Involvement drives complexity of language in online debates

TL;DR

This study investigates how the involvement of users in online debates influences the linguistic complexity of their language, revealing significant variations based on account type, political stance, content reliability, and sentiment.

Contribution

It introduces a comprehensive analysis of linguistic complexity across multiple dimensions in social media debates, highlighting how involvement shapes language use in online discourse.

Findings

Influential users show significant differences in language complexity based on account type and political leaning.

Profiles with negative and offensive content tend to use more complex language.

Users with similar political and reliability profiles converge in their linguistic patterns.

Abstract

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.