVAs to examine differences in Valence, Energy Arousal, and Tension Arousal of anthems across continents. The results revealed significant continental differences for all three dimensions. Valence exhibited a significant effect of continent (\u003cem\u003eF\u003c/em\u003e(4, 170)\u0026thinsp;=\u0026thinsp;5.87, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001), with means ranging from 4.39 to 5.00. Energy Arousal also showed significant differences (\u003cem\u003eF\u003c/em\u003e(4, 170)\u0026thinsp;=\u0026thinsp;2.48, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.046), with means between 4.54 and 4.93. Tension Arousal demonstrated a strong continental effect as well (\u003cem\u003eF\u003c/em\u003e(4, 170)\u0026thinsp;=\u0026thinsp;4.65, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.001), with means varying from 3.51 to 4.05.\u003c/p\u003e \u003cp\u003eNext, we conducted post hoc tests using pairwise \u003cem\u003et\u003c/em\u003e-tests with false discovery rate (FDR) correction to account for multiple comparisons. Figure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e presents violin plots for each of the emotion dimensions, illustrating the distribution of scores, and displays pairwise significant differences identified through FDR-adjusted \u003cem\u003eq\u003c/em\u003e values (\u0026lt;\u0026thinsp;.05). Most notably, Valence in the Americas was significantly more negative than in all other continents, reflecting a distinct emotional tone in anthems from this region. Additionally, Tension Arousal in the Americas was higher compared to Europe, Africa, and Oceania, suggesting that anthems from the Americas convey a heightened sense of urgency or intensity relative to those from these other continents.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section3\"\u003e \u003ch2\u003e3.2.2 Basic emotions\u003c/h2\u003e \u003cp\u003eFor the basic emotions, the one-way ANOVAs revealed significant differences across continents for Fear (\u003cem\u003eF\u003c/em\u003e(4, 170)\u0026thinsp;=\u0026thinsp;5.23, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001), Happiness (\u003cem\u003eF\u003c/em\u003e(4, 170)\u0026thinsp;=\u0026thinsp;3.31, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.012), Sadness (\u003cem\u003eF\u003c/em\u003e(4, 170)\u0026thinsp;=\u0026thinsp;2.47, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.047), and Tenderness (\u003cem\u003eF\u003c/em\u003e(4, 170)\u0026thinsp;=\u0026thinsp;3.01, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.020), while no significant differences were found for Anger (\u003cem\u003eF\u003c/em\u003e(4, 170)\u0026thinsp;=\u0026thinsp;1.36, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.250). Again, post hoc tests were conducted using pairwise \u003cem\u003et\u003c/em\u003e-tests with false discovery rate (FDR) correction to account for multiple comparisons. Figure\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e presents violin plots for each of the five basic emotions, illustrating the distribution of scores and highlighting pairwise significant differences identified through FDR-adjusted p values (\u0026lt;\u0026thinsp;.05). It can be observed that Fear was significantly higher in the Americas than in all other continents, while Happiness was significantly higher in Oceania than in the Americas and Europe.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Emotions of anthems and location on the globe\u003c/h2\u003e \u003cp\u003eTo gain a more detailed understanding of the relationship between the emotional content of anthems and geographical location, we conducted correlation analyses between the predicted perceived emotions of the anthems and the latitudes and longitudes of the geographical centroids of the respective countries.\u003c/p\u003e \u003cdiv id=\"Sec22\" class=\"Section3\"\u003e \u003ch2\u003e3.3.1 Emotion dimensions\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e.a summarizes the observed geographical patterns in the emotional dimensions, highlighting how these emotional characteristics vary along latitudinal and longitudinal gradients. The correlations between the emotion dimensions and the geographical coordinates of the countries, along with the optimal longitude offsets, are displayed in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCorrelation coefficients between the intensity of the three emotion dimensions in the anthems and latitude (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{r}_{\\phi\\:}\\)\u003c/span\u003e\u003c/span\u003e), absolute value of latitude (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{r}_{\\left|\\phi\\:\\right|}\\)\u003c/span\u003e\u003c/span\u003e), and sine-transformed longitude (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{r}_{sin(\\lambda\\:-\\stackrel{\\sim}{\\delta\\:})}\\)\u003c/span\u003e\u003c/span\u003e) of the countries' geographical centroids, as well as the optimal longitude offset (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\stackrel{\\sim}{\\delta\\:}\\)\u003c/span\u003e\u003c/span\u003e). **p\u0026thinsp;\u0026lt;\u0026thinsp;.01, ***p\u0026thinsp;\u0026lt;\u0026thinsp;.001, two-tailed.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEmotion dimension\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{r}_{\\phi\\:}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{r}_{\\left|\\phi\\:\\right|}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{r}_{sin(\\lambda\\:-\\stackrel{\\sim}{\\delta\\:})}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\stackrel{\\sim}{\\delta\\:}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eValence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.252***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-7\u0026deg;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEnergy arousal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.142\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.217**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.060\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u0026deg;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTension arousal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.075\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.208**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u0026deg;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFor Valence, the significant positive correlation with sine-transformed longitude (r\u0026thinsp;=\u0026thinsp;.252, p\u0026thinsp;\u0026lt;\u0026thinsp;.001) and an optimal longitude offset of -7\u0026deg; suggests that Valence tends to increase when moving towards the east from the longitude 7\u0026deg;W, and vice versa. Energy Arousal, with a significant negative correlation to absolute latitude (r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;.217, p\u0026thinsp;\u0026lt;\u0026thinsp;.01), indicates that anthems from countries closer to the equator exhibit higher Energy Arousal. Finally, Tension Arousal, which correlates negatively with sine-transformed longitude (r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;.208, p\u0026thinsp;\u0026lt;\u0026thinsp;.01) at an optimal offset of 1\u0026deg;, implies a regional increase in Tension Arousal when moving towards the west from the longitude 1\u0026deg;. It is notable that the optimal longitude offset values are close to zero for all three emotion dimensions.