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Among Us: Language of Conspiracy Theorists on Mainstream Reddit

Conference: ACL 2026
arXiv: 2506.05086
Code: N/A
Area: Social Computing
Keywords: conspiracy theory, language features, Reddit analysis, psycholinguistics, community adaptation

TL;DR

Analyzing 500 million Reddit comments over 10 years of longitudinal data, this study finds that users active in conspiracy theory communities exhibit detectable unique language patterns in mainstream communities (average 87% classification accuracy), but these patterns are highly context-dependent, with community-specific models outperforming global models by up to 17 percentage points.

Background & Motivation

Background: Conspiracy theories are not merely fringe beliefs—they are associated with vaccine hesitancy, public health risks, and even threats to democratic institutions (e.g., the 2021 Capitol incident). Existing research primarily focuses on detecting conspiracy theory content while neglecting how conspiracy theory believers express themselves in mainstream spaces.

Limitations of Prior Work: (1) It is known that conspiracy theorists use specific rhetorical styles and vocabulary, but it remains unclear whether these patterns are confined to conspiracy spaces or permeate mainstream communication; (2) Existing detection methods mostly focus on content-level features (e.g., topic words), neglecting language style features independent of discussion topics.

Key Challenge: Do conspiracy theorists possess a "conspiracy mindset" (monological worldview) that pervades all their communication, or can they fully adapt to different communities' linguistic norms?

Goal: Systematically examine the linguistic distinguishability of conspiracy community users in mainstream spaces using large-scale longitudinal data.

Key Insight: Use LIWC-22 psycholinguistic features (rather than topic words) to construct user language profiles, training separate classifiers for each of 22 mainstream communities.

Core Idea: Conspiracy theory users' language is indeed distinguishable, but the distinguishing patterns are highly community-dependent—no single global model can capture these patterns, requiring community-specific analysis.

Method

Overall Architecture

Data collection (all r/conspiracy comments + 22 mainstream communities) → LIWC-22 feature extraction (110 dimensions) → user-level feature aggregation → Random Forest classifier trained per community → feature importance analysis (SHAP values) → cross-community similarity analysis.

Key Designs

  1. Large-Scale Longitudinal Data Construction:

    • Function: Provide reliable long-term language behavior data
    • Mechanism: Extract approximately 510 million comments from 2013–2023 from the Pushshift Reddit dataset, covering 980,000 users of r/conspiracy and 22 mainstream communities. Exclude bots and low-activity users (<20 comments)
    • Design Motivation: Sufficient comment volume is needed to construct stable user language profiles; short-term or sparse data may be dominated by noise

  2. Community-Specific Classification Experiments:

    • Function: Test whether linguistic distinguishability is consistent across communities
    • Mechanism: Train an independent Random Forest for each mainstream community; positive class = users who have commented in r/conspiracy, negative class = randomly sampled equal-sized ordinary users. Repeat 5 times with random sampling to reduce variance. Permutation tests verify statistical significance
    • Design Motivation: Classifiers are not the goal itself but serve as proxy tools for quantifying linguistic distinguishability

  3. SHAP Feature Importance Analysis and Cross-Community Clustering:

    • Function: Reveal which language features are most discriminative and whether distinguishing patterns are similar across communities
    • Mechanism: Compute SHAP values for each community model to obtain 110-dimensional feature importance vectors, then use cosine similarity + hierarchical clustering to analyze cross-community pattern similarity
    • Design Motivation: If all communities use the same features for discrimination, there exists a global "conspiracy language"; if features vary by community, language expression is context-adaptive

Loss & Training

Random Forest with grid search and 5-fold cross-validation for hyperparameter tuning, 80/20 train-test split. Feature normalization performed only on training data. 100 permutation tests evaluate statistical significance.

Key Experimental Results

Main Results

Metric Value Note
Average classification accuracy 87% Binary classification across 20+ communities
Community-specific vs. global Up to +17pp Community-specific models significantly outperform global models
Statistical significance p<0.01 Permutation tests significant for all communities

Ablation Study

Config Key Metric Note
Activity threshold Higher activity performs better More comments → more stable language profiles
r/AskReddit as positive Accuracy ~random General community users cannot be distinguished (negative control)
r/MensRights as positive Medium accuracy Ideological communities also show partial discriminability

