# Involvement drives complexity of language in online debates

arXiv: https://arxiv.org/abs/2506.22098 · HTML: https://mlsanity.com/paper/2506.22098/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 75 references that are in the map.
- First posted on arXiv 27 Jun 2025.

## Builds on (most cited first, 16 of 16)

- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [ChatGPT Outperforms Crowd-Workers for Text-Annotation Tasks](https://mlsanity.com/paper/2303.15056/) (2023)
- [BERTopic: Neural topic modeling with a class-based TF-IDF procedure](https://mlsanity.com/paper/2203.05794/) (2022)
- [TweetEval: Unified Benchmark and Comparative Evaluation for Tweet Classification](https://mlsanity.com/paper/2010.12421/) (2020)
- [BERTweet: A pre-trained language model for English Tweets](https://mlsanity.com/paper/2005.10200/) (2020)
- [TimeLMs: Diachronic Language Models from Twitter](https://mlsanity.com/paper/2202.03829/) (2022)
- [COVID-Twitter-BERT: A Natural Language Processing Model to Analyse COVID-19 Content on Twitter](https://mlsanity.com/paper/2005.07503/) (2020)
- [Shifting Polarization and Twitter News Influencers between two U.S. Presidential Elections](https://mlsanity.com/paper/2111.02505/) (2021)
- [The Statistical Physics of Real-World Networks](https://mlsanity.com/paper/1810.05095/) (2018)
- [Inferring monopartite projections of bipartite networks: an entropy-based approach](https://mlsanity.com/paper/1607.02481/) (2016)
- [Tweets in Time of Conflict: A Public Dataset Tracking the Twitter Discourse on the War Between Ukraine and Russia](https://mlsanity.com/paper/2203.07488/) (2022)
- [Sentiment analysis in tweets: an assessment study from classical to modern text representation models](https://mlsanity.com/paper/2105.14373/) (2021)
- [Ideology and polarization set the agenda on social media](https://mlsanity.com/paper/2412.05176/) (2024)
- [Ideological Fragmentation of the Social Media Ecosystem: From echo chambers to echo platforms](https://mlsanity.com/paper/2411.16826/) (2024)
- [Highly engaging events reveal semantic and temporal compression in online community discourse](https://mlsanity.com/paper/2306.14735/) (2023)
- [Decoding Musical Evolution Through Network Science](https://mlsanity.com/paper/2501.07557/) (2025)

## Abstract (arXiv)

Language is a fundamental aspect of human societies, continuously evolving in response to various stimuli, including societal changes and intercultural interactions. Technological advancements have profoundly transformed communication, with social media emerging as a pivotal force that merges entertainment-driven content with complex social dynamics. As these platforms reshape public discourse, analyzing the linguistic features of user-generated content is essential to understanding their broader societal impact. In this paper, we examine the linguistic complexity of content produced by influential users on Twitter across three globally significant and contested topics: COVID-19, COP26, and the Russia-Ukraine war. By combining multiple measures of textual complexity, we assess how language use varies along four key dimensions: account type, political leaning, content reliability, and sentiment. Our analysis reveals significant differences across all four axes, including variations in language complexity between individuals and organizations, between profiles with sided versus moderate political views, and between those associated with higher versus lower reliability scores. Additionally, profiles producing more negative and offensive content tend to use more complex language, with users sharing similar political stances and reliability levels converging toward a common jargon. Our findings offer new insights into the sociolinguistic dynamics of digital platforms and contribute to a deeper understanding of how language reflects ideological and social structures in online spaces.