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003e3.3.2 Basic emotions\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e.b summarizes the observed geographical patterns in basic emotions. Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e shows the correlations between the strength of basic emotions and the geographical coordinates of the countries.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCorrelation coefficients between the strength of basic emotions in the anthems and latitude (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{r}_{\\phi\\:}\\)\u003c/span\u003e\u003c/span\u003e), absolute value of latitude (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{r}_{\\left|\\phi\\:\\right|}\\)\u003c/span\u003e\u003c/span\u003e), and sine-transformed longitude (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{r}_{sin(\\lambda\\:-\\stackrel{\\sim}{\\delta\\:})}\\)\u003c/span\u003e\u003c/span\u003e) of the countries' geographical centroids, as well as the optimal longitude offset (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\stackrel{\\sim}{\\delta\\:}\\)\u003c/span\u003e\u003c/span\u003e). *p\u0026thinsp;\u0026lt;\u0026thinsp;.05, **p\u0026thinsp;\u0026lt;\u0026thinsp;.01, ***p\u0026thinsp;\u0026lt;\u0026thinsp;.001, two-tailed.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBasic emotion\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{r}_{\\phi\\:}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{r}_{\\left|\\phi\\:\\right|}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{r}_{sin(\\lambda\\:-\\stackrel{\\sim}{\\delta\\:})}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\stackrel{\\sim}{\\delta\\:}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHappiness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.219**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.178*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.065\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-79\u0026deg;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTenderness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.114\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.099\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-32\u0026deg;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSadness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.249***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.247***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.134*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e33\u0026deg;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnger\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.090\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.155*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-11\u0026deg;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFear\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.242***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4\u0026deg;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAs can be seen, Happiness shows a significant negative correlation with latitude (r\u003csub\u003eφ\u003c/sub\u003e = \u0026minus;\u0026thinsp;.219, p\u0026thinsp;\u0026lt;\u0026thinsp;.01) and absolute latitude (r\u003csub\u003e|φ|\u003c/sub\u003e = \u0026minus;\u0026thinsp;.178, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.05), suggesting that Happiness is stronger closer to the equator and in the south. Sadness, on the other hand, exhibits significant positive correlations with both latitude (r\u003csub\u003eφ\u003c/sub\u003e = .249, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001) and absolute latitude (r\u003csub\u003e|φ|\u003c/sub\u003e = .247, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001), indicating that higher levels of Sadness are associated with regions farther from the equator and in the north. Additionally, Sadness has a weaker, but significant, positive correlation with sine-transformed longitude (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{r}_{sin(\\lambda\\:-\\stackrel{\\sim}{\\delta\\:})}\\)\u003c/span\u003e\u003c/span\u003e = .134, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.05) with an optimal offset of 33\u0026deg;E, indicating that Sadness tends to increase slightly when moving eastward from this longitude. Both Fear (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{r}_{sin(\\lambda\\:-\\stackrel{\\sim}{\\delta\\:})}\\)\u003c/span\u003e\u003c/span\u003e= \u0026minus;\u0026thinsp;.242, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001) and Anger (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{r}_{sin(\\lambda\\:-\\stackrel{\\sim}{\\delta\\:})}\\)\u003c/span\u003e\u003c/span\u003e = \u0026minus;\u0026thinsp;.155, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.05) show significant negative correlations with sine-transformed longitude, with optimal offsets \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\stackrel{\\sim}{\\delta\\:}\\:\\)\u003c/span\u003e\u003c/span\u003eclose to 0\u0026deg;, implying that these emotional qualities tend to become stronger when moving towards west from the zero meridian. By contrast, Tenderness does not exhibit significant correlations with any geographical coordinate, suggesting a lack of strong geographical patterns for this emotion. These findings indicate that Sadness and Happiness are primarily associated with latitude, while Fear and Anger display notable longitudinal patterns.