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This exploratory study renders it an open empirical question how ordinary people understand the meaning of culture and what are its sociopolitical implications. Using original survey data from over 11,000 respondents across nine European countries, the study focuses on an open-ended question where the respondents defined the meaning of "culture" in their own words. The open-ended answers are scrutinized by structural topic modeling, which allows for identifying relatively coherent clusters of understandings of culture and their relationships. In addition to examining how these understandings vary across national contexts, the study investigates their variation according to major sociodemographic divisions and sociopolitical and cultural factors in and across the nine European societies through regression methods. The results underscore substantial national variation and social stratification of the understandings of culture and the potential of computational text analysis in open-ended survey research.

What, in the 21st century, is our vision of the "good society," and what are the obstacles to its realization? What is the ideal mix of equality and tradition, individual initiative and social welfare, economic prosperity and environmental responsibility, national unity and respect for diversity? Research suggests that liberals and conservatives differ considerably in the prioritization of these values, but nearly all of this research makes use of closed-ended responses to questionnaire items. To examine ideological similarities and dissimilarities in value expression and social representation when it comes to relatively open-ended communication in online social media networks, we used quantitative text-analytic methods to analyze more than 3.8 million messages sent by over 1 million Twitter users about what constitutes a good (vs. bad) society. Results revealed a fairly high degree of ideological divergence: Liberals were more likely to raise themes of social justice, global inequality, women's rights, racism, criminal justice, health care, poverty, progress, social change, personal growth, and environmental sustainability, whereas conservatives were more likely to mention religion, social order, business, capitalism, national symbols, immigration, and terrorism, as well as individual authorities and news organizations. There were also some areas of convergence: Liberals, moderates, and conservatives were equally likely to prioritize economic prosperity, family, community, and the pursuit of health, happiness, and freedom.

Sociologists frequently make use of language as data in their research using methodologies including open-ended surveys, in-depth interviews, and content analyses. Unfortunately, the ability of researchers to analyze the growing amount of these data declines as the costs and time associated with the research process increases. Topic modeling is a computer-assisted technique that can help social scientists to address these data challenges. Despite the central role of language in sociological research, to date, the field has largely overlooked the promise of automated text analysis in favor of more familiar and more traditional methods. This article provides an overview of a topic modeling framework especially suited for social scientific research. By way of a case study using abstracts from social movement studies literature, a short tutorial from data preparation through data analysis is given for the method of structural topic modeling. This example demonstrates how text analytics can be applied to research in sociology and encourages academics to consider such methods not merely as novel tools, but as useful supplements that can work beside and enhance existing methodologies.

Sovereign Wealth Funds (SWFs) are an emerging community of investment organizations, subject to the conflicting demands of international and domestic institutions. From the lens of variegated capitalism, one can explain their diversity and apparent contradictions by underlining how they embody negotiations between global and local institutional pressures. Many SWFs now publish official reports, which represents a unique opportunity to analyze how they portray these institutional influences. We use an unsupervised machine learning method, structural topic modeling, to analyze the 2015 official reports of 40 SWFs and 17 public pension funds globally. With this novel method, we propose the first international, community-wide study that shows how capitalism is adapted across regions, political systems, and even organizational frontiers through SWFs. We show that region and political systems are factors supporting a polarization of missions between neoliberal and state capitalist view. However, this is nuanced by international clubs that support new investment strategies, and some high-profile SWFs that mix the neoliberal and state capitalist views.

In this article, we analyze anti- and pro-immigrant attitudes expressed following the Essex Lorry Deaths tragedy in October 2019 in Britain, in which 39 Vietnamese immigrants died in a sealed lorry truck on their way to their destination. We apply Structural Topic Modeling, an automated text analysis method, to a Twitter dataset (N= 4,376), to understand public responses to the Lorry Deaths incident. We find that Twitter users' posts were organized into two themes regarding attitudes toward immigrants: (1) migration narratives, stereotypes, and victim identities, and (2) border control. Within each theme, both pro- and anti-immigration attitudes were expressed. Pro-immigration posts reflected counter-narratives that challenged the mainstream media's coverage of the incident and critiqued the militarization of borders and the criminalization of immigration. Anti-immigration posts ranged from reproducing stereotypes about Vietnamese immigrants to explicitly blaming the victims themselves or their families for the deaths. This study demonstrates the uses and limitations of using Twitter for public opinion research by offering a nuanced analysis of how pro-and anti-immigration attitudes are discussed in response to a tragic event. Our research also contributes to a growing literature on public opinion about an often-forgotten immigrant group in the UK, the Vietnamese.

The drastic increase in domestic production of natural gas due to the fracking boom prompted efforts to develop a robust infrastructure in the U.S. to export natural gas. Given environmental concerns over increased fossil fuel development, significant opposition mobilized to "keep it [fossil fuels] in the ground" by acting to prevent not only natural gas production but also its transportation via pipelines and shipping via export terminals. Our analysis focuses on the latter component, specifically examining the long history of proposed liquefied natural gas infrastructure in two coastal communities in Oregon. Members of the public engaged in the formal siting processes and mobilized both opposition and support. We examine their use of collective action frames in both comments at public hearings and letters to the editor in local newspapers ( N = 4618) over the 16 years that these proposals were under consideration, quantifying the dynamic nature of framing using computational text analysis. We find that both groups vary their use of framing over time and by venue, reacting to exogenous events (e.g., September 11th, Fukushima) and tailor their messages to the context (e.g., an in-person hearing or a letter to the editor). Opponents concentrated on potential threats, initially emphasizing local impacts like tanker and pipeline safety but eventually focusing on climate concerns. Supporters, meanwhile, stressed the economic benefits of the projects but alternated their specific framing based on venue—focusing on employment when talking to regulatory agencies and community economic benefits in letters. While this juxtaposition of economic benefits and environmental threats was a key part of public discourse, opponents also expanded their framing to questions of local sovereignty and governance, allowing a broader coalition to develop and ultimately succeeding in defeating the proposals.

Purpose
The rising number of food recalls has raised concerns about complexity, globalization and weak governance in the food supply chain. This paper aims to investigate the recall of plant-based products with data from the US Food and Drug Administration.


Design/methodology/approach
Introducing the structural topic modeling method allowed us to test theories on recall in the context of sustainable food consumption, enhancing the understanding of food recall processes. This approach helps identify latent topics of product recalls and their interwoven relationships with various stakeholders.


Findings
The results answer a standing research call for empirical investigation in a nascent food industry to identify stakeholders' engagements for food safety crisis management for corporate social responsibility practices. This finding provides novel insights on managing threats to food safety at an industry level to extend existing antecedents and consequences of product recall at a micro level.


Practical implications
For practitioners, this empirical finding may provide insights into stakeholder management and develop evidence-based strategies to prevent threats to food safety. For public policymakers, this analysis may help identify patterns of recalls and assist guidelines and alarm systems (e.g. EU's Rapid Alert System for Food and Feed) on threats in the food supply chain.


Originality/value
Two detected clusters, such as opportunisms of market actors in the plant-based food system and food culture, from the analysis help understand corporate social responsibility and food safety in the plant-based food industry.

Ethnic and racial disparities in the coronavirus (COVID-19) pandemic raise significant concerns. This study analyzes social media discourses toward four ethnic communities in the United States during the pandemic and reveals disparities in pandemic experiences among them. A total of 488,029 tweets mentioning one of the four ethnic communities, that is, Asians, Blacks, Hispanics, and Native Americans, were investigated by a structural topic modeling approach with emotional expressions and time as covariates in the topic model. The results demonstrate that discourses about Asian, Hispanics, and Native American communities were often induced by pandemic-related events, concerning topics beyond one's community, and reflecting an experience of implicit racism and an adoption of technical supports from health systems. Meanwhile, discourses about Blacks were racially related, discussing topics within the community, and reflecting an experience of explicit racism and an adoption of psychological supports from ingroup. We discuss the implications of our findings on ethnic health disparities.