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Inside the echo chamber: Linguistic underpinnings of misinformation on Twitter

Xinyu Wang, Jiayi Li, Sarah Rajtmajer

TL;DR

The paper investigates how linguistic signals underpin misinformation echo chambers on Twitter by comparing explicit and implicit group identity cues and processing fluency across misinformation topics. Using topic-specific user interaction networks, echo chambers are identified via strongly connected components, and a suite of metrics—including in-/out-group language, big words, readability, discourse connectives, and ambient affiliation via hashtags—are applied, with bootstrapped zero-inflated beta regressions to quantify effects ($ZIBR$). Key findings show increased group identity signals and processing fluency within echo chambers for some misinformation topics (notably US election fraud and QAnon), though patterns vary by topic (e.g., anti-vaccination differences) and ambient affiliation is more pronounced for certain topics. The work enhances understanding of the linguistic underpinnings of online misinformation, offering insights for targeted moderation and future research on information diffusion in social networks.

Abstract

Social media users drive the spread of misinformation online by sharing posts that include erroneous information or commenting on controversial topics with unsubstantiated arguments often in earnest. Work on echo chambers has suggested that users' perspectives are reinforced through repeated interactions with like-minded peers, promoted by homophily and bias in information diffusion. Building on long-standing interest in the social bases of language and linguistic underpinnings of social behavior, this work explores how conversations around misinformation are mediated through language use. We compare a number of linguistic measures, e.g., in-/out-group cues, readability, and discourse connectives, within and across topics of conversation and user communities. Our findings reveal increased presence of group identity signals and processing fluency within echo chambers during discussions of misinformation. We discuss the specific character of these broader trends across topics and examine contextual influences.

Inside the echo chamber: Linguistic underpinnings of misinformation on Twitter

TL;DR

The paper investigates how linguistic signals underpin misinformation echo chambers on Twitter by comparing explicit and implicit group identity cues and processing fluency across misinformation topics. Using topic-specific user interaction networks, echo chambers are identified via strongly connected components, and a suite of metrics—including in-/out-group language, big words, readability, discourse connectives, and ambient affiliation via hashtags—are applied, with bootstrapped zero-inflated beta regressions to quantify effects (). Key findings show increased group identity signals and processing fluency within echo chambers for some misinformation topics (notably US election fraud and QAnon), though patterns vary by topic (e.g., anti-vaccination differences) and ambient affiliation is more pronounced for certain topics. The work enhances understanding of the linguistic underpinnings of online misinformation, offering insights for targeted moderation and future research on information diffusion in social networks.

Abstract

Social media users drive the spread of misinformation online by sharing posts that include erroneous information or commenting on controversial topics with unsubstantiated arguments often in earnest. Work on echo chambers has suggested that users' perspectives are reinforced through repeated interactions with like-minded peers, promoted by homophily and bias in information diffusion. Building on long-standing interest in the social bases of language and linguistic underpinnings of social behavior, this work explores how conversations around misinformation are mediated through language use. We compare a number of linguistic measures, e.g., in-/out-group cues, readability, and discourse connectives, within and across topics of conversation and user communities. Our findings reveal increased presence of group identity signals and processing fluency within echo chambers during discussions of misinformation. We discuss the specific character of these broader trends across topics and examine contextual influences.
Paper Structure (37 sections, 4 figures, 6 tables)

This paper contains 37 sections, 4 figures, 6 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Misinformation dynamics
  4. Social identity formation
  5. Social identity theory.
  6. Ambient affiliation.
  7. Processing fluency
  8. Simplicity bolsters information processing.
  9. Elaboration bolsters information processing.
  10. Data
  11. Preliminaries
  12. Linguistic metrics
  13. In-/out-group identity language.
  14. Big words.
  15. Readability.
  16. ...and 22 more sections

Figures (4)

  • Figure 1: Top 10 hashtags, by density, in echo chambers (left) and overall (right) for misinformation topics.
  • Figure 2: US election fraud. Internal interaction network amongst members of the largest echo chamber (red) and other echo chamber members with which they interact.
  • Figure 3: Readability ease score box plot for 100 samples of aggregated text. ***p<0.001, +significance in the positive direction, -significance in the negative direction.
  • Figure 4: Heatmap of URL density of echo chamber interactions, non-echo chamber interactions (excluding isolates), and isolated tweets for misinformation topics