Political polarization has become a defining feature of online discourse, yet its long-term evolution remains poorly understood. We present a longitudinal analysis of ideological polarization in Reddit discussions by measuring semantic differences in the language used by opposing political communities. We construct temporally aligned community-specific word embeddings and quantify ideological polarization as the semantic divergence of political concepts over time. Our analysis shows that ideological polarization has increased substantially during the study period, both at the concept- and topic-level. Unlike prior computational work, which has largely focused on cross-sectional analyses or affective dimensions of polarization at a single point at time, our approach captures the evolution of ideological differences in semantic framing. The proposed framework provides a scalable method for studying the temporal dynamics of ideological polarization in large-scale social media discourse.
TSN4PI, a unified framework for tracking the evolution of political ideologies on social media, includes two core modules: the PIDN and the PIPN, which employs temporal graph neural networks to predict future ideological shifts, enabling comprehensive analysis of ideology presence, intensity, and evolution.
This research demonstrates that applying linguistically native AI models is critical for accurate sociological inference in the Russophone world, bridging the gap between machine learning precision and cultural hermeneutics.
R. Delhibabu, N. Zenkova· Journal of Computational Soc...· 0 citations
Abstract Polarization research has demonstrated how people cluster in homogeneous groups with opposing opinions. However, this effect emerges not only through interaction between people, limiting communication between groups, but also between narratives, shaping opinions and partisan identities. Yet, how polarized info...
Jan Elfes, M. Bastos, L. Aiello· PNAS Nexus· 0 citations
Today most media content is consumed based on algorithmic recommendations. Evidence suggests that this can lead to politically biased media consumption patterns. Automated extraction of political agendas from texts can reveal and analyze political biases in online media -- and thus help fostering politically unbiased m...
Political discourse on social networks continuously evolves across electoral cycles and communicative contexts, producing measurable changes in discourse distributions over time. This work introduces an unsupervised framework for detecting and quantifying these changes through multidimensional drift analysis. The app...
Ibai Guillén-Pacho, Carlos Badenes-Olmedo, Óscar Corcho· Social Network Analysis and...· 0 citations
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