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Anthem emotions and cultural dimensions\u003c/h2\u003e \u003cp\u003ePower Distance (PDI) showed a moderate positive correlation with Energy Arousal (\u003cem\u003er\u003c/em\u003e(64)\u0026thinsp;=\u0026thinsp;.516, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001), indicating that countries with hierarchical structures, where unequal power distribution is more accepted, tend to have more energetic anthems. Individualism vs. Collectivism (IDV) exhibited negative correlations with both Energy Arousal (\u003cem\u003er\u003c/em\u003e(64)\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;.373, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.05) and Tension Arousal (\u003cem\u003er\u003c/em\u003e(64)\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;.369, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.05), suggesting that individualistic societies have less energetic and less tense anthems. Additionally, IDV correlated positively with Tenderness (\u003cem\u003er\u003c/em\u003e(64)\u0026thinsp;=\u0026thinsp;.383, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.05), implying that these societies favor anthems with gentler emotional tones. Finally, Indulgence vs. Restraint (IVR) correlated positively with Fear (\u003cem\u003er\u003c/em\u003e(87)\u0026thinsp;=\u0026thinsp;.374, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.01), indicating that indulgent societies, which emphasize enjoyment and gratification of desires, tend to have anthems expressing higher levels of fear.\u003c/p\u003e \u003c/div\u003e"},{"header":"4 DISCUSSION","content":"\u003cp\u003eIn this study, we explored the possible connections between the emotional content of instrumental renditions of 176 national anthems and their geographical location. To achieve this goal, we extracted a set of acoustic features describing expressed emotions by the anthems and three geodetic features based on latitude and longitude of the geometric center of the countries. We expected to observe geographical trends, assuming that climatic and biogeographic factors influence the emotional content of national anthems\u0026mdash;just as they do with other national symbols like flags, coats of arms, and birds, as well as national identity components such as language, history, ancestry, culture, and cuisine.\u003c/p\u003e \u003cdiv id=\"Sec26\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Summary of results\u003c/h2\u003e \u003cp\u003eMost national anthems across the studied countries conveyed some degree of Happiness (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). However, a cross-continental comparison revealed notable differences in emotional expression (Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Valence was significantly more negative in the Americas than in other continents. Tension Arousal was also higher in the Americas compared to Europe, Africa, and Oceania. Additionally, Fear levels were significantly higher in the Americas than in all other regions, while Happiness was more pronounced in Oceania than in the Americas and Europe.\u003c/p\u003e \u003cp\u003eLatitudinal trends revealed that Energy Arousal was stronger in countries closer to the equator. Happiness was also more prominent near the equator and in the southern regions, while Sadness was more prevalent farther from the equator and in the north. Longitudinally, Valence increased eastward, whereas Tension Arousal and Anger intensified westward. Similarly, Sadness rose in an eastward direction, whereas Fear increased toward the west.\u003c/p\u003e \u003cp\u003eWhen examining emotions in relation to Hofstede\u0026rsquo;s cultural dimensions\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e,\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e, countries with hierarchical power structures tended to have more energetic anthems. In contrast, individualistic societies favored anthems that were less tense, less energetic, and more tender. Additionally, indulgent societies tended to have anthems that expressed higher levels of Fear.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec27\" class=\"Section2\"\u003e \u003ch2\u003e4.2 General discussion\u003c/h2\u003e \u003cp\u003eOne of the clearest results of this study was that anthems of American countries exhibit higher levels of Fear, greater Tension Arousal, and more negative Valence compared to those from other regions. Analyzing countries with extreme values for key predictors of these emotional features further supports this pattern. For instance, Chile\u0026rsquo;s and Nicaragua\u0026rsquo;s anthems feature frequent harmonic changes, Jamaica\u0026rsquo;s and Aruba\u0026rsquo;s have low pulse clarity, and Antigua and Barbuda\u0026rsquo;s anthem has high spectral energy in center frequencies, all of which are strong predictors of Fear. Anthems from Barbados and Dominica are very high in roughness, with their instrumentation dominated by brass and percussive elements, resulting in high Tension Arousal and low Valence. Brazil and Costa Rica exhibit very low key clarity, which refers to how clearly a musical piece establishes a tonal center, and corresponds to high Fear and Tension Arousal.\u003c/p\u003e \u003cp\u003eWhile these musical features highlight the emotional qualities of anthems, their interpretation can be informed by the broader historical and cultural context. The native music of many of these nations is characterized by pentatonic scales, as seen in Andean cultures\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e, yet postcolonial anthems are heavily influenced by\u0026mdash;or even composed by\u0026mdash;European musicians of the 19th century. The rich rhythmic and tonal palette of Italian opera and other Romantic music might partly explain the emotional qualities of these anthems. At the same time, these musical choices reflect a decolonization process largely led by elites of mixed European and non-European ancestry, rather than by the broader colonized population\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e,\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. In line with this interpretation, another study suggested that the language of an anthem\u0026mdash;specifically Spanish\u0026mdash;is a significant factor contributing to the bellicose nature of anthems \u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e, which may also explain some of the heightened emotional intensity observed.