Key Findings

  • Conspiracy theory users' language is indeed detectable in mainstream spaces—average 87% accuracy far exceeds random
  • But no single global model captures these patterns—community-specific models outperform global models by up to 17 percentage points
  • This indicates conspiracy theory users' language expression is dynamically adaptive—while they have distinctive features, they adjust according to community norms
  • r/AskReddit users (negative control) cannot be distinguished, validating effect specificity

Highlights & Insights

  • "Distinguishable but context-dependent" is an elegant finding—supporting the existence of a "conspiracy mindset" while showing it is not a simple global label
  • Direct implications for content moderation strategies—unified detection models are insufficient; community-customized approaches are needed
  • Using LIWC psycholinguistic features (rather than topic words) ensures analysis targets language style rather than discussion content

Limitations & Future Work

  • Equating "having commented in r/conspiracy" with "conspiracy theory believer" may be overly broad
  • LIWC's dictionary-based approach may miss emerging language patterns
  • Only analyzes the Reddit platform; patterns on other social media may differ
  • Future work could combine content analysis and style analysis for more fine-grained research
  • vs Content detection methods: Focuses on language style rather than content, revealing deeper cognitive traits
  • vs User pathway studies: Rather than tracking how users enter conspiracy communities, analyzes their behavior in mainstream spaces
  • vs Community detection: Reveals cross-community behavioral adaptability, complementing community boundary research

Rating

  • Novelty: ⭐⭐⭐⭐ Studies conspiracy theory users' cross-community behavior from a language style perspective
  • Experimental Thoroughness: ⭐⭐⭐⭐⭐ 500 million comments, 10-year longitudinal data, 22 communities, statistical tests
  • Writing Quality: ⭐⭐⭐⭐ Rigorous research design with comprehensive negative controls
  • Value: ⭐⭐⭐⭐ Practical guidance for social media governance and conspiracy theory research

title: >- [Paper Note] Among Us: Language of Conspiracy Theorists on Mainstream Reddit description: >- [ACL 2026][Social Computing] Analyzing 500 million Reddit comments over 10 years of longitudinal data, this study finds that users active in conspiracy communities exhibit detectable unique linguistic patterns in mainstream communities (average 87% classification accuracy), but these patterns are highly context-dependent—community-specific models outperform global models by up to 17 percentage points. tags: - ACL 2026 - Social Computing - Conspiracy Theories - Linguistic Features - Reddit Analysis - Psycholinguistics date: 2026-05-08 content_hash: d1e601467bf457c4

Among Us: Language of Conspiracy Theorists on Mainstream Reddit

Conference: ACL 2026
arXiv: 2506.05086
Code: N/A
Area: Social Computing
Keywords: conspiracy theories, linguistic features, Reddit analysis, psycholinguistics, community adaptability

TL;DR

Analyzing 500 million Reddit comments over 10 years of longitudinal data, this study finds that users active in conspiracy communities exhibit detectable unique linguistic patterns in mainstream communities (average 87% classification accuracy), but these patterns are highly context-dependent—community-specific models outperform global models by up to 17 percentage points.

Background & Motivation

Background: Conspiracy theories are not merely fringe beliefs—they are linked to vaccine hesitancy, public health risks, and even threats to democratic institutions (e.g., the 2021 Capitol incident). Existing research primarily focuses on detecting conspiracy content but ignores how conspiracy believers express themselves in mainstream spaces.

Limitations of Prior Work: (1) It is known that conspiracy theorists use specific rhetorical styles and vocabulary, but it is unclear whether these patterns are confined to conspiracy spaces or permeate mainstream communication; (2) Existing detection methods mostly focus on content-level features (e.g., topic words) while ignoring stylistic linguistic features independent of discussion topics.

Key Challenge: Do conspiracy theorists possess a "conspiratorial mindset" (monological worldview) that permeates all their communication, or can they fully adapt to different communities' linguistic norms?

Goal: Use large-scale longitudinal data to systematically test the linguistic distinguishability of conspiracy community users in mainstream spaces.

Key Insight: Use LIWC-22 psycholinguistic features (rather than topic words) to build user linguistic profiles, training separate classifiers on 22 mainstream communities.

Core Idea: Conspiracy users' language is indeed distinguishable, but the distinguishing patterns are highly community-dependent—no single global model can capture these patterns; community-specific analysis is required.

Method

Overall Architecture

Data collection (all r/conspiracy comments + 22 mainstream communities) → LIWC-22 feature extraction (110 dimensions) → User-level feature aggregation → Random Forest classifier training per community → Feