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computational text analysis for Qualitative IS Research: A Methodological eRfletolcin

COMMUNICATIONS OF THE ASSOCIATION FOR INFORMATION SYSTEMS 2025年 56卷

作者： Mettler, Tobias Univ Lausanne Lausanne Switzerland

Qualitative analysis is an essential component of the dynamic process of sensemaking, where researchers sift through data to extract innovative insights that can contribute to new theoretical perspectives. In most cases, this involves analyzing unstructured text data gathered from naturalistic inquiries and secondary data material. However, due to the predominantly manual nature of qualitative text analysis, there is often a trade-off between feasibility and expanding the scope of a study, giving rise to criticism by quantitative scholars that theoretical generalizations from qualitative research often lack a larger empirical backing, are not reproducible, or are subjectively biased. As computational text analysis (CTA) gradually becomes more accessible, also new research opportunities for qualitative scholars arise, which must be aligned with traditional qualitative thinking and evaluation criteria. In this article, we explore the value and purpose, process, and validation of CTA in qualitative IS research. Drawing from a specific case illustration, we examine potential issues concerning data collection and sampling, analysis, and interpretation of findings. Additionally, we discuss the potential obstacles that qualitative researchers using CTA may encounter when conducting the study but also when submitting their work for consideration for publication in IS journals.

关键词： computational text analysis Digital Qualitative Research Machine Learning Qualitative analysis Qualitative-Quantitative Chasm Theory Development

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Convergence or Divergence: A computational text analysis of Stakeholder Concerns on Manufacturing Upgrading in China

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IEEE TRANSACTIONS ON ENGINEERING MANAGEMENT 2024年 71卷 1285-1295页

作者： Hu, Zhiming Ma, Huimin Xiong, Jie Gao, Peiran Divakaran, Pradeep Kumar Ponnamma Huazhong Univ Sci & Technol Sch Management Wuhan 430074 Peoples R China ESSCA Sch Management Dept Strategy Entrepreneurship & Int Business F-49003 Angers France Zhengzhou Univ Sch Management Engn Zhengzhou 450001 Peoples R China Rennes Sch Business Dept Mkt F-35065 Rennes France

Stakeholder participation is essential to the reasonable design and smooth implementation of industrial policies. Discourse analysis can be employed as a valuable methodology to decode stakeholder (dis)agreements. Following stakeholder theory, we examine four stakeholder groups, namely the government, enterprises, media, and academia, to analyze the public focus of industrial upgrading in China. We adopt a computational text analysis approach (keyword frequency calculation, word collocation analysis, and theme identification) to understand the divergent and convergent stakeholders' concerns toward manufacturing upgrading in China from 2015 to 2020, which is widely considered as a manufacturing upgrading policy formulation stage. Our results show that stakeholders mainly focus on innovation capability and digital transformation for China's manufacturing upgrading. There, industrial planning is the major issue for government and academia. Contradistinctively, enterprises' main concern is servitization. Enterprise internationalization is more frequently mentioned than manufacturing upgrading in the media industry. Policymakers should engage in various tactics to make the policies endorsed by other stakeholders. Most importantly, the government should fully integrate the views of entrepreneurs in the policy initiation stage. We provide practical implications for the Chinese government to implement better the Made in China 2025 plan.

关键词： Stakeholders Manufacturing text analysis Media Government Big Data text mining China computational text analysis industrial upgrading manufacturing stakeholder concerns

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How do archaeologists write about racism? computational text analysis of 41 years of Society for American Archaeology annual meeting abstracts

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ANTIQUITY 2022年第387期96卷 696-709页

作者： Park, Gayoung Wang, Li-Ying Marwick, Ben Univ Washington Dept Anthropol Seattle WA 98195 USA

How often, and in what contexts, have archaeologists discussed racism over the last four decades? Do societal events lead to sustained discussions of racism among the academic community? Here, the authors seek to answer these questions by applying computational text analysis methods to 68176 abstracts from 41 meetings of the Society for American Archaeology. Discussions of racism are found to be rare-usually a passing mention in the context of broader social issues. Historical archaeologists have addressed racism more frequently than other archaeologists. The results form a baseline against which the discipline's engagement with racism as a research theme and with anti-racist strategies might be tracked.

关键词： computational text analysis racism academic discourse Society for American Archaeology history of archaeology

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From Ends to Means: The Promise of computational text analysis for Theoretically Driven Sociological Research

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SOCIOLOGICAL METHODS & RESEARCH 2022年第4期51卷 1469-1483页

作者： Bonikowski, Bart Nelson, Laura K. NYU Dept Sociol New York NY USA Univ British Columbia Dept Sociol Vancouver BC Canada NYU New York NY 10012 USA

As the field of computational text analysis within the social sciences is maturing, computational methods are no longer seen as ends in themselves, but rather as means toward answering theoretically motivated research questions. The objective of this special issue is to showcase such research: the use of novel computational methods in the service of advancing substantive scientific knowledge. In presenting the contributions to the issue, we discuss several insights that emerge from this work, which hold relevance not only for current and aspiring practitioners of computational text analysis, but also for its skeptics. These concern the central role of theory in designing and executing computational research, the selection of appropriate techniques from a rapidly growing methodological toolkit, the benefits-and risks-of methodological bricolage, and the necessity of validating all aspects of the research process. The result is a set of broad considerations concerning the effective application of computational methods to substantive questions, illustrated by eight exemplary empirical studies.

