Conference Agenda

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.