\u003c/p\u003e \u003cp\u003eAnother key finding was that anthems from Oceania expressed higher levels of Happiness compared to those from the Americas and Europe. An examination based on mode, a key predictor of Happiness, shows that Fiji is the most major anthem in the dataset. It has also been observed that most Oceanic anthems are uplifting church hymns or patriotic songs from their current or former colonizing countries\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e, whereas European and American anthems are more diverse in that they can be persistently major (e.g., Germany, Bolivia) or have passages in minor mode (e.g., Argentina, France). However, this finding should be interpreted with caution due to the small sample size (8 countries) from Oceania.\u003c/p\u003e \u003cp\u003eRegarding latitudinal trends, we found that anthems from countries closer to the equator exhibit higher levels of Happiness and Energy Arousal, along with lower levels of Sadness. Similarly, countries in southern regions tend to have higher Happiness and lower Sadness. The similarities in correlations between these emotions and both latitude and absolute latitude may stem from the uneven distribution of countries between the Northern and Southern Hemispheres. Examples of this relationship include, Niger, Libya, and Western Sahara, which display the highest pulse clarity in the dataset\u0026mdash;a positive predictor of Happiness and Energy Arousal and a negative predictor of Sadness. Additionally, Zimbabwe's anthem scores exceptionally high in major mode, while New Zealand\u0026rsquo;s high spectral irregularity is associated with Happiness. Meanwhile, Brazil and Chile's frequent harmonic changes correspond to lower Sadness. Some of these regions, particularly those near the tropics, may benefit from more pleasant climates that enhance mood, encourage outdoor activities, and promote a more active, social lifestyle\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eWe found an Eastward increase in Valence and a decrease in Tension Arousal and Anger. This pattern is exemplified by the Japanese anthem, which, with its longer notes and minimal percussion, shows decreased spectral roughness (indicating high Valence and low Tension Arousal) and low spectral flux (linked to low Anger). Like Vanuatu's anthem, it also has very high key clarity (corresponding to high Valence and low Tension Arousal). Compared to the 19th-century Latin American epic anthems, more Eastern anthems \u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e (from Europe, Asia, Africa, and Oceania) are more varied in age and are often better characterized as odes, marches, and fanfares, with higher key clarity and more consistent tempo. However, interpreting these results is challenging and requires further investigation.\u003c/p\u003e \u003cp\u003eOur results show an eastward increase in Sadness. For instance, Eastern Hemisphere countries such as Japan and Cambodia head the list of countries having the lowest harmonic change, with this feature being a negative predictor of Sadness. Contributing to this result might be the use of anhemitonic musical scales in some East Asian anthems, such as the pentatonic scale in China and the \u003cem\u003eyo\u003c/em\u003e scale in Japan, reflecting the traditional music of these countries\u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e. The lack of half steps results in a more uncertain mode than the clearly major mode of other anthems. Also, Japan\u0026rsquo;s anthem ranks amongst the lowest in rhythmic clarity, a feature that contributes negatively to Sadness. It should be noted that these results have the lowest magnitude and do not align with other findings.\u003c/p\u003e \u003cp\u003eHofstede\u0026rsquo;s cultural dimensions can help further explain the connection between the emotions expressed in anthems and geographic coordinates. Specifically, countries with high Power Distance and collectivist tendencies tend to have more energetic anthems, reflecting a cultural emphasis on collective identity and hierarchical social structures. For instance, Ecuador and Panama\u0026mdash;highly collectivistic and power-distant countries\u0026mdash;feature anthems with clear rhythmic periodicity, frequent percussion, and high spectral entropy, flux, and brightness. These elements contribute to a more dynamic and forceful anthem, potentially reinforcing collective identity through energetic symbolism. Conversely, countries such as Denmark and New Zealand, which score high in Individualism and low in Power Distance, tend to have more subdued anthems. Their anthems are cantabile, with longer notes and less frequent percussion, creating a less energetic and tense atmosphere. These anthems could be seen as a reflection of individualistic values and egalitarian social structures, where personal autonomy is emphasized and hierarchical power distribution is minimized. It is worth noting that Power Distance is moderately negatively correlated with Individualism (r = -0.60, p\u0026thinsp;\u0026lt;\u0026thinsp;.001), linking to the concept of \u0026lsquo;vertical collectivism\u0026rsquo; \u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAnother relevant cultural dimension was Indulgence, which significantly correlated with the expression of Fear in national anthems. More broadly, Indulgence is negatively linked with Long-Term Orientation (r=-.45, p\u0026thinsp;\u0026lt;\u0026thinsp;.001) and with the Human Development Index (r = -0.31, p\u0026thinsp;\u0026lt;\u0026thinsp;.001), suggesting that countries with higher indulgence scores tend to experience greater political, economic, or social instability\u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e. This may help explain why nations such as Venezuela, Mexico, Puerto Rico, and El Salvador\u0026mdash;where Indulgence and short-term orientation are high\u0026mdash;also emphasize emotional intensity in their anthems. These anthems often feature more harmonic changes\u0026mdash;a positive predictor of Fear\u0026mdash;and increased chromaticism. In contrast, more stable countries like Sweden, which rank low in Indulgence, tend to have anthems with greater key clarity, which is a negative predictor of Fear.