Social Media Algorithms and Gender Visibility in Political Discussions: A Systematic Review

Abstract

Social media platforms have become central to political communication, influencing how political information is produced, disseminated, and consumed. Beyond facilitating public engagement, these platforms rely on algorithmic recommendation systems that shape the visibility of political content and influence whose voices gain prominence in online discussions. Emerging scholarship suggests that algorithmic curation may reinforce existing gender inequalities by amplifying some political actors while marginalizing others through engagement-based ranking, personalization, and moderation practices. Despite growing research on algorithmic governance and digital politics, there remains limited synthesis of evidence concerning the relationship between social media algorithms and gender visibility in political discussions. This systematic review examines peer-reviewed literature published between 2015 and 2025 to analyze how recommendation algorithms, content moderation systems, and platform design affect the visibility of male and female political actors. Using the PRISMA framework, studies from Scopus, Web of Science, Google Scholar, and other academic databases are reviewed through thematic synthesis. The review identifies six recurring themes: Algorithmic amplification of political content, Gender bias in recommendation systems, Engagement-driven visibility, Online harassment and algorithmic suppression, Platform governance, and Implications for democratic participation. The findings suggest that while algorithms are designed to optimize user engagement rather than discriminate by gender, engagement-based ranking and historical behavioral patterns can contribute to unequal visibility and disproportionately affect women participating in political discussions. The review concludes by proposing an integrated conceptual framework linking algorithmic governance, gendered political communication, and democratic participation and identifies key directions for future interdisciplinary research.