Note. UR: Under Review, WP: Working Paper, WIP: Work-In-Progress
LLMs, beliefs, and culture
[WIP] Leveraging Large Language Models For Analyzing Belief Space At Scale (with Junsol Kim).Abstract
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 will extend the analysis to investigate how Americans’ cultural belief spaces are structured by socio-demographic characteristics and partisanship over time.Abstract
How do people navigate a wide range of belief spaces? Conceptualizing a set of political attitudes as a network of correlations, this paper shows that major social events activate different core beliefs as cognitive heuristics to organize otherwise unassociated beliefs. Comparing belief networks from General Social Surveys during presidential election years from 2000 to 2016 to those from American National Election Studies, I find sharp differences in which belief lies at the core of political belief networks. In the ANES, liberal-conservative ideology occupies the center of belief networks. In contrast, the centrality of the gay rights issue has been highest in the GSS since 2008. Using a moving window approach based on survey timing, I show that the centrality of gay rights in the GSS peaked right after the major events related to gay rights such as the legalization of same-sex marriage, Pride Parade, and the Orlando shooting. These results suggest that the activation of core beliefs is context-dependent.
Abstract
Large language models (LLMs) that produce human-like responses have begun to revolutionize research practices in the social sciences. We develop a novel methodological framework that fine-tunes LLMs with repeated cross-sectional surveys to incorporate the meaning of survey questions, individual beliefs, and temporal contexts for opinion prediction. We introduce two new emerging applications of the AI-augmented survey: retrodiction (i.e., predict year-level missing responses) and unasked opinion prediction (i.e., predict entirely missing responses). Among 3,110 binarized opinions from 68,846 Americans in the General Social Survey from 1972 to 2021, our models based on Alpaca-7b excel in retrodiction (AUC = 0.86 for personal opinion prediction, ρ = 0.98 for public opinion prediction). These remarkable prediction capabilities allow us to fill in missing trends with high confidence and pinpoint when public attitudes changed, such as the rising support for same-sex marriage. On the other hand, our fine-tuned Alpaca-7b models show modest success in unasked opinion prediction (AUC = 0.73, ρ = 0.67). We discuss practical constraints and ethical concerns regarding individual autonomy and privacy when using LLMs for opinion prediction. Our study demonstrates that LLMs and surveys can mutually enhance each other’s capabilities: LLMs can broaden survey potential, while surveys can improve the alignment of LLMs.
[WIP] “Rethinking Demographic Prompts for Simulating Human Responses: Open-Ended Survey Responses Improve Large Language Model Predictions of Voting Behavior” (with Junsol Kim)
[WIP] “Am I the Asshole? The Danger of Cultural Homogenization in the Era of Generative AI” (with Junsol Kim)Abstract
Social norms guide interactions and behavior, with individuals often seeking advice to navigate ambiguous or sensitive situations. Increasingly, generative AI tools like large language models (LLMs) play a role in these cultural boundary-making processes, raising concerns about their impact on the diversity of social conventions. While LLMs enable diverse individual content creation, their reliance on frequent language patterns may homogenize beliefs at societal levels. This study examines the influence of LLMs on cultural diversity using the subreddit r/AmItheAsshole, where users seek judgments on personal dilemmas. We conducted a computational experiment by simulating LLM-generated responses to 489 questions, comparing these to nearly 4,000 human responses. Results show LLMs produced more sympathetic judgments, with lower proportions of blame (e.g., “You’re the Asshole”) and reduced diversity in sentiment, lexical, and semantic expressions. Even at higher randomness settings, AI responses remained less varied than human counterparts, reflecting a narrower emotional and intellectual spectrum. These findings suggest LLMs may unintentionally standardize cultural norms, prioritizing safety and positivity over the richness of human expression.