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec28\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Limitations\u003c/h2\u003e \u003cp\u003eWhile the study provides valuable insights into the emotional characteristics of national anthems, several limitations should be considered. First, our results should be interpreted merely as associations without implying any causality, and might be confounded by various unconsidered variables. Second, the study operates on the assumption that emotions modeled from musical features are universally perceived, which may overlook cultural differences in emotional interpretation, such as those found with regard to self-perceived well-being\u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e or to the perception of mode in chords and melodies\u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e. Additionally, the emotion modeling relies on ratings derived from a separate film soundtrack dataset, which may not perfectly align with the emotional characteristics of national anthems. Another limitation lies in the extracted musical features, as they may not capture all aspects of music that contribute to perceived emotions. Further, traditional Music Information Retrieval (MIR) techniques typically fail to describe certain musical elements, such as subtle variations in articulation, thematic development, gradual structural changes, and cross-modal associations.\u003c/p\u003e \u003cp\u003eApart from methodological limitations, cross-cultural comparisons face several challenges, including ethnocentrism of the researchers. The research team is composed predominantly of individuals who are White and influenced by Western academic traditions. We recognize that our cultural, academic, and socioeconomic backgrounds may shape our perspectives and interpretations, potentially introducing biases into our analysis. Further, with our results, we do not aim to make unwarranted overly simplified claims about specific nations or cultures contributing to stereotypes or prejudice. Especially the complexity of the concept of culture presents difficulties for cultural research, as culture is a multifaceted and nuanced construct with a constantly evolving nature. Accordingly, Hofstede\u0026rsquo;s theory has received criticism as well, regarding its overgeneralization and limited scope, static nature and Western bias\u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e. Furthermore, the history, creation and adoption of national anthems vary greatly between countries, influenced by historical context, political elites, and cultural diversity within nations. Additionally, while this analysis focused on the emotions reflected in an important national symbol, national anthems do not necessarily reflect the population\u0026rsquo;s current (or former) sentiment and identification with their national anthems. For instance, a marginalized group in Israeli society appears to view the anthem more negatively compared to mainstream Israelis\u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e. Further, singing the national anthem has sparked protests in some countries and criticisms related to (forced) patriotism\u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec29\" class=\"Section2\"\u003e \u003ch2\u003e4.4 Implications and future directions\u003c/h2\u003e \u003cp\u003eSince the aim of this study was to uncover associations using computational modeling, it can serve as a starting point for future research. Based on our computational findings, further studies might now include the viewpoints of specific nations to integrate the voice of the culture and nation itself. For instance, the sentiment toward one\u0026rsquo;s own national anthem could vary substantially among its own population (ranging from nationalism and pride over indifference to discontent and rage). It might therefore be worthwhile to conduct studies based on self-report data about the sound and emotions directly evoked by the anthem. Further, experimental research could manipulate the musical features of anthems to examine their direct emotional impact. In addition, based on the cultural (and often historical) overlap and similarity within regions of different nations, it might be interesting to focus on the investigation of anthems in specific continents or regions.\u003c/p\u003e \u003cp\u003eFurthermore, future research could further explore emotions expressed by national anthems by integrating musical analysis and lyric analysis through advanced computational methods such as latent semantic analysis (LSA), deep learning, and multimodal processing, allowing for deeper insights into the interplay between musical structure and lyrical semantics\u003csup\u003e\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e,\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e. Cross-cultural perception studies could investigate how individuals from different cultural backgrounds interpret and emotionally respond to different musical anthems, considering factors such as familiarity with the music and sentiment toward the countries. Further, temporal and historical analysis could provide insights into how political, social, and cultural shifts might have influenced the emotional content of national anthems and other politically significant music, examining e.g. how harmonic choices and rhythmic structures relate to musical trends and historical events.\u003c/p\u003e \u003cp\u003eOur study also has valuable implications for broader intercultural music research. While national anthems often evoke strong feelings of patriotism and unity, they can also be critiqued for promoting a nationalized worldview overshadowing global interconnectedness, and for legitimizing power and authority over a nation\u0026rsquo;s people. On the other hand, cultural music in general is deeply embedded in a nation's heritage, helping to preserve traditions and deepen a sense of community. Since the creation and adoption of national anthems are influenced by a variety of sociological, political and historical factors\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e, folk and traditional music might be more representative of the population itself in a nation. MER could therefore be used as a computational approach to examining and comparing musical differences in folk and traditional music as one possible reflection of a nation.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAUTHOR CONTRIBUTIONS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePetri Toiviainen:\u0026nbsp;\u003c/strong\u003eConceptualization, Methodology, Software, Formal analysis, Investigation, Data Curation, Writing - Original Draft, Writing - Review \u0026amp; Editing, Visualization, Supervision, Project administration, Funding acquisition.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMartín Hartmann:\u0026nbsp;\u003c/strong\u003eConceptualization, Writing - Original Draft, Writing - Review \u0026amp; Editing, Visualization.