Populism is one of the most frequently discussed concepts in explaining contemporary global politics. While it lacks a strong ideological foundation, populism gains influence by merging with various sociopolitical issues and ideologies. Political sociology and social psychology theories emphasize that political attitudes are not the result of a single factor but rather a multidimensional outcome of dynamic social structures and psychological elements. This suggests that populist attitudes are also shaped by such complex influences. However, existing research on populism primarily focuses on the relationship between political ideology and psychological attitudes, with studies largely limited to the U.S. and Europe.

This study seeks to expand the scope of populism research by examining its multidimensional nature in South Korea, where related studies remain scarce. Using network analysis, this study examines how populist attitudes in South Korea connect with identity shaped by key social cleavages (ideology, security, class, and gender), political issue positions, and psychological dispositions. Through this approach, we systematically map the belief system underlying Koreans’ populist attitudes. Additionally, considering generational conflict as a major dividing factor in Korean society, we explore how populist belief systems vary across age groups.

To achieve this, we analyze data from the 2022 Political Perception Survey (PPS) and employ Explanatory Graph Analysis (EGA), a method widely used in recent psychological research on belief systems. By providing a comprehensive mapping of South Koreans’ populist attitudes, this study contributes to a deeper understanding of populism beyond Western contexts, offering nuanced insights into its political and sociological implications.

Biographical events are known to disrupt personal networks, but public events – such as the Covid-19 crisis and the policy decisions it prompted – also have a significant impact on these networks. On can hypothesize a connection between individuals' opinions and practices regarding public health measures during the Covid-19 crisis, their social characteristics, the nature of their personal relationships and the emergence of conflicts with specific individuals. Do differing views on public health measures generate tensions within social circles? How do these conflicts play out in different networks? Can these tensions be seen as a new form of social capital, shaped by individuals' social characteristics and relationships? Based on data from the "Life in Lockdown" (VICO) project, a longitudinal survey funded by the French National Research Agency, this study examines various variables, including opinions on vaccination, social characteristics (e.g., gender, age, income, education), types of sociability (family-oriented vs. friendship-oriented), and changes in personal relationships after the lockdowns. Particular attention will be given to interpersonal conflicts that led to broken ties and the success or failure in re-establishing these ties one year later.

Despite the soaring importance of political hostility, little is known about how affective attitudes are interrelated with other salient political values and opinions within a broad political belief system. This study aims to understand how tangible political attitudes align with affective hostility across various social groups based on different identities in American politics. Three key foci arise to do this: affects, stability, and heterogeneity—emotional elements, variability over time, and social group differences in belief systems, respectively.

The theoretical framework addresses two emotional dimensions shaping political hostility. First, affective intelligence theory (Marcus, Neuman, and Mackuen 2000) focuses on emotional responses influenced by political leaders, distinguishing between dispositional (habit-driven enthusiasm) and surveillance (anxiety-driven reconsideration) systems. Second, group stereotype theory (Fiske, Cuddy, and Glick 2007), anchored in social identity theory (Tajfel 1981), emphasizes everyday emotional dynamics based on group perceptions. This model evaluates groups on warmth (trustworthiness) and competence (effectiveness), highlighting everyday prejudice and stereotypes.

The data comes from American National Election Studies (ANES), the most comprehensive nationally representative survey dataset consistently measuring emotional reactions toward political figures and social groups since the 1950s. Thirteen post-election waves from 1984 to 2024 are analyzed. This paper empirically uses emotional evaluations of presidential candidates—anger, fear, hopefulness, pride—across election periods and feeling thermometers toward various social groups, such as ideologues, economic classes, races/ethnicities, religions, and sexualities.

Using belief network analysis, this study draws on American National Election Studies data from 1984 to 2024. Belief network analysis conceptualizes a belief system in which individual attitudes are structured as networks (Boutyline and Vaisey 2017). Several measures of centrality and clusteredness within belief networks are investigated to identify structural aspects both cross-sectionally and longitudinally. This method offers three advantages: identifying central beliefs through network centrality, analyzing relationality between beliefs to examine cognitive interdependence, and revealing structural equivalence through modularity, illuminating clustered alignment of political attitudes central to polarization research. In so doing, I track the changes in centrality and modularity within belief networks to understand better how affective dimensions have had a key role in belief alignment.

The analysis categorizes key variables into three groups: (1) Affective Components: Emotional reactions toward presidential candidates and social groups. (2) Issue Positions: Attitudes on civil rights, economic policy, and immigration. (3) Social Identities: Subjectively perceived identities and objective socio-demographic characteristics. Overall, this study aims to clarify how affective dimensions shape political belief systems, providing novel insights into affective polarization and the social foundations of political attitudes in contemporary American politics.