Mental health and suicide
[WIP] “A population-based study of suicide among gays and lesbians in the United States” (with Junsol Kim and Bernice Pescosolido).Abstract
Sexual minorities have higher rates of suicidal ideation and attempts than heterosexuals, but the risk of suicide remains underexplored due to missing sexual orientation data in death records. We conduct the first population-based study in the United States to estimate suicide rates and risk factors among gay and lesbian populations by combining suicide cases from the National Violent Death Reporting System (NVDRS) with living population data from the Behavioral Risk Factor Surveillance System from 2014 to 2021. The suicide rate among gays and lesbians was significantly higher at 34.33 per 100,000 compared to 18.91 per 100,000 among heterosexual individuals, revealing a substantial disparity (15.42 per 100,000 gap). This disparity persisted after adjusting for socio-demographic factors, including educational attainment and unemployment status. Notably, controlling for marital status reduced the association by half, which suggests the importance of policies promoting social integration in reducing suicide disparities within sexual minorities.
Abstract
While financial strain is known to be associated with suicide risk, it is difficult to establish a causal link between income and suicide. To fill this gap, we examine the long-term consequences of the Permanent Fund Dividend program approved by Alaska voters in 1976, distributing a substantial annual cash payment to all Alaska residents since 1982. We construct state-level suicide data from 1961 to 2020, which allows us to use a synthetic control model that requires sufficient preintervention information. We find that the establishment of the dividend program significantly reduced the suicide rate in Alaska for the first fifteen years. At the same time, we find that Alaska’s suicide rates increased significantly after 1990 compared to synthetic Alaska, which was not significantly correlated with the varying size of annual dividends from 1982 to 2020. These results together suggest that the cash transfer policy may affect suicide by enhancing collective efficacy.
Abstract
Despite long-standing evidence linking higher unemployment rates to increased suicide rates, a puzzling trend emerged in the United States after the Great Recession: suicide rates continued to rise even as unemployment rates declined. Drawing on theories of social networks and reference groups, we advance the concept of “sameness”—in this case, the extent to which an individual’s employment status aligns with the fate of others in one’s community—to clarify how unemployment rates influence suicide. Constructing a multilevel dataset of U.S. suicide deaths from 2005 to 2017, we find that while unemployed individuals face a higher risk of suicide compared to the employed, this gap diminishes in communities with high local unemployment rates. Moreover, the “sameness” effect extends beyond geographic contexts to temporal ones, as national unemployment spikes reduce suicide risk among the unemployed and diminish the importance of local sameness. Together, these findings suggest a mechanism of “situational awareness,” whereby local and national economic contexts shape the meaning of unemployment, shifting its interpretation from personal failure to system failure and reducing its stigma. Our article offers a novel framework for examining the effects of cross-level interactions in suicide research, highlighting the crucial role of culture as deeply intertwined with social network mechanisms in shaping contextual influence.
Abstract
Among deaths of despair, the individual and community correlates of US suicides have been consistently identified and are well known. However, the suicide rate has been stubbornly unyielding to reduction efforts, promoting calls for novel research directions. Linking levels of influence has been proposed in theory but blocked by data limitations in the United States. Guided by theories on the importance of connectedness and responding to unique data challenges of low base rates, geographical dispersion, and appropriate comparison groups, we attempt a harmonization of the National Violent Death Reporting System (NVDRS) and the American Community Survey (ACS) to match individual and county–level risks. We theorize cross-level sociodemographic homogeneity between individuals and communities, which we refer to as “social similarity” or “sameness,” focusing on whether having like-others in the community moderates individual suicide risks. While analyses from this new Multilevel Suicide Data for the United States (MSD-US) replicate several individual and contextual findings, considering sameness changes usual understandings of risk in two critical ways. First, high individual risk for suicide among those who are younger, not US born, widowed or married, unemployed, or have physical disabilities is cut substantially with greater sameness. Second, this moderating pattern flips for Native Americans, Alaska Natives, Asians, and Hispanics, as well as among native-born and unmarried individuals, where low individual suicide risk increases significantly with greater social similarity. Results mark the joint influence of social structure and culture, deliver unique insights on the complexity of connectedness in suicide, and offer considerations for policy and practice.
