Management and Organizational Research: Structural Topic Modeling for a Better Understanding of Theory Application

In management and organization research, theory development is often linked with developing a new theory. However, regardless of the number of existing theories, most theories remain empirically untested, and the progress in understanding the application of theories has been scarce. This article discusses how theories are applied in existing management and organization research studies. This study applies the Structural Topic Model to 4636 research papers from the S2ORC dataset. The results reveal twelve research themes, establish correlations, and document the evolution of themes over time. The findings of this study reveal that the theoretical application is not consistent across research themes, theories are primarily used for descriptive and communicative properties, and most research themes in management and organization research are more concerned with discovering phenomena rather than with understanding and forecasting them.

Questing for Justice on Twitter: Topic Modeling of #StopAsianHate Discourses in the Wake of Atlanta Shooting

On March 16, 2021, a shooting in Atlanta killed eight people, six were women of Asian descent. This creates a new atmosphere online and offline to discuss hate crimes, racism, and violence against Asian Americans in the United States. The current research utilizes structural topic modeling and text mining to explore how the 2021 Atlanta shooting ignited debates and public discourse on the #StopAsianHate-related conversations on Twitter. The study analyzes the first 7 days of the shooting to explore the temporal patterns and emergent topics of Twitter discourses. Findings show that salient topics and temporal patterns differ from day to day, but topics such as “stand with AAPI community” and “stop racism” are prevalent throughout the 7-day period. This study discusses social media’s role in shaping and reporting public discourses, that is, how digital justice is exercised, and offers social and policy implications. There can be implications for social media’s role in shaping and reporting public discourses on social phenomena with digital justice.

Facets of Specialization and Its Relation to Career Success: An Analysis of U.S. Sociology, 1980 to 2015

We investigate how sociology students garner recognition from niche field audiences through specialization. Our dataset comprises over 80,000 sociology-related dissertations completed at U.S. universities, as well as data on graduates’ pursuant publications. We analyze different facets of how students specialize—topic choice, focus, novelty, and consistency. To measure specialization types within a consistent methodological frame, we utilize structural topic modeling. These measures capture specialization strategies used at an early career stage. We connect them to a crucial long-term outcome in academia: becoming an advisor. Event-history models reveal that specific topic choices and novel combinations exhibit a positive influence, whereas focused theses make no substantial difference. In particular, theses related to the cultural turn, methods, or race are tied to academic careers that lead to mentorship. Thematic consistency of students’ publication track also has a strong positive effect on the chances of becoming an advisor. Yet, there are diminishing returns to consistency for highly productive scholars, adding important nuance to the well-known imperative of publish or perish in academic careers.

Understanding the landscape and propagation of COVID-19 misinformation and its correction on Sina Weibo

The prevalence of health misinformation on social media could significantly influence individuals’ health behaviors. To examine the prevalent topics, propagation, and correction of coronavirus disease 2019 (COVID-19) misinformation, automated content analyses were conducted for posts on Sina Weibo, which is China’s largest microblogging site. In total, 177,816 posts related to COVID-19 misinformation during the COVID-19 outbreak in China were analyzed. The structural topic modeling identified 23 valid topics regarding COVID-19 misinformation and its correction, which were further categorized into three general themes. Sentiment analysis was conducted to generate positive and negative sentiment scores for each post. The zero-inflated Poisson model indicated that only the negative sentiment was a significant predictor of the number of comments (β = 0.003, p < 0.001) but not reposts. Furthermore, users are more prone to repost and comment on information regarding prevention/treatment (e.g., traditional Chinese medicine preventing COVID) as well as potential threats of COVID-19 (e.g., COVID-19 was defined as an epidemic by World Health Organization). Health education and promotion implications are discussed.

Strategic Framing Matters But Varies: A Structural Topic Modeling Approach to Analyzing China’s Foreign Propaganda About the 2019 Hong Kong Protests on Twitter

Drawing upon the approach of strategic framing, this study investigated how China’s state-run media mobilize foreign propaganda machine and use specific patterns to describe the 2019 Hong Kong protests on Twitter. It also shed light on the heterogeneity of both production and reception of the strategic frames used by state media. Structural topic modeling was employed to analyze a large amount of Twitter content (i.e., 14,412 tweets) posted by 13 verified organizational accounts, and six strategic frames were identified as conflicts and violence, calling for stability and order, marginalizing protests, criticizing the West as accomplices, delegitimizing protests, and social and economic disruption. These frames highlighted insider–outsider and causes and consequences as two overarching communication strategies. The results also revealed that the bureaucratic rank of state media and the engagement rate of each tweet were closely associated with the content prevalence of various strategic frames. In addition to enhancing our understanding of the construction of “protest paradigm” against the social media context, these empirical findings uncover the often overlooked mobility and flexibility of China’s state media discourse as well as the communication ecology shaped and consolidated by the increasing importance state media communicators attach to online engagement metrics.

Polarization Over Vaccination: Ideological Differences in Twitter Expression About COVID-19 Vaccine Favorability and Specific Hesitancy Concerns

Vaccine hesitancy has been a growing public health issue, but during COVID-19, understanding vaccine hesitancy and promote vaccine favorability takes on a troubling immediacy. With the growing political polarization on scientific issues, the COVID-19 vaccine-related sentiment has recently been divided across ideological lines. This study aims to understand how vaccine favorability and specific vaccine-related concerns including possible side effects, distrust in medical professionals, and conspiratorial beliefs concerning COVID-19 vaccines were articulated and transmitted by Twitter users from opposing ideological camps and with different follower scopes. Using a combination of computational approaches, including supervised machine-learning and structural topic modeling, we examined tweets surrounding COVID-19 vaccination ( N = 16,959) from 1 March to 30 June 2020. Results from linear mixed-effects models suggested that Twitter users high on conservative ideology and with a standard instead of large follower scope tend to express less favorable vaccine-related sentiments and talk more about vaccine side effects, distrust of medical professionals, and conspiracy theories. There is also an interaction effect where liberals with large follower scope expressed the least amount of distrust of medical professionals, whereas extreme conservatives expressed greater distrust for health professionals, regardless of their follower scope. Finally, structural topic modeling revealed distinct topical focuses among liberal and conservative users. Theoretical and practical implications for leveraging social media in effective health communication practice were discussed.

Half a Century of NVSQ: Thematic Stability Across Years and Editors

The aim of this article is to understand how the scholarship of the nonprofit sector shifted after almost half a century (1972–2019) of publication in the field’s premier journal, Nonprofit and Voluntary Sector Quarterly. Unlike previous attempts to understand the field’s scholarly evolution, we did not rely on expert opinion and analysis of themes but applied an automated content analytic method, more specifically structural topic modeling (STM). Using this method, we identified 37 key thematic topics that most optimally represent the 1,516 articles that were published in the studied period. After reporting these 37 thematic topics, we analyzed fluctuations based on three key periods of the journal and the editors’ disciplinary fields. While overall there was a trend of continuity (29 out of 37 topics) and little if any impact of the editors’ disciplines, a few thematic topics showed decline and fewer showed increase over time.