Divided by discipline? A systematic literature review on the quantification of online sexism and misogyny using a semi-automated approach

2024-09-30

arXiv.org

AI Summary

This paper addresses persistent challenges in quantifying online gender-based discrimination and misogyny, exposing fundamental epistemological and methodological divergences between social sciences and computer science—particularly regarding conceptual definitions, measurement logic, and operationalization. Employing a PRISMA-guided, semi-automated systematic review, it integrates interdisciplinary theoretical frameworks to analyze literature from the past decade. The analysis identifies three critical gaps: (1) insufficient integration of intersectional perspectives; (2) underrepresentation of non-Western languages and sociocultural contexts; and (3) weak design of proactive, post-detection interventions beyond binary text classification. To bridge these gaps, the study pioneers an analytical framework that coherently unites social theory with computational modeling. It establishes a reproducible, cross-disciplinary review standard and proposes a next-generation research roadmap centered on a detection–interpretation–intervention feedback loop. This work advances both theoretical foundations and methodological tools for equitable, culturally responsive algorithmic governance.

Technology Category

• Application Domains: Humanities & Computational Social Science
• Humans and AI: Other Foundations of Human Computation & AI
• Computer Vision: Bias, Fairness & Privacy

Application Category

• Social Networks and Social Media: Fairness and bias in social network and social media analysis
• Web Mining and Content Analysis: Web data quality in the era of algorithmically-generated content
• Responsible Web: Algorithmic accountability and transparency on the web

Abstract

Several computational tools have been developed to detect and identify sexism, misogyny, and gender-based hate speech, particularly on online platforms. These tools draw on insights from both social science and computer science. Given the increasing concern over gender-based discrimination in digital spaces, the contested definitions and measurements of sexism, and the rise of interdisciplinary efforts to understand its online manifestations, a systematic literature review is essential for capturing the current state and trajectory of this evolving field. In this review, we make four key contributions: (1) we synthesize the literature into five core themes: definitions of sexism and misogyny, disciplinary divergences, automated detection methods, associated challenges, and design-based interventions; (2) we adopt an interdisciplinary lens, bridging theoretical and methodological divides across disciplines; (3) we highlight critical gaps, including the need for intersectional approaches, the under-representation of non-Western languages and perspectives, and the limited focus on proactive design strategies beyond text classification; and (4) we offer a methodological contribution by applying a rigorous semi-automated systematic review process guided by PRISMA, establishing a replicable standard for future work in this domain. Our findings reveal a clear disciplinary divide in how sexism and misogyny are conceptualized and measured. Through an evidence-based synthesis, we examine how existing studies have attempted to bridge this gap through interdisciplinary collaboration. Drawing on both social science theories and computational modeling practices, we assess the strengths and limitations of current methodologies. Finally, we outline key challenges and future directions for advancing research on the detection and mitigation of online sexism and misogyny.

Problem

• Reviewing computational tools for detecting online sexism and misogyny
• Bridging disciplinary gaps in defining and measuring online sexism
• Identifying challenges and future directions in online sexism research

Innovation

• Semi-automated systematic review using PRISMA guidelines
• Interdisciplinary lens combining social and computer science
• Automated detection methods for online sexism