(Reference)

Boutyline, Andrei, and Stephen Vaisey. 2017. "Belief network analysis: A relational approach to understanding the structure of attitudes." American journal of sociology 122(5): 1371-1447.

Fiske, Susan T., Amy JC Cuddy, and Peter Glick. 2007. "Universal dimensions of social cognition: Warmth and competence." Trends in cognitive sciences 11(2): 77-83.

Marcus, George E., W. Russell Neuman, and Michael MacKuen. 2000. Affective intelligence and political judgment. University of Chicago Press.

Tajfel, Henri. 1981. Human Groups and Social Categories: Studies in Social Psychology. New York: Cambridge University Press.

Scholars have increasingly begun to examine beliefs and belief change through systemic models, where group level belief systems are derived from and describe the propensities of what individuals think about the world. However, few studies examine the effects of belief system structures on belief change processes. Establishing a link between supraindividual organization of belief systems and individual level change is key to understanding the relational dependencies of ideological systems and the individuals who constitute those systems. I use a networks-based approach to examine 161 survey questions across a broad array of policy areas in the 2006-2014 overlapping General Social Survey (GSS) panels. I weight the survey by inverse density, model the surveys as weighted bipartite networks and assess them individually within each survey wave. Each time period is processed independent of all other time periods. I produce a bipartite projection of survey responses, and use this bipartite projection to cluster respondents into inductively communities using the Leiden community detection algorithm with modularity maximization. This groups respondents into clusters based only on their belief containing survey responses. In each independent period, I then subset the unique detected communities and produce belief networks of their survey responses to represent their unique belief system. Within each belief network, I calculate density, density of belief modules, core-periphery structures, network autocorrelation, and network autocorrelation of belief modules represent structures through which the belief system exerts its influence and pulls individuals into alignment with its underlying ideal values across 20 different belief areas. I use these network measures within multilevel, multivariate panel models to assess whether individuals move towards or away from their belief system means over time. My results show that while the network attributes of modular structures have little relationship with belief change trajectories, core-periphery structures, density, and autocorrelation are associated with significant levels of change towards the belief system mean across the areas of racial attitudes, religion, abortion, suicide, and LGBT rights. Critically, while individual level change is relatively uncommon, these are policy areas where individual level change is in fact observable within the GSS. The stronger the network connections, the more individuals change towards the mean value of their belief system. These results shed light onto a part of the process of the mutual constitution of individuals and the belief systems they both exist within and create. The supraindividual structure of the belief system can act as a driver of individual level processes of people changing their mind.

Abstract: Understanding the everyday meanings of happiness and unhappiness, which are inherently polysemic and vary by individual context, is crucial for valid measures of these emotional states, yet research is scant. This study addresses this gap by constructing semantic networks based on data from a mini-snowballing word association task involving over 2,500 participants. In this task, individuals listed five words associated with 'happiness' or 'unhappiness', and subsequently named three associated words for each. Through network construction and community detection from these associations, we identified sub-concepts underlying happiness and unhappiness. Key sub-concepts of happiness included Peacefulness, Joyness, Friends, and Play, while those for unhappiness included Stress, Loneliness, Poverty, Conflict, and Disaster. Further analysis examined how demographic and socio-economic statuses influence conceptualizations of these states. Regression analyses with sub-concepts as dependent variables and demographic and socio-economic variables as predictors showed variations in perceptions of happiness and unhappiness based on gender and education level. Our findings illustrate how demographic factors shape the semantic schema of emotional states and highlight the complexity of understanding happiness and unhappiness across different social groups. This study contributes to the broader discourse on happiness and unhappiness by mapping the diverse ways people interpret these fundamental human experiences.

This work in progress examines the evolution of belief networks in the United States of America from the 1970s to 2022 using longitudinal data from the General Social Survey. By applying both Statistical Entailment Analysis (SEA) pioneered by Douglas White and the algebraic belief models of Martin & Wiley, we map how population-level belief networks of political, religious, and social attitudes transform over time. Following Butts and Hilgeman's approach to inferring memetic structure from cross-sectional data, we decompose observed behavioral characters into latent "microbeliefs" or "quasi-memes" that reveal underlying connections between seemingly disparate attitudes.

Preliminary findings reveal increasing polarization in belief networks on issues like abortion, with formerly nuanced combinations of microbeliefs present at the aggregate population level becoming more polarized and calcified into "all-or-nothing" positions. This contrasts with the stability observed in religious belief structures during the 1988-1998 period, suggesting differential trajectories across belief domains. Our analysis reveals complex structures that are reducible neither to distinct scales nor to models of itemwise independence, but rather form interlocking scale-like structures that evolve over time.

This approach conceptualizes beliefs as interconnected systems made up of individuals sharing complexes of microbeliefs, allowing us to work toward identifying distinct trajectories of "belief migration" and explore demographic variables that may be driving these population mental model changes in the US. By examining large, representative samples, we can investigate large-scale memetic ecologies and their evolution. Our research contributes to understanding how collective mental models evolve and how belief networks reconfigure in response to broader social changes.

This study presents findings from the project “Behavioural Change: Perspectives for the Stabilization of Sustainable Behaviours,” funded by the Università Cattolica del Sacro Cuore. Two surveys were administered to representative samples of over 2,000 Italians—first in June 2021 (when COVID had ended but its impact lingered) and again in June 2023 (when pre-pandemic routines had partially resumed).

We examined Italians’ Habits, Attitudes, and Behaviours (HAB) using Social Network Analysis to explore the correlations among approximately 50 variables (i.e. nodes) related to daily online and offline routines, sustainable practices, attitudes toward technology and pro-sociality. Our analysis detects the network structure of these HABs, focusing on layers possibly causing differences in HABs structures, i.e. gender and generations (i.e. Baby Boomers, Generation X and Generations Y and Z).

Key findings reveal that 2021 survey indicated stable HABs, with increased reliance on technology for work and leisure. The most central and strategic (measured as betweenness) HABs differ according to layers. For example, for females, sustainable habits and video chats with friends and relatives are central, while home cooking and socialising activities are more strategic in males’ networks of HABs. In 2023 the most central HABs shifted for both genders, with in-persons interactions taking a more central and strategic role, while technology-driven HABs became less central.

According to generations layers both the 2021 and 2023 surveys show different structures, with older generations changing less and younger changing more, especially in technology-related HABs, though the latter group also experienced increased anxiety and psychological distress.

Recent research has advanced cultural network analysis to map out cultural schemas held by individuals by measuring the correlations or relationality between beliefs in nationally representative surveys. However, longitudinal analysis of belief spaces is largely limited because not all beliefs were repeatedly asked over time. Since the survey questions asked multiple times are more likely to be politically charged, belief spaces constructed in this manner will likely exclude non-political and non-contentious beliefs. Our study aims to address this gap by fine-tuning large language models with the General Social Survey (GSS) from 1972 to 2021. Specifically, we analyze the latent individual belief embeddings trained during the fine-tuning process to examine the patterns of cultural belief spaces across 3,110 opinions among 68,846 individuals. Our initial analysis shows that the cultural divide between liberals and conservatives has widened, with liberals moving further to the left, whereas conservatives have maintained similar positions in the belief space from 1972 to 2021 in the GSS. We show how Americans’ cultural belief spaces are structured by socio-demographic characteristics and partisanship over time.

Recent studies on polarization have been shifting focus from elites to the mass public and from a unidimensional perspective of “polarization intensity” to a multidimensional framework encompassing both intensity and breadth. Belief Network Analysis has been applied to quantify these two dimensions. However, cross-national comparisons of opinion polarization remain limited, despite their increasing relevance in an era of deglobalization. Furthermore, the politically embedded societal context of a country may shape opinion polarization in specific ways, yet research in this area remains underexplored due to the scarcity of cross-national comparisons, which are essential for examining political influences on opinion polarization. In terms of BNA measurement, existing network indicators often reflect structural complexities, making it difficult to distill them into two unified dimensions, thereby hindering cross-national comparability.

This study utilizes the latest WVS/EVS datasets to construct belief networks for each surveyed country and employs principal component analysis to integrate network indicators into two unified polarization dimensions: the Global Concentration Index (GCI) for polarization intensity and the Universality Index (UI) for polarization breadth. We hypothesize that political environments shape polarization through top-down party competition, bottom-up civic engagement, and corresponding political-societal values. Using OLS and fixed-effects models, we find that polarization intensity is positively influenced by party competition and secular values, while the effect of party competition on polarization breadth follows a U-shaped curve. Civic engagement and self-expression values exhibit no significant impact on opinion polarization. These findings contribute to a deeper understanding of cross-national variations in opinion polarization and their institutional determinants.

This project uses a network approach to study the mental models and social learning in a small groups of stakeholders involved in a unique participatory experiment in collaborative rangeland management. Participants included traditional ranchers running an economic enterprise, conservation rangeland managers who use grazing to pursue economic and environmental goals, and government agency employees who managing public grazing programs. For two different day-long experimental sessions, these stakeholders were divided into four groups and asked to deliberate about the management of public land. Their mental models of rangeland management were measured by asking them to link their management goals to the practices that should be used to achieve the goals. This results in a bipartite network for each group, which we analysed before and after group discussion to measure social learning using temporal ERG models. We find that the most change and ‘learning’ occurred not in adding new goals and methods, but adding new relationships between the goals and methods respondents had previously mentioned. Additionally, in two of the groups, members added linkages that made their mental models significantly more similar to other group members.

Causal loop diagrams (CLDs) are an increasingly popular and flexible technique that empowers us to understand the behavior of agents or factors to explain complex behavior. It is now extensively used in the social science domain since it is a tool that allows us to map attitudes and concepts and the relationships between them. These diagrams are networks; hence, their analysis benefits from the employment of tools already established in network analysis.

Several techniques can be used to construct causal loop diagrams. However, most require qualitative data collection where stakeholders are invited to identify crucial factors to represent the system and the causal relationship between them.

This study introduces a methodological contribution focused on improving the accuracy of diagram construction. The qualitative data collection and the practice of merging more than one diagram into a finalized one might be the source of several biases in the finalized diagram. For instance, a group of stakeholders might be too influential in the depiction of the system, and another group might not be represented enough. This work introduces an application of Exponential Random Graph Models (ERGMs) to systematically appraise potential biases in the diagram formalized as a network. This methodology enables researchers to explore the system’s structure and identify the underlying dynamics that led to the construction of the system under examination. The method has implications for understanding the attitudes and constructs depicted in the system.

Background: Advocacy coalitions consist of individuals who share beliefs and often act together. They have become increasingly important in community health to influence health practices. However, social network analysis has been underutilized in studying such coalitions due to the challenges of collecting whole network data across multiple settings. This paper demonstrates the use of ego-network analysis to infer coalition structures.

We apply this approach to tobacco prevention in schools. School staff are sometimes reluctant to enforce non-smoking rules, partly because they believe their colleagues do not prioritize this issue. As part of the ADHAIRE smoking prevention project, we examine homophily among teachers regarding smoking status and attitudes toward smoking prevention.

Method: We collected ego networks from 537 teachers across 20 secondary schools in a deprived Belgian province with high smoking rates. Teachers named up to five colleagues from whom they sought advice (average alters=4.7). Exponential random graph modeling (ERGM) was applied to assess homophily.

Results: We found significant homophily in attitudes toward smoking prevention (nodematch=0.525, stderr=0.008). Teachers opposed to stronger prevention measures exhibited greater homophily (nodematch=15.3) compared to those supporting more prevention (nodematch=0.33).

Conclusion: Teachers cluster into distinct coalitions based on their prevention attitudes. Opposition to prevention is particularly cohesive, weakening collective enforcement of smoking rules. Ego-network analysis offers a feasible and valuable approach to study coalition dynamics in school settings, highlighting the need to consider social divisions when implementing school-based prevention policies.

Ideological polarisation is typically understood as differences in beliefs about societal issues. We propose that polarisation can manifest not just at the level of beliefs, but at the level of how individuals perceive belief relations. A person's beliefs are embedded and structured within a broader belief system—their personal belief network. Beliefs that are not aligned with the rest of the personal network create dissonance. Previous research has explored how dissonance induces belief change (node values) but has neglected how individuals update their perceptions of the relationships between the beliefs (edge weights). We present a model of belief network formation at the individual level based on two psychological drivers: social conformity—where observing the beliefs of others influences personal belief network edges—and internal coherence—where an individual weakens/strengthens edges between dissonant/coherent beliefs. By applying this model to panel data on the political beliefs of German, Dutch, and Austrian citizens, we infer the dynamics of individuals' personal belief networks. The model predicts which beliefs are most central in individuals' networks, depending on the interplay between social and internal pressures. Our findings suggest that personal belief networks have become more interconnected and ideological in recent years, and that the average networks of political groups have diverged, reinforcing partisan divisions. Individuals polarise not only in terms of their beliefs but also more fundamentally in terms of how they structure these beliefs and what they perceive as coherent. This aspect of polarisation can widen ideological divides and undermine social cohesion.

This project seeks to map the dimensional structure underlying contemporary belief clusters in the US. Literature on American public opinion has long debated the extent to which the public is ideologically consistent. While understandings of American ideology are overwhelmingly operationalized with a liberal-to-conservative spectrum, scholars as well as recent polling data show that a plurality of Americans seem to fall outside of this dichotomy. Some recent research has then turned to belief networks and correlational analyses to understand opinion co-occurrence, without being restricted by a single-dimension ideology axis. However, this work often pre-selects highly polarized opinion areas. This can be useful to track trends in polarization, but it also artificially restricts the belief heterogeneity that can be measured. On the other hand, some recent studies use a large array of belief items, but focus on the trajectory of belief clustering (e.g., density and modularity) over time. Instead, my project focuses on the ideological contents of co-occurring beliefs. I use General Social Survey data from 2022, including all opinion questions, to inductively assess the latent dimensions organizing belief systems in the American public. I use a combination of belief network analysis – employing weighted ties to represent pair-wise absolute correlations between belief nodes – with dimensionality reduction techniques to map clusters of co-occurring beliefs and interpret the latent dimensions that organize them. The paper explores how a multidimensional ideological structure (particularly for the plurality of Independents) complicates some assumptions in works on ideological consistency and current polarization.

This study aims to explore the role of AI-generated ethical scenarios in values education with a focus on the students with different types of social networking. It attempts to divide the young students into the AI users of two types: one featured with inward-looking social networking while another with outward-looking social networking. The former is what this study described as bonding AI users while the latter as bridging AI users. Researcher would then look into whether and how these different AI users achieve certain acceptance of the positive and core values; for example, respect for others, commitment, integrity, empathy, and so on.

In order to examine the dynamic relationships between the different AI users and their formation of positive values, this study was designed to adopt qualitative research method of in-depth individual interview with the students. The research targets were Hong Kong’s students aged between 15 and 18, and interviewees comprised a total of 30 students from 5 different schools. It was generally found that by using AI-generated ethical scenarios, the bridging users could more effectively operationalize the conceptual notion of positive values as various daily-life and practical issues for their understanding. On the other hand, the bonding users could be more capable of making use of AI-generated ethical scenarios by going through the inevitable ethical reasoning and then making the decisions that were reflecting the positive and core values. Overall, this study is expected to shed some light on the innovative delivery of values education that could better address the individual needs of students with different types of social networking.

Fifty years after the introduction of the European patent, the European Unified Patent Court (UPC) was established in 2023 as a unified body for the litigation of validity and infringement cases for all the participating member states in Europe. Because the UPC integrates judges from different national jurisdictions who were trained in different legal regimes and cultures, and because the UPC’s 20 divisions are geographically distributed across the 18 member states, the new court faces the challenge of offering consistent and reliable case law to businesses from around the world. We examine the social mechanisms that promote the harmonization of national patent jurisprudence within this transnational institution. After one year of operation, we conducted a network survey on over 110 technical and legal judges at the UPC to explore the extent to which they had established personal contact, read each other’s decisions and legal commentaries, and had engaged in inter-personal deliberation about general aspects of patent law before and after their appointment to the court. The findings from an explorative network analysis inform a relational, neo-structural model of transnational institutionalization that is shaped by mechanisms of both variation and convergence. Both, the appellate process as well as the formation of judicial beliefs affect harmonization. Judicial beliefs are enforced through informal deliberation networks among judges, exchanges at convergence events, scholarly commentary in publications, and citations of precedent-setting rulings. Given its geographical dispersion, the UPC will rely on the organization of temporary proximity as well as dense interpersonal deliberation networks among judges to ensure consistent jurisprudence in the future.

Network analysis of mass belief systems has emerged as a powerful approach for studying public opinions. This methodology transforms correlation matrices derived from subjective survey questions—items measuring attitudes and beliefs—into weighted networks where beliefs denote nodes and correlation values signify tie strength. Such transformations of survey data into belief networks, however, capture only one projection of an inherently bipartite structure—where survey respondents and beliefs constitute two distinct modes connected through responses. Current research faces two critical limitations: the methodological constraint of analyzing only the belief network projection, and the predominant focus on Western democratic contexts. This study addresses both gaps through a comprehensive bipartite network analysis of the 2017 Chinese General Social Survey.

The dual-projection analysis reveals distinct structural patterns. In the belief network, Confucian values—particularly filial piety and traditional gender roles—demonstrate high betweenness centrality and strong inter-node connections, while political beliefs occupy peripheral positions with low centrality measures and weak ties to other beliefs. In the respondent network, we apply community detection algorithms to identify distinct opinion groups. Subsequent multinomial logistic regression reveals systematic associations between group membership and sociodemographic factors such as urban residency, gender, education levels, and age cohorts. Interestingly, central filial piety beliefs show minimal variation across detected communities, while traditional gender role beliefs exhibit significant differentiation.

This study advances belief network methodology by demonstrating how a bipartite, dual-projection analysis captures complementary structural dimensions of beliefs and people, while empirically documenting how local traditional values remain influential under the authoritarian context.