Analyzing Gender Differences in Information Systems Research using Topic Modeling with Machine Learning - Research-in-Progress

Aug 5, 2026

Gender disparities in information systems (IS) research extend beyond participation to the topics pursued by men and women scholars. This study investigates how gender shapes research focus and examines implications for diversity, innovation, and a holistic understanding of the IS field. Using an unsupervised machine learning pipeline with Latent Dirichlet Allocation (LDA), the research analyzes abstract subsets authored by men versus women, leveraging Web of Science data and gender information from online self-reported resources.

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Association for Information Systems AIS Electronic Library (AISeL) | NEAIS 2023 Proceedings | New England Chapter of Association for Information Systems | | --- | --- | | 10-28-2023
Analyzing Gender Differences in Information Systems Research using Topic Modeling with Machine Learning
Shimi Zhou
Worcester Polytechnic Institute, [szhou7@wpi.edu](szhou7@wpi.edu)
Eleanor Loiacono
William & Mary, [eloiacono@wm.com](eloiacono@wm.com)
Follow this and additional works at: [https://aisel.aisnet.org/neais2023](https://aisel.aisnet.org/neais2023) | | Recommended Citation Zhou, Shimi and Loiacono, Eleanor, "Analyzing Gender Differences in Information Systems Research using Topic Modeling with Machine Learning" (2023) . NEAIS 2023 Proceedings. 11. [https://aisel.aisnet.org/neais2023/1](https://aisel.aisnet.org/neais2023/1)1 This material is brought to you by the New England Chapter of Association for Information Systems at AIS Electronic Library (AISeL) . It has been accepted for inclusion in NEAIS 2023 Proceedings by an authorized administrator of AIS Electronic Library (AISeL) . For more information, please [contact](contact elibrary@aisnet.org)[ elibrary@aisnet.org](contact elibrary@aisnet.org). Analyzing Gender Differences in Information Systems Research Analyzing Gender Differences in Information Systems Research using Topic Modeling with Machine Learning Research-in-Progress Shimi Zhou Worcester Polytechnic Institute [szhou7@wpi.edu](szhou7@wpi.edu) Eleanor Loiacono William & Mary [eloiacono@wm.com](eloiacono@wm.com) ABSTRACT As we navigate through the digital age, information system (IS) plays an increasingly vital role in transforming businesses, shaping societal structures, and driving innovation. However, despite its importance, IS research is not immune to the widespread gender disparities found in many academic disciplines. A critical aspect of these disparities lies in the research topics pursued by men and women (Masiero & Aaltonen, 2020) . Several factors contribute to these gender-based topic preferences. Societal and cultural influences often play a fundamental role in shaping the research interests of scholars. From early socialization processes to societal norms, gender-based distinctions can direct individuals towards specific interests and career trajectories, ultimately influencing their choice of research topics (Muraya et al., 2019) . However, the implications of gender differences in research topics go beyond mere representation. A diversity of research perspectives in IS research, driven by balanced gender participation, is integral to fostering innovation and ensuring a holistic understanding of IS research (Vesseyet al., 2002) . Moreover, studies reveal that women in STEM, including IS, face pressures to conform to conventional research interests, sidelining more human-centric topics (Leslie et al., 2015) . This underscores the need to reassess our valuation of research and push for greater topic diversity in top-tier academic journals (Loiacono et al., 2016) . Therefore, this brings us to our central research inquiry: “How do the research topics in IS differ between men and women scholars, and what are the implications of these differences on the evolution and innovation within the IS domain?” To shed light on this issue, the study employs an unsupervised machine learning-based approach to identify and analyze trendsand gender differences in topics of IS research papers, specifically Latent Dirichlet Allocation (LDA) (Blei et al., 2003) . Our research sources data from the Web of Science, focusing on papers from Basket journals. Additionally, we incorporate a dataset that gathers gender data from various online and self-reported resources. After refining the abstracts and aligning them with the gender data, we apply LDA to subsets of abstracts written by men and women, aiming to reveal gender-specific topics. Significant differences in the topics pursued by men

FAQs

What central question does the study address about IS research?

How research topics in information systems differ between men and women scholars and what the implications are for the evolution and innovation of the IS domain.

What machine learning technique is used to analyze research topics?

Latent Dirichlet Allocation (LDA), an unsupervised topic modeling method, applied to abstract subsets written by men and women.

What data sources support the analysis of gender and IS research topics?

Web of Science records focusing on basket journals, along with a dataset that collects gender information from online and self-reported resources.

Aug 5, 2026

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