关键词： computational text analysis computational social science applied methods research design logic of inquiry

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Stakeholder concerns of air pollution in Hong Kong and policy implications: A big-data computational text analysis approach

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ENVIRONMENTAL SCIENCE & POLICY 2019年 101卷 374-382页

作者： Lam, Jacqueline C. K. Cheung, Lawrence Y. L. Wang, Shanshan Li, Victor O. K. Univ Hong Kong Dept Elect & Elect Engn Pokfulam Hong Kong Peoples R China Univ Cambridge Judge Business Sch Energy Policy Res Grp Trumpington St Cambridge England MIT MIT Energy Initiat CEEPR Cambridge MA 02139 USA Chinese Univ Hong Kong Dept Linguist & Modern Languages Shatin Hong Kong Peoples R China Harvard Univ Dept Linguist Cambridge MA 02138 USA

Stakeholder engagement is critical to the successful formulation and implementation of environmental policies. text analysis can serve as an important means to decipher stakeholder (dis)agreements. Traditionally, researchers analyze texts using qualitative text analysis. Relying only on qualitative text analysis can no longer be sufficient to handle very large amounts of textual data. Now computational methods can be applied to help extract opinions in electronic texts. Our paper adopts a big data computational text analysis approach (keyword analysis, keyword co-occurrence, thematic analysis), based on a corpus of 2.4 million words, to compare concerns towards air pollution among three stakeholder groups (i.e. the government (GOV), the environmental groups (NGO) and the news media (MEDIA)) in Hong Kong, between 2002 and 2012, a period when air pollution was subject to rigorous policy debates and discussions. Our analysis shows the somewhat different concerns of the stakeholders. The stakeholders focus heavily on emissions and end-of-pipe pollution control. Though the advocation of sustainability since 1990s, the government-led command-and-control approach still dictates the discourse. Sustainability is featured more intensively by the Hong Kong Special Administrative Region (HKSAR) Government. Transboundary pollution (e.g. from Guangdong, China) is not a major concern for all stakeholder groups. Public health is a greater concern in MEDIA, as compared to the other two stakeholder groups. Future air pollution policy-making may direct more of its attention to the pressing concerns over the negative impacts of air pollution on health and its associated costs, with health experts getting more closely involved in the regulatory decision-makings. More information should be given to vulnerable groups, such as young children or workers in polluted environments. To better relate differential health risks of pollution in everyday life, both local and international government

关键词： Air pollution Big data computational text analysis Stakeholder concerns Public health Personalisation

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text as big data: Develop codes of practice for rigorous computational text analysis in energy social science

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ENERGY RESEARCH & SOCIAL SCIENCE 2020年 70卷

作者： Mueller-Hansen, Finn Callaghan, Max W. Minx, Jan C. Mercator Res Inst Global Commons & Climate Change EUREF Campus 19Torgauer Str 12-15 D-10829 Berlin Germany Leibniz Assoc Potsdam Inst Climate Impact Res PIK POB 60 12 03 D-14412 Potsdam Germany Univ Leeds Sch Earth & Environm Priestley Int Ctr Climate Leeds LS2 9JT W Yorkshire England

Augmenting traditional social science methods with computational analysis is crucial if we are to exploit the vast digital archives of text data that have become available over the past two decades. In this journal, Benites-Lazaro et al. [1] showcase this in an application of topic modeling and other computational methods to an actor-specific examination of changes in policy discourse on ethanol in Brazil and point out methodological promises and challenges. However, their contribution also highlights the need for establishing codes of practice for computational text analysis. In this perspective, we discuss five areas for improvement when treating text as big data in light of guiding principles from computational research - transparency, reproducibility and validation - to facilitate rigorous research practice: (1) full transparency over data collection and corpus construction, (2) comprehensive method descriptions that enable reproducibility by other researchers, (3) application of rigorous model validation procedures, (4) results interpretation based on primary text and clear research design and (5) critical discussion and contextualization of main findings. We conclude that the energy social science community needs to develop codes of practice to build on the promising research within the field of computational text analysis and suggest first steps into this direction.

关键词： computational text analysis Big data Automated content analysis text mining Topic modeling Discourse analysis

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Integrating Human Insights Into text analysis: Semi-Supervised Topic Modeling of Emerging Food-Technology Businesses' Brand Communication on Social Media

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SOCIAL SCIENCE COMPUTER REVIEW 2024年第2期42卷 416-437页

作者： Su, Leona Yi-Fan Chen, Tianli Ng, Yee Man Margaret Gong, Ziyang Wang, Yi-Cheng Univ Illinois Charles H Sandage Dept Advertising Urbana IL USA Univ Illinois Ctr Digital Agr Urbana IL USA Univ Illinois Informat Programs Urbana IL USA Univ Illinois Inst Commun Res Urbana IL USA Univ Illinois Personalized Nutr Initiat Urbana IL USA Univ Illinois Dept Food Sci & Human Nutr Urbana IL USA Univ Illinois Dept Journalism Urbana IL USA Univ Illinois Dept Comp Sci Urbana IL USA Univ Illinois Charles H Sandage Dept Advertising Urbana IL 61801 USA Univ Illinois Dept Food Sci & Human Nutr Urbana IL 61801 USA

textual social media data have become indispensable to researchers' understanding of message strategies and other marketing practices. In a new departure for the field of brand communication, this study adopts and extends a semi-supervised machine-learning approach, guided latent Dirichlet allocation (LDA), which incorporates human insights into the discovery and classification of topics. We used it to analyze tweets from businesses involved with an emerging food technology, cultured meat, and delineated four key message strategies used by these brands: providing functional, educational, corporate social responsibility, and relational content. We further ascertained the relationships between brands and the key topics embedded in their Twitter data. A comparison of model performance suggests that guided LDA can be an advantageous alternative to traditional LDA, which is characterized by high efficiency and immense popularity among researchers, but-because of its unsupervised nature-yields findings that can be difficult to interpret. The present study therefore has critical theoretical and methodological implications for communication and marketing scholars.

关键词： guided latent Dirichlet allocation machine learning computational text analysis cultured meats social media marketing

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computational grounded theory revisited: From computer-led to computer-assisted text analysis

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BIG DATA & SOCIETY 2022年第1期9卷

作者： Carlsen, Hjalmar Bang Ralund, Snorre Univ Copenhagen Copenhagen Ctr Social Data Sci Copenhagen Denmark Univ Copenhagen Dept Sociol Copenhagen Denmark

The size and variation in both meaning-making and populations that characterize much contemporary text data demand research processes that support both discovery, interpretation and measurement. We assess one dominant strategy within the social sciences that takes a computer-led approach to text analysis. The approach is coined computational grounded theory. This strategy, we argue, relies on a set of unwarranted assumptions, namely, that unsupervised models return natural clusters of meaning, that the researcher can understand text with limited immersion and that indirect validation is sufficient for ensuring unbiased and precise measurement. In response to this criticism, we develop a framework that is computer assisted. We argue that our reformulation of computational grounded theory better aligns with the principles within grounded theory, anthropological theory generation and ethnography.

关键词： computational text analysis interpretation mixed methods grounded theory digital social research

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Analysing community reaction to refugees through text analysis of social media data

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JOURNAL OF ETHNIC AND MIGRATION STUDIES 2023年第2期49卷 492-534页

作者： Kelling, Claire Monroe, Burt L. Penn State Univ Dept Stat State Coll PA 16801 USA Penn State Univ Liberal Arts Prof Polit Sci Social Data Analyt & State Coll PA USA

Understanding the social integration of refugees requires scholars and community leaders to understand the complex and varied political reaction of citizens to the prospect and reality of refugees entering their local communities. In this study, we apply the Structural Topic Model (STM) to characterise citizen-level discourse in comments posted in response to refugee-related news articles on Facebook in Lancaster, Pennsylvania, and Roanoke, Virginia, two cities with similar demographics and conservative partisanship, but sharply contrasting refugee-related policies and experiences. We find that, overall, commenters framed their arguments with an identity-based frame more often than economics, morality, security or legality frames, but that these tended to be blended in ways that obscure the basis in identity. We also find that comments within the discourse of the more refugee-experienced Lancaster community were more likely to involve substantive arguments than in Roanoke, more likely to use economics frames, less likely to use identity frames, less likely to involve incivility and less likely to feature a salient misinformation-influenced theme (refugees vs. homeless veterans). This suggests that host community discourse grows more substantive and positive as a function of hospitable refugee policies and refugee hosting experience, and we discuss how this research might be expanded beyond this pair of cases to evaluate this broader implication.

关键词： Refugee computational text analysis integration public discourse

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A workflow for analyzing cultural schemas in texts

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JOURNAL OF MATHEMATICAL SOCIOLOGY 2025年第1期49卷 1-24页

作者： Taylor, Marshall A. Stoltz, Dustin S. New Mexico State Univ Dept Sociol 292B Sci Hall Las Cruces NM 88003 USA Lehigh Univ Dept Sociol & Anthropol Bethlehem PA USA

Concept class analysis (CoCA) is a method for recovering cultural schemas in texts using a combination of word embedding and community detection models. Like survey-based forms of schematic class analysis (SCA), however, interpreting results can be difficult. Some of these interpretive difficulties are applicable across types of SCA, while others are unique to CoCA. In this paper, we propose a complete workflow for interpreting and analyzing CoCA output. We use the case of social identity schemas in a collection of over 13,000 U.S. political blog posts to outline a number of interpretive and analytical strategies and a robustness check to make sense of the cultural schemas recovered from texts.

关键词： computational text analysis concept class analysis cultural schemas cultural sociology word embeddings

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