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFriederike Koehler:\u0026nbsp;\u003c/strong\u003eConceptualization, Writing - Original Draft, Writing - Review \u0026amp; Editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDATA AVAILABILITY\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe recordings of the national anthems were retrieved from https://nationalanthems.info/.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePredicted emotional characteristics for each country’s anthem are available at\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ehttps://osf.io/wz65t/files/osfstorage?view_only=77e7a80975774522879a67ad4b1cdfb4\u003c/p\u003e\n\u003cp\u003eScripts used for the analysis are available from the first author upon request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFUNDING\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe research was funded by the Research Council of Finland’s Centre of Excellence Programme (project number 346210).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCOMPETING INTERESTS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eCerulo, K. A. Symbols and the world system: National anthems and flags. \u003cem\u003eSociol. Forum\u003c/em\u003e. \u003cb\u003e8\u003c/b\u003e, 243\u0026ndash;271 (1993).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKolst\u0026oslash;, P. National symbols as signs of unity and division. \u003cem\u003eEthn. Racial Stud.\u003c/em\u003e \u003cb\u003e29\u003c/b\u003e, 676\u0026ndash;701 (2006).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCerulo, K. A. Sociopolitical Control and the Structure of National Symbols: An Empirical Analysis of National Anthems. \u003cem\u003eSoc. Forces\u003c/em\u003e. \u003cb\u003e68\u003c/b\u003e, 76\u0026ndash;99 (1989).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBillig, M. \u003cem\u003eBanal Nationalism\u003c/em\u003e (SAGE, 1995).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSlater, M. J., Haslam, S. A. \u0026amp; Steffens, N. K. Singing it for \u0026lsquo;us\u0026rsquo;: Team passion displayed during national anthems is associated with subsequent success. \u003cem\u003eEur. J. Sport Sci.\u003c/em\u003e \u003cb\u003e18\u003c/b\u003e, 541\u0026ndash;549 (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSanchez Adorno, S. Anthems in Middle Childhood: Negotiating Musical Experiences and National Identities. in Music, Words, and Nationalism: National Anthems and Songs in the Modern Era (eds Moreno-Luz\u0026oacute;n, J. \u0026amp; Nagore-Ferrer, M.) 229\u0026ndash;253 (Springer International Publishing, Cham, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/978-3-031-41644-6_11\u003c/span\u003e\u003cspan address=\"10.1007/978-3-031-41644-6_11\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNagore-Ferrer, M. National Anthems in the Nineteenth Century: Honour Anthems versus Revolutionary Anthems. in Music, Words, and Nationalism: National Anthems and Songs in the Modern Era (eds Moreno-Luz\u0026oacute;n, J. \u0026amp; Nagore-Ferrer, M.) 33\u0026ndash;56 (Springer International Publishing, Cham, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/978-3-031-41644-6_3\u003c/span\u003e\u003cspan address=\"10.1007/978-3-031-41644-6_3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSilaghi-Dumitrescu, R. Topics in National Anthems. \u003cem\u003eJ. Lang. Lit.\u003c/em\u003e \u003cb\u003e20\u003c/b\u003e, 288 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSilaghi-Dumitrescu, R. Trends in the texts of national anthems: A comparative study. \u003cem\u003eHeliyon\u003c/em\u003e \u003cb\u003e9\u003c/b\u003e, e19105 (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVoros, V. et al. Word Use and Content Analysis of the First Verses of Six National Anthems: A Transcultural Aspect of Suicidal Behaviour. \u003cem\u003ePsychiatr Danub\u003c/em\u003e. \u003cb\u003e28\u003c/b\u003e, 82\u0026ndash;85 (2016).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKyridis, A. et al. Nationalism through state constructed national symbols. The case of the national anthems. \u003cem\u003eInt. J. Interdiscip Soc. Sci.\u003c/em\u003e \u003cb\u003e4\u003c/b\u003e, 1\u0026ndash;14 (2009).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMutemererwa, S., Chamisa, V. \u0026amp; Chambwera, G. The national anthem: A mirror image of the Zimbabwean identity? \u003cem\u003eMuziki\u003c/em\u003e \u003cb\u003e10\u003c/b\u003e, 52\u0026ndash;61 (2013).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLester, D. \u0026amp; Gunn, J. F. National anthems and suicide rates. \u003cem\u003ePsychol. Rep.\u003c/em\u003e \u003cb\u003e108\u003c/b\u003e, 43\u0026ndash;44 (2011).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHasan, S. M. R. et al. Analyzing Musical Characteristics of National Anthems in Relation to Global Indices. Preprint at (2024). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.48550/arXiv.2404.03606\u003c/span\u003e\u003cspan address=\"10.48550/arXiv.2404.03606\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJuslin, P. N. \u0026amp; V\u0026auml;stfj\u0026auml;ll, D. Emotional responses to music: the need to consider underlying mechanisms. \u003cem\u003eBehav. Brain Sci.\u003c/em\u003e 31, 559\u0026ndash;575; discussion 575\u0026ndash;621 (2008).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEvans, P. \u0026amp; Schubert, E. Relationships between expressed and felt emotions in music. \u003cem\u003eMusic Sci.\u003c/em\u003e \u003cb\u003e12\u003c/b\u003e, 75\u0026ndash;99 (2008).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEerola, T. \u0026amp; Vuoskoski, J. K. A comparison of the discrete and dimensional models of emotion in music. \u003cem\u003ePsychol. Music\u003c/em\u003e. \u003cb\u003e39\u003c/b\u003e, 18\u0026ndash;49 (2011).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEkman, P. Are there basic emotions? \u003cem\u003ePsychol. Rev.\u003c/em\u003e \u003cb\u003e99\u003c/b\u003e, 550\u0026ndash;553 (1992).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRussell, J. A. A circumplex model of affect. \u003cem\u003eJ. Pers. Soc. Psychol.\u003c/em\u003e \u003cb\u003e39\u003c/b\u003e, 1161\u0026ndash;1178 (1980).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchimmack, U. \u0026amp; Grob, A. Dimensional models of core affect: A quantitative comparison by means of structural equation modeling. \u003cem\u003eEur. J. Personal\u003c/em\u003e. \u003cb\u003e14\u003c/b\u003e, 325\u0026ndash;345 (2000).