# When Words Divide: Diachronic Ideological Polarization in Political Discourse on Social Media

arXiv: https://arxiv.org/abs/2608.01176 · HTML: https://mlsanity.com/paper/2608.01176/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 26 references that are in the map.
- First posted on arXiv 2 Aug 2026.

## Builds on (most cited first, 9 of 9)

- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [SimLex-999: Evaluating Semantic Models with (Genuine) Similarity Estimation](https://mlsanity.com/paper/1408.3456/) (2014)
- [Analyzing Polarization in Social Media: Method and Application to Tweets on 21 Mass Shootings](https://mlsanity.com/paper/1904.01596/) (2019)
- [Native Language Cognate Effects on Second Language Lexical Choice](https://mlsanity.com/paper/1805.09590/) (2018)
- [Understanding who uses Reddit: Profiling individuals with a self-reported bipolar disorder diagnosis](https://mlsanity.com/paper/2104.11612/) (2021)
- [Gaining Insights into Unrecognized User Utterances in Task-Oriented Dialog Systems](https://mlsanity.com/paper/2204.05158/) (2022)
- [The Enemy from Within: A Study of Political Delegitimization Discourse in Israeli Political Speech](https://mlsanity.com/paper/2508.15524/) (2025)
- [On the Interplay between Musical Preferences and Personality through the Lens of Language](https://mlsanity.com/paper/2508.18208/) (2025)
- [Unveiling Affective Polarization Trends in Parliamentary Proceedings](https://mlsanity.com/paper/2512.05231/) (2025)

## Abstract (arXiv)

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.