Abstract
Much literature on peer influence has relied on central tendency–based approaches to examine the role of peer groups. This article develops a distributional framework that (1) differentiates between the influence of depressive peers and that of a majority group of nondepressive peers; and (2) considers the multilayered nature of peer environments. The authors investigate which segments of the distribution of peer depressive symptoms drive peer effects on adolescent depression across different layers of peer groups. Results from the Add Health data show that, for institutionally imposed peer groups, exposure to depressive peers significantly increases adolescents’ depressive symptoms. For self-selected peer groups, the central tendency of peer depression largely captures its impact on adolescent depression. High parent-child attachment buffers the deleterious consequence of exposure to depressive grademates. The implications of these findings are discussed for research and policy regarding peer effects on adolescent well-being.
Agent-based models
2024 “Generative Agent-Based Models Powered by Large Language Models” Korean Journal of Sociology, 58(4): 151-188. PaperAbstract
Agent-based models (ABMs) have been widely used as a key research tool to demonstrate how micro-level interactions among agents give rise to the emergence of macro-level social structures, such as the spread of innovations, the dynamics of collective action, and the rise of political polarization. Despite their utility, questions have been raised about their relevance to the real world, as ABMs typically rely on a limited number of deductive assumptions about agents’ behavioral rules and environment settings. In response, recent advancements in large language models (LLMs) have opened new possibilities for inductively creating more realistic artificial societies by leveraging their remarkable ability to mimic human behavior and cognition. This paper aims to examine the potential and limitations of testing social theories in more realistic artificial societies constructed through generative ABMs powered by LLMs. Using Schelling’s segregation model as an example, I illustrate how ABMs built with LLMs differ from traditional ABMs designed deductively. I introduce three distinct approaches to ABMs based on different types of LLM agents–simple LLM agents, group LLM agents, and individual LLM agents– each representing varying levels of behavioral complexity, from basic rule-based actions to personalized, human-like decision-making. I discuss how these approaches can bridge the gap between deductive, abstract ABM models and inductive, realistic ABM models. Finally, I propose future research directions for the development of generative ABMs powered by LLMs.Abstract
How can real terrorist organizations – even those facing severe state repression – simultaneously maintain secrecy and their capacity for coordinated action? We demonstrate that very simple decision rules governing tie formation within terrorist organizations induce structures that are both robust to penetration and capable of sustained action and growth. An implication of this work is that the extant portrayals of terrorist groups – for example, the 9/11 terrorist network and the recent Islamic State terrorist network in Europe – are revealed to be at best partial, a discovery that helps account for the capacity of terrorist organizations to continue to operate even when significant numbers of their members are killed or captured. Implications for counter-terrorism strategy are discussed.Other projects
[WP] The Great Recession and Its Aftermath: Rising Inequality in the Sociology Academic Job Market from 2001 to 2023 (with Eehyun Kim, Anne Kavalerchik, and Koji Chavez)Abstract
This study examines the dynamics of stratification within the sociology job market from 2001 to 2023, with a particular focus on the post-Great Recession era. Using longitudinal data from the American Sociological Association’s Guide to Graduate Departments of Sociology and US News and World Report rankings, we analyze patterns of graduate outcomes, faculty transitions, and the impact of institutional prestige on PhD exchange networks. We find that the number of PhDs did not significantly change over time, but the proportion of PhDs who received a faculty job in sociology departments decreased dramatically after the Great Recession in 2008. Simultaneously, we identify the substantial increases in job market inequality, measured by the Gini Index, which coincided with the stronger consolidation of the status hierarchy after the Great Recession. These findings indicate that in scenarios when people compete for a limited number of jobs, the significance of institutional prestige becomes even more pronounced under conditions of heightened inequality. This study contributes to the understanding of how economic downturns and institutional prestige shape academic job markets, reflecting broader trends of inequality and the reinforcement of an academic caste system.Abstract
Vaccine hesitancy is a critical barrier to widespread vaccination uptake and containment of the COVID-19 pandemic. In the United States, vaccines have become politically polarized, with high rates of vaccine hesitancy observed among Republicans. In contrast to prior research focusing on partisan gaps, we investigate vaccination attitudes and uptake among a group overlooked in prior research: those who are eligible to vote but did not register in the presidential elections. Drawing on nationally representative and longitudinal survey data from April 2020 to October 2021, we show that this group – whom we call “political outsiders” – represents about 16% of the U.S. population. They had the lowest vaccination rate (47%) by 2021 October, significantly lower than Republican (65%), Independent (76%), and Democratic voters (88%). Further, we find that political outsiders are less likely to trust physicians compared to other partisan groups. Because the sources they trust differ from partisans, existing public health messaging may be less likely to reach them successfully. Finally, we find that political outsiders experience more socio-economic hardships and are less integrated into society. Hence, our results underscore the importance of targeted efforts to reach this highly vulnerable population.