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZentner, M., Grandjean, D. \u0026amp; Scherer, K. R. Emotions evoked by the sound of music: characterization, classification, and measurement. \u003cem\u003eEmot. Wash. DC\u003c/em\u003e. \u003cb\u003e8\u003c/b\u003e, 494\u0026ndash;521 (2008).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJuslin, P. N. From everyday emotions to aesthetic emotions: Towards a unified theory of musical emotions. \u003cem\u003ePhys. Life Rev.\u003c/em\u003e \u003cb\u003e10\u003c/b\u003e, 235\u0026ndash;266 (2013).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eG\u0026oacute;mez-Ca\u0026ntilde;\u0026oacute;n, J. S. et al. Music Emotion Recognition: Toward new, robust standards in personalized and context-sensitive applications. \u003cem\u003eIEEE Signal. Process. Mag\u003c/em\u003e. \u003cb\u003e38\u003c/b\u003e, 106\u0026ndash;114 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchedl, M., G\u0026oacute;mez, E. \u0026amp; Urbano, J. Music Information Retrieval: Recent Developments and Applications. \u003cem\u003eFound. Trends\u0026reg; Inf. Retr.\u003c/em\u003e \u003cb\u003e8\u003c/b\u003e, 127\u0026ndash;261 (2014).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang, Y. H. \u0026amp; Chen, H. H. Machine Recognition of Music Emotion: A Review. \u003cem\u003eACM Trans. Intell. Syst. Technol.\u003c/em\u003e \u003cb\u003e3\u003c/b\u003e (1\u0026ndash;40), 30 (2012).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMouriquand, D. Is Oscar-winning composer Hans Zimmer reimagining Saudi Arabia\u0026rsquo;s national anthem? \u003cem\u003eEuronews\u003c/em\u003e (2025).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAgence France-Presse. Saudi Arabia asks Hans Zimmer to rework national anthem. \u003cem\u003eGuardian\u003c/em\u003e (2025).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHofstede, G. \u003cem\u003eCulture\u0026rsquo;s Consequences: International Differences in Work-Related Values\u003c/em\u003e (SAGE, 1980).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHofstede, G. Dimensionalizing Cultures: The Hofstede Model in Context. \u003cem\u003eOnline Read. Psychol. Cult.\u003c/em\u003e \u003cb\u003e2\u003c/b\u003e, (2011).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMinkov, M. \u0026amp; Kaasa, A. Do dimensions of culture exist objectively? A validation of the revised Minkov-Hofstede model of culture with World Values Survey items and scores for 102 countries. \u003cem\u003eJ. Int. Manag\u003c/em\u003e. \u003cb\u003e28\u003c/b\u003e, 100971 (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNational Anthems.info. WWW (n.d.).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEerola, T. Music and emotion stimulus sets consisting of film soundtracks. \u003cem\u003eOSF\u003c/em\u003e (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLartillot, O., Toiviainen, P. \u0026amp; Eerola, T. A Matlab Toolbox for Music Information Retrieval. in \u003cem\u003eData analysis, machine learning and applications\u003c/em\u003e vol. 35 261\u0026ndash;268Springer, (2008).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTibshirani, R. Regression shrinkage and selection via the Lasso. \u003cem\u003eJ. R Stat. Soc. Ser. B-Methodol\u003c/em\u003e. \u003cb\u003e58\u003c/b\u003e, 267\u0026ndash;288 (1996).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGreene, C. A. et al. The Climate Data Toolbox for MATLAB. \u003cem\u003eGeochem. Geophys. Geosyst.\u003c/em\u003e \u003cb\u003e20\u003c/b\u003e, 3774\u0026ndash;3781 (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJuh\u0026aacute;sz, Z. Revealing Footprints of Ancient Sources in Recent Eurasian and American Folk Music Cultures Using PCA of the Culture-Dependent Moment Vectors of Shared Melody Types. \u003cem\u003eMusic Sci.\u003c/em\u003e \u003cb\u003e7\u003c/b\u003e, 20592043241228982 (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ede Alva, J. J. K. Colonialism and postcolonialism as (Latin) American mirages. \u003cem\u003eColon Lat Am. Rev.\u003c/em\u003e \u003cb\u003e1\u003c/b\u003e, 3\u0026ndash;23 (1992).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMuyolema, A. Colonialismo y representaci\u0026oacute;n: Hacia una relectura de los discursos latinoamericanista, indigenista, y clasista\u0026eacute;tnico en los Andes del siglo XX. (PhD dissertation. Department of Hispanic Languages and Literatures \u0026amp;#8230.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMayo-Harp \u003cem\u003eM. I. National anthems and identities: the role of national anthems in the formation process of national identities\u003c/em\u003e (Simon Fraser University, 2001).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKeller, M. C. et al. A Warm Heart and a Clear Head: The Contingent Effects of Weather on Mood and Cognition. \u003cem\u003ePsychol. Sci.\u003c/em\u003e \u003cb\u003e16\u003c/b\u003e, 724\u0026ndash;731 (2005).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDenissen, J. J., Butalid, L., Penke, L. \u0026amp; Van Aken, M. A. The effects of weather on daily mood: a multilevel approach. \u003cem\u003eEmotion\u003c/em\u003e \u003cb\u003e8\u003c/b\u003e, 662 (2008).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLawrance, E. L., Thompson, R., Le Vay, N., Page, J., Jennings, N. \u0026amp; L. \u0026amp; The Impact of Climate Change on Mental Health and Emotional Wellbeing: A Narrative Review of Current Evidence, and its Implications. \u003cem\u003eInt. Rev. Psychiatry\u003c/em\u003e. \u003cb\u003e34\u003c/b\u003e, 443\u0026ndash;498 (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCosgrove, S. Kimigayo and the Meiji Musical Aesthetic: A Comparative Study of Fenton and Hayashi\u0026rsquo;s Anthems. \u003cem\u003eJ. Joshibi Univ. Art Des.\u003c/em\u003e 25\u0026ndash;34 (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu, H., Jiang, K., Gamboa, H., Xue, T. \u0026amp; Schultz, T. Bell Shape Embodying Zhongyong: The Pitch Histogram of Traditional Chinese Anhemitonic Pentatonic Folk Songs. \u003cem\u003eAppl. Sci.\u003c/em\u003e \u003cb\u003e12\u003c/b\u003e, 8343 (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTriandis, H. C. \u0026amp; Gelfand, M. J. Converging measurement of horizontal and vertical individualism and collectivism. \u003cem\u003eJ. Pers. Soc. Psychol.\u003c/em\u003e \u003cb\u003e74\u003c/b\u003e, 118 (1998).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGaygısız, E. How are cultural dimensions and governance quality related to socioeconomic development? \u003cem\u003eJ. Socio-Econ\u003c/em\u003e. \u003cb\u003e47\u003c/b\u003e, 170\u0026ndash;179 (2013).