This study employs AI-powered computational discourse analysis to examine Instagram® posts by 18 Colombian women politicians, focusing on polarisation, emotion, and manipulative narratives related to gender issues. Analysing 2843 relevant posts from a corpus of 37,308, the research utilises word frequency analysis, Latent Dirichlet Allocation (LDA), emotion detection, and semantic networks. Findings reveal a low lexical focus on “mujer” (woman), diverse thematic framings reflecting distinct policy priorities, and a predominant use of positive, trust-building sentiment, although with significant undercurrents of negative emotion. The study identifies patterns of strategic narrative construction, highlighting a complex interplay of performative advocacy, policy specialisation, and identity construction. This gendered digital political discourse is further characterized by a disregard for previously achieved women's rights, the reinforcement of gender stereotypes, and a pronounced polarisation along political lines, which is suggestive of “femwashing.” This research contributes to understanding women's digital political communication in Latin America. • AI analysis reveals distinct gender narratives on Instagram. • Discourse blends trust-building with underlying fear and anger. • Ideological divides hinder a unified women's rights agenda. • Semantic networks map strategic framing of gender issues. • Results suggest “femwashing” and reinforcement of stereotypes.
Pacheco-Ortiz et al. (Sat,) studied this question.
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