Abstract
Recently, offspring sex has been widely used as a natural experiment and argued to induce changes in political orientation among parents. However, prior results have been contradictory: in the UK, researchers found that having daughters led to parents favoring left-wing political parties and to holding more liberal views on family/gender roles, whereas in the United States scholars found that daughters were associated with more Republican (rightist) party identification and more conservative views on teen sexuality. We propose and examine three plausible explanations to account for these puzzling results using data from the General Social Survey and the European Social Survey; contextual (period/country) differences, heterogeneous treatment effects, and publication bias. In an analysis of thirty-six countries, we obtain null effects of the sex of the first child on party identification as well as on political ideology while ruling out country heterogeneity. Further, we observe no evidence of other heterogeneous treatment effects based on the analysis of Bayesian Additive Regression Tree models. As a corrective to the source of publication bias, we here add comprehensive null findings to the polarized canon of significant results.
2016 “Robust Null Findings on Offspring Sex and Political Orientation” Social Forces 95(2): 899-908. Paper, Code and data
2016 “A Network Approach to Economic Models of Fertility” Rationality and Society 28(4):386-409. Paper, Code and dataAbstract
Since its first appearance in the late 1950s, the neoclassical economic theory of fertility, particularly as exemplified by Gary Becker’s model of household production function that assumes a unitary utility function of the household, has become one of the most popular paradigms with which to examine fertility changes. Recently, the bargaining model that assumes separate utility functions has emerged as a strong opponent to the original paradigm. This article provides network foundation to reconcile two competing economic paradigms. Our formal model predicts that the way in which separate utilities of couples are treated in their joint childbearing decisions depends on the network embeddedness of spouses (i.e. the intra-household network). If spouses are not embedded into each other’s networks, the assumption of the unitary utility function is no longer warranted, and their decision process follows the bargaining model. However, strongly embedded couples behave as if they share the common utility function, predicted by the Becker model. Our model prediction is supported by analysis of three waves of panel data, Korean Longitudinal Survey of Women and Families, collected in South Korea where a dramatic drop in the fertility rate is reported. We find that the wife’s bargaining power, measured by the income difference between couples, can exert its influence on having a new-born child only when couples’ intra-household networks are weakly embedded, whereas strongly embedded couples consistently maintain high fertility rates regardless of how much the wife earns. We conclude that social networks play a significant role in shaping how neoclassical economic models of fertility work and discuss its implication to the efforts enhancing the fertility rate.
Please send me an email (bklee@nyu.edu) if you do not have library access to any of my listed papers.