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAyuso-Mateos, J. L. et al. Multi-Country Evaluation of Affective Experience: Validation of an Abbreviated Version of the Day Reconstruction Method in Seven Countries. \u003cem\u003ePLOS ONE\u003c/em\u003e. \u003cb\u003e8\u003c/b\u003e, e61534 (2013).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSmit, E. A., Milne, A. J., Sarvasy, H. S. \u0026amp; Dean, R. T. Emotional responses in Papua New Guinea show negligible evidence for a universal effect of major versus minor music. \u003cem\u003ePLOS ONE\u003c/em\u003e. \u003cb\u003e17\u003c/b\u003e, e0269597 (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSignorini, P., Wiesemes, R. \u0026amp; Murphy, R. Developing alternative frameworks for exploring intercultural learning: a critique of Hofstede\u0026rsquo;s cultural difference model. \u003cem\u003eTeach. High. Educ.\u003c/em\u003e \u003cb\u003e14\u003c/b\u003e, 253\u0026ndash;264 (2009).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGilboa, A. \u0026amp; Bodner, E. What are your thoughts when the national anthem is playing? An empirical exploration. \u003cem\u003ePsychol. Music\u003c/em\u003e. \u003cb\u003e37\u003c/b\u003e, 459\u0026ndash;484 (2009).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSteinfeld, J. Blurring the lines: National anthems are back in fashion. Why and where are people being forced to sing against their will? \u003cem\u003eIndex. Censorsh.\u003c/em\u003e \u003cb\u003e46\u003c/b\u003e, 114\u0026ndash;116 (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGreer, T., Singla, K., Ma, B. \u0026amp; Narayanan, S. Learning Shared Vector Representations of Lyrics and Chords in Music. in \u003cem\u003eICASSP \u0026ndash;2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)\u003c/em\u003e 3951\u0026ndash;3955 (2019). (2019). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1109/ICASSP.2019.8683735\u003c/span\u003e\u003cspan address=\"10.1109/ICASSP.2019.8683735\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSaari, P. \u0026amp; Eerola, T. Semantic Computing of Moods Based on Tags in Social Media of Music. \u003cem\u003eIEEE Trans. Knowl. Data Eng.\u003c/em\u003e \u003cb\u003e26\u003c/b\u003e, 2548\u0026ndash;2560 (2014).\u003c/span\u003e\u003c/li\u003e\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":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"National Anthems, Music and Emotion, Computational Music Analysis, Geographical Patterns, Cultural Dimensions, Music Information Retrieval (MIR)","lastPublishedDoi":"10.21203/rs.3.rs-6318443/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6318443/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eNational anthems serve as powerful symbols of national identity, often evoking strong emotional responses. While prior research has examined anthem lyrics, the emotional content of their musical features remains underexplored. This study employs computational modeling to predict the perceived emotional characteristics of 176 national anthems and investigates geographical and cultural variations. Using perceptual data from a prior study and musical features extracted with the MIR Toolbox, we trained LASSO regression models to predict eight emotional characteristics: Valence, Energy Arousal, Tension Arousal, Happiness, Sadness, Tenderness, Anger, and Fear. The predicted emotions were analyzed for continental differences, correlated with latitude and longitude, and compared to Hofstede\u0026rsquo;s cultural dimensions. The results revealed significant geographic trends, with Valence lower in the Americas and Energy Arousal higher near the equator. Fear and Tension Arousal were more pronounced in the Americas, while Happiness was highest in Oceania. Cultural analyses indicated that hierarchical societies exhibited more energetic anthems, individualistic cultures had less tense but more tender anthems, and indulgent societies expressed greater Fear. These findings highlight the role of musical features in shaping anthem emotions and underscore the potential of computational approaches for large-scale music-emotion research.\u003c/p\u003e","manuscriptTitle":"The emotional geography of national anthems","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-09 18:57:28","doi":"10.21203/rs.3.rs-6318443/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-05-12T14:35:39+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-27T14:33:11+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-25T06:21:36+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-23T23:32:51+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-21T00:16:46+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"196082319589866312320357017349021181395","date":"2025-04-15T22:21:45+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"259001066798379804015183073942893717856","date":"2025-04-15T03:26:42+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"169846037024978856307694071645863780371","date":"2025-04-14T15:57:38+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"227095912192534264611794605183747202998","date":"2025-04-07T11:08:00+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-04-07T10:36:36+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-04-07T10:32:49+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-04-07T09:49:06+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-04-05T09:05:27+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-03-27T08:19:15+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"09f0786f-43be-4bff-9178-79e93ed9b80a","owner":[],"postedDate":"April 9th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":46873202,"name":"Biological sciences/Psychology/Human behaviour"},{"id":46873203,"name":"Physical sciences/Mathematics and computing/Computer science"}],"tags":[],"updatedAt":"2025-07-07T16:00:33+00:00","versionOfRecord":{"articleIdentity":"rs-6318443","link":"https://doi.org/10.1038/s41598-025-08956-6","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-07-02 15:57:11","publishedOnDateReadable":"July 2nd, 2025"},"versionCreatedAt":"2025-04-09 18:57:28","video":"","vorDoi":"10.1038/s41598-025-08956-6","vorDoiUrl":"https://doi.org/10.1038/s41598-025-08956-6","workflowStages":[]},"version":"v1","identity":"rs-6318443","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6318443","identity":"rs-6318443","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}