About

Alina Arseniev-Koehler is a computational and cultural sociologist with substantive interests in language, health, and social categories. She strives to clarify core concepts and debates about cultural meaning in sociology, focusing on how individuals learn and deploy stereotypes. Her empirical work concentrates on cases where meaning is linked to inequality and health, such as the moral meanings attached to body weight, the stigmatizing meanings of disease, and gender stereotypes. To investigate these topics, Alina uses computational methods and machine learning, especially computational text analysis. Her work also addresses a methodological question: how can scientists measure meanings encoded in text data, such as news articles and social media posts? Computational text analysis requires mathematically modeling the nuanced ways in which human language encodes and conveys meaning. Alina emphasizes that innovation in computational text analysis is tightly intertwined with innovation in the theoretical understanding of meanings. She holds a B.A. in Sociology from the University of Washington (2014), and a master's and Ph.D. in Sociology from the University of California, Los Angeles (2022).

Research topics

• Computer Science
• Artificial Intelligence
• Psychology
• Natural Language Processing
• Sociology
• Social psychology
• Machine Learning
• Political Science
• Epistemology
• Gender studies

Selected publications

• Meaning in Hyperspace: Word Embeddings as Tools for Cultural Measurement

  Annual Review of Sociology · 2025-04-11 · 10 citations

  articleOpen accessSenior author

  Word embeddings are language models that represent words as positions in an abstract many-dimensional meaning space. Despite a growing range of applications demonstrating their utility for sociology, there is little conceptual clarity regarding what exactly embeddings measure and whether this matches what we need them to measure. Here, we fill this theoretical gap by clarifying how cultural meaning can be understood in spatial terms. We argue that embeddings operationalize context spaces, where words’ positions can reflect any regularity in usage. We then examine sociologists' embeddings-based measurements to argue that most sociologists are instead implicitly interested in capturing concept spaces, where positions strictly indicate meaningful conceptual features (e.g., femininity or status). Because meaningful features yield regularities in usage, context spaces can proxy for concept spaces. However, context spaces also reflect surface regularities in language—e.g., syntax, morphology, dialect, and phraseology—which are irrelevant to most sociological investigations and can bias cultural measurement. We draw on our framework to propose best practices for measuring meaning with embeddings.

  PublisherDOI

• Disease frames and their consequences for stigma and medical research funds

  Social Science & Medicine · 2025-03-11 · 2 citations

  articleOpen access1st authorCorresponding

  Illnesses are often understood as criminal acts, as medically treatable conditions, or through metaphors of battles and journeys. Theorists suggest that frames vary across diseases and over time in systematic ways, and that frames have concrete consequences for the distribution of resources. But data limitations have prevented scholars from testing these hypotheses. We combine word embeddings and regression analysis to examine four frames for 104 conditions in news media. Our corpus includes over four million news documents published between 1980 and 2018. First, we study the determinants of disease framing by examining which diseases tend to be medicalized, criminalized, and linked to battle and journey metaphors. We find evidence for systematic links between the demographic characteristics of affected individuals and the extent to which diseases are medicalized or criminalized. Next, we examine disease frames’ consequences for stigma and federal medical research funding. While medical and criminal frames are associated with higher levels of stigma, battle and journey frames are associated with less stigma. And while medical, criminal, and battle frames are associated with more research funding, journey frames are associated with less. Together, our results identify the ways in which the social construction of disease reflects and reinforces social inequality. • We generalize prior work on the framing of disease to 104 conditions. • Disease framing is patterned by gender, race, disease type, and disease burden. • Criminal and medical frames are linked with more stigma but more research funding. • Journey framing is linked with less stigma but also less research funding. • Battle framing is linked less stigma and more research funding.

  PublisherDOI

• Leveraging diagnosis and biometric data from the All of Us Research Program to uncover disparities in obesity diagnosis

  Obesity Pillars · 2025-02-07 · 1 citations

  articleOpen access1st authorCorresponding

  Background: Despite extensive efforts to standardize definitions of obesity, clinical practices of diagnosing obesity vary widely. This study examined (1) discrepancies between biometric body mass index (BMI) measures of obesity and documented diagnoses of obesity in patient electronic health records (EHRs) and (2) how these discrepancies vary by patient gender and race and ethnicity from an intersectional lens. Methods: Research Program dataset. Results: Over half (60 %) of participants with a BMI indicating obesity had no clinical diagnosis of obesity in their EHRs. Adjusting for BMI, comorbidities, and other covariates, women's adjusted odds of diagnosis were far higher than men's (95 % confidence interval 1.66-1.75). However, the gender gap between women's and men's likelihood of diagnosis varied widely across racial groups. Overall, Non-Hispanic (NH) Black women and Hispanic women were the most likely to be diagnosed and NH-Asian men were the least likely to be diagnosed. Conclusion: Men, and particularly NH-Asian men, may be at heightened risk of underdiagnosis of obesity. Women, and especially Hispanic and NH-Black women, may be at heightened risk of unanticipated harms of obesity diagnosis, including stigma and competing demand with other health concerns. Leveraging diagnosis and biometric data from this unique public domain dataset from the All of Us project, this study revealed pervasive disparities in diagnostic attribution by gender, race, and ethnicity.

  PublisherDOI

• Measuring Narrative Complexity Among Suicide Deaths in the National Violent Death Reporting System (2003–2021 NVDRS)

  Information · 2025-11-15

  articleOpen access

  A widely used repository of violent death records is the U.S. Centers for Disease Control National Violent Death Reporting System (NVDRS). The NVDRS includes narrative data, which researchers frequently utilize to go beyond its structured variables. Prior work has shown that NVDRS narratives vary in length depending on decedent and incident characteristics, including race/ethnicity. Whether these length differences reflect differences in narrative information potential is unclear. We use the 2003–2021 NVDRS to investigate narrative length and complexity measures among 300,323 suicides varying in decedent and incident characteristics. To do so, we operationalized narrative complexity using three manifest measures: word count, sentence count, and dependency tree depth. We then employed regression methods to predict word counts and narrative complexity scores from decedent and incident characteristics. Both were consistently lower for black non-Hispanic decedents compared to white, non-Hispanic decedents. Although narrative complexity is just one aspect of narrative information potential, these findings suggest that the information in NVDRS narratives is more limited for some racial/ethnic minorities. Future studies, possibly leveraging large language models, are needed to develop robust measures to aid in determining whether narratives in the NVDRS have achieved their stated goal of fully describing the circumstances of suicide.

  PublisherOA PDFDOI

• Identifying Witnessed Suicides in National Violent Death Reporting System Narratives

  Healthcare · 2024-01-15 · 2 citations

  articleOpen access

  There is increasing attention to suicides that occur in view of others, as these deaths can cause significant psychological impact on witnesses. This study illuminates characteristics of witnessed suicides and compares characteristics of these deaths to non-witnessed suicides. We develop a codable definition of what constitutes witnessed (vs. non-witnessed) suicide. Our data include a sample of 1200 suicide descriptions from the 2003–2017 National Violent Death Reporting System (NVDRS). We first developed criteria to identify probable cases of witnessed suicide. The coding scheme achieved 94.5% agreement and identified approximately 10% (n = 125) of suicides as witnessed. Next, we examined differences between witnessed and non-witnessed suicides in demographics, manner of death, and social/environmental factors using bivariate Chi-squared tests, multivariate logistic regression, and ANOVA. Witnessed suicide decedents were significantly more likely than non-witnessed suicide decedents to be male, younger, and members of a sexual minority, and to have died in living spaces by means of a firearm. Two thirds of witnesses were strangers to the decedents, while 23.2% were romantic partners or ex-partners of the decedents. Our coding method offers a reliable approach to identify witnessed suicides. While witnessed suicides are relatively infrequent, these deaths have profound impact on witnesses. Articulating the features of witnessed suicides may contribute to identifying potential risk mitigation strategies.

  PublisherOA PDFDOI

• Talk of Family: How Institutional Overlap Shapes Family-Related Discourse Across Social Class

  RSF The Russell Sage Foundation Journal of the Social Sciences · 2024-08-20 · 4 citations

  articleOpen access

  We develop a novel application of machine learning and apply it to the interview transcripts from the American Voices Project (N = 1,396), using discourse atom topic modeling to explore social class variation in the centrality of family in adults' lives. We take a two-phase approach, first analyzing transcripts at the person level and then at the line level. Our findings suggest that family, as represented by talk, is more central in the lives of those without a college degree than among the college educated. However, the degree of institutional overlap between family and other key institutions-health, work, religion, and criminal justice-does not vary by education. We interpret these findings in the context of debates about the deinstitutionalization of family in the contemporary United States. This demonstrates the value of a new method for analyzing qualitative interview data at scale. We address ways to expand the use of this method to shed light on educational disparities.

  PublisherOA PDFDOI

• Gendered Patterns in Manifest and Latent Mental Health Indicators Among Suicide Decedents: 2003–2020 National Violent Death Reporting System (NVDRS)

  American Journal of Public Health · 2023-11-10 · 8 citations

  articleOpen access1st authorCorresponding

  Objectives. To investigate differences in the documentation of mental health symptomology between male and female suicide decedents in the 2003–2020 US National Violent Death Reporting System (NVDRS). Methods. Using information on 271 998 suicides in the 2003–2020 NVDRS, we evaluated precoded mental health–related variables and topic model–derived latent mental health themes in the law enforcement and coroner or medical examiner death narratives compiled by trained public health workers. Results. Public health records of male compared with female suicides were less likely to include notations of mental health conditions or treatment interventions. However, topic modeling of death summaries revealed that male suicide decedents were more likely to evidence several subclinical cognitive and emotional indicators of distress. Conclusions. Suicide death records vary by gender, both in recorded evidence for mental health conditions at time of death and in accompanying narratives describing proximal circumstances surrounding these deaths. Our findings hint that patterns of subclinical mental health changes among men might be less well captured in commonly used mental health indicators, suggesting that prevention efforts may benefit from measures that also target assessment of subclinical distress. ( Am J Public Health. 2024;114(S3):S268–S277. https://doi.org/10.2105/AJPH.2023.307427 )

  PublisherOA PDFDOI

• The Stigma of Diseases: Unequal Burden, Uneven Decline

  American Sociological Review · 2023-09-30 · 58 citations

  articleOpen accessSenior author

  Why are some diseases more stigmatized than others? And, has disease stigma declined over time? Answers to these questions have been hampered by a lack of comparable, longitudinal data. Using word embedding methods, we analyze 4.7 million news articles to create new measures of stigma for 106 health conditions from 1980 to 2018. Using mixed-effects regressions, we find that behavioral health conditions and preventable diseases attract the strongest connotations of immorality and negative personality traits, and infectious diseases are most marked by disgust. These results lend new empirical support to theories that norm enforcement and contagion avoidance drive disease stigma. Challenging existing theories, we find no evidence for a link between medicalization and stigma, and inconclusive evidence on the relationship between advocacy and stigma. Finally, we find that stigma has declined dramatically over time, but only for chronic physical illnesses. In the past four decades, disease stigma has transformed from a sea of negative connotations surrounding most diseases into two primary conduits of meaning: infectious diseases spark disgust, and behavioral health conditions cue negative stereotypes. These results show that cultural meanings are especially durable when they are anchored by interests, and that cultural changes intertwine in ways that only become visible through large-scale research.

  PublisherDOI

• School, Studying, and Smarts: Gender Stereotypes and Education Across 80 Years of American Print Media, 1930–2009

  Social Forces · 2023 · 44 citations

  • Sociology
  • Political Science
  • Gender studies

  Abstract In this article, we apply computational word embeddings to a 200-million-word corpus of American print media (1930–2009) to examine how education-relevant gender stereotypes changed as women’s educational attainment caught up with and eventually surpassed men’s. This case presents a rare opportunity to observe how cultural components of the gender system transform alongside the reversal of an important pattern of stratification. We track six stereotypes that prior work linked to academic outcomes. Our results suggest that stereotypes most closely tied to the core stereotypical distinction between women as communal and men as agentic remained unchanged. The other stereotypes we tracked, however, became increasingly gender polarized: as school and studying gained feminine associations, intelligence and unintelligence gained masculine ones. Unexpectedly, we observe that trends in the gender associations of intelligence and studying are near-perfect mirror opposites, suggesting an interrelationship. We use these observations to further elaborate contemporary theoretical accounts of the gender system, arguing that this system persists partly because stereotypes shift to reinterpret social change in terms of a durable hierarchical distinction between women and men.

  PublisherDOI

• The Stigma of Diseases: Unequal Burden, Uneven Decline

  2022-01-18 · 5 citations

  preprintOpen accessSenior author

  Has disease stigma declined? Our ability to answer this question has been hampered by the lack of comparable data across diseases and over time. Using word embeddings, we analyze 4.5 million news articles to create new measures of stigma for 107 health conditions. We find that in the 1980s, most diseases were marked by strong connotations of disgust, danger, impurity, and negative personality traits. Since then, stigma has declined dramatically for most physical illnesses; cancers, neurological conditions, genetic diseases, and many other conditions have shed most of their negative connotations. But this decline was uneven: mental illnesses, eating disorders, and addictions saw no stigma declines, and stigma declined more slowly for infectious diseases than for chronic conditions. Using multivariate regression, we find that patients’ activism explains some but not all of the variation in stigma. Stigma has transformed from a sea of negative connotations surrounding most diseases to a narrower set of judgments targeting conditions where the primary symptoms are aberrant behaviors.

  PublisherOA PDFDOI

Frequent coauthors

• Jacob G. Foster

  25 shared

• Vickie M. Mays

  UCLA Health

  22 shared

• Susan D. Cochran

  UCLA Health

  22 shared

• Kai-Wei Chang

  14 shared

• Megan A. Moreno

  University of Wisconsin–Madison

  9 shared

• Xingruo Zhang

  Chicago Department of Public Health

  6 shared

• Peter Broadwell

  Stanford University

  5 shared

• Joanna Chen Cham

  5 shared