Social cohesion and ego-centric networks
[WIP] “Ingroup bias and othering process in close social ties: How Americans perceive the infection status of others during the COVID-19 pandemic” (with Kangsan Lee)Abstract
In times of crisis, social networks often tighten as individuals seek support, leading to homogeneity and the reinforcement of in-group bias and othering processes. This study investigates how ingroup bias and othering shaped perceptions of COVID-19 infection likelihood among close social ties. We conducted a nationwide online survey of 36,345 Americans from April 2020 to April 2021, collecting daily responses from over 2,100 counties. We found that individuals perceived their in-group members—across racial and partisan lines—as less likely to contract COVID-19, while out-groups were viewed as more likely. Moreover, the ingroup bias and othering processes were more pronounced in communities with high COVID-19 transmission. This paper highlights the paradoxical risks of homogeneity bias, where prioritizing in-group safety can inadvertently heighten infection risks within those same groups. Understanding these dynamics is crucial for addressing inequities in health outcomes during pandemics.
Abstract
The COVID-19 pandemic, with millions of Americans compelled to stay home and work remotely, presented an opportunity to explore the dynamics of social relationships in a predominantly remote world. Using the 1972–2022 General Social Surveys, we found that the pandemic significantly disrupted the patterns of social gatherings with family, friends, and neighbors but only momentarily. Drawing from the nationwide ego-network surveys of 41,033 Americans from 2020 to 2022, we found that the size and composition of core networks remained stable, although political homophily increased among nonkin relationships compared to previous surveys between 1985 and 2016. Critically, heightened remote communication during the initial phase of the pandemic was associated with increased interaction with the same partisans, although political homophily decreased during the later phase of the pandemic when in-person contacts increased. These results underscore the crucial role of social institutions and social gatherings in promoting spontaneous encounters with diverse political backgrounds.
Abstract
Close elections are rare, but most Americans have experienced a close election at least once in their lifetime. How does intense politicization in close elections affect our close relationships? Using four national egocentric network surveys during the 1992, 2000, 2008, and 2016 election cycles, I find that close elections are associated with a modest decrease in network isolation in Americans’ political discussion networks. While Americans are more politically engaged in close elections, they also are less likely to be exposed to political dissent and more likely to deactivate their kinship ties to discuss politics. I further investigate a potential mechanism, the extent of political advertising, and show that cross-cutting exposure is more likely to disappear in states with more political ads air. To examine the behavioral consequence of close elections within American families, I revisit large-scale cell phone location data during the Thanksgiving holiday in 2016. I find that Americans are less likely to travel following close elections, and that families comprised of members with strong, opposing political views are more likely to shorten their Thanksgiving dinner. These results illuminate a process in which politicization may “close off” strong-tied relationships in the aftermath of close elections.
Abstract
This study documents historical trends of size and political diversity in Americans’ discussion networks, which are often seen as important barometers of social and political health. Contrasting findings from data drawn out of a nationally representative survey experiment of 1,055 Americans during the contentious 2016 U.S. presidential election to data arising from 11 national data sets covering nearly three decades, we find that Americans’ core networks are significantly smaller and more politically homogeneous than at any other period. Several methodological artifacts seem unlikely to account for the effect. We show that in this period, more than before, “important matters” were often framed as political matters, and that this association probably accounts for the smaller networks.
Abstract
The 2004 General Social Survey (GSS) reported significant increases in social isolation and significant decreases in ego network size relative to previous periods. These results have been repeatedly challenged. Critics have argued that malfeasant interviewers, coding errors, or training effects lie behind these results. While each critique has some merit, none precisely identify the cause of decreased ego network size. In this article, we show that it matters that the 2004 GSS—unlike other GSS surveys—was fielded during a highly polarized election period. We find that the difference in network size between nonpartisan and partisan voters in the 2004 GSS is larger than in all other GSS surveys. We further discover that core discussion network size decreases precipitously in the period immediately around the first (2004) presidential debate, suggesting that the debate frames “important matters” as political matters. This political priming effect is stronger where geographic polarization is weaker and among those who are politically interested and talk about politics more often. Combined, these findings identify the specific mechanism for the reported decline in network size, indicate that inferences about increased social isolation in America arising from the 2004 GSS are unwarranted, and suggest the emergence of increased political isolation.