Beyond the Campaign Trail: Gendered Patterns of Negativity and Issue Emphasis in Politicians’ Social Media Communication During Routine Times

Research on gender and political communication shows that gender stereotypes shape the kinds of messages men and women politicians produce (Winfrey & Schnoebelen, 2019). Yet most existing work focuses on election campaigns or traditional media, where communication is mediated by party strategies, journalistic norms, and gender-equality pressures (Aaldering & van der Pas, 2020). These studies often rely on small-N datasets and manual content coding, offering only a limited snapshot of gendered communication during exceptional periods of heightened visibility. As a result, we know little about whether such patterns persist in the routine, day-to-day communication of elected representatives (Aaldering & van der Pas, 2020). This paper addresses this gap by examining gender differences in legislators’ social media use during non-campaign periods. Building on theories of gendered political socialization and role congruity, we argue that women politicians face stronger normative constraints against overt aggression and incivility (Schneider & Bos, 2019, Evans & Clark, 2016). Consequently, we expect women not only to engage less frequently in negative discourse overall, but also to be less likely to use uncivil forms of attack. When women do go negative, we anticipate that they rely more on issue-based, policy-oriented criticism and less on personal or trait-based attacks. Extending insights from issue ownership and gendered issue framing, we further expect women to emphasize soft-policy domains and to direct criticism primarily within these areas where they enjoy greater perceived credibility (Eagly & Karau, 2002). Empirically, the study analyzes nearly 250,000 tweets published between May 2019 and April 2023 by all 425 elected members of the Belgian federal, Flemish, and Walloon parliaments. Belgium’s multi-party, gender-balanced system provides a valuable context for observing routine political communication beyond the dominant US–UK cases. Using large language models for computational zero-shot text classification, each tweet was coded for attack behavior, incivility, attack type (issue-based and trait-based), and issue domain (soft-policy and hard-policy). This enables a detailed longitudinal analysis of how gender shapes both the likelihood and the form of negativity in everyday digital communication. By combining large-scale computational text analysis with theories of gendered representation, the paper contributes to efforts to understand how gender structures political speech, digital engagement, and patterns of democratic representation. Methodologically, it illustrates how machine-learning approaches can illuminate mechanisms underlying gendered political outcomes and highlights the value of computational methods for uncovering systematic patterns in contemporary political communication. In doing so, the study bridges subfields of gender and political communication and sheds new light on how individual behavior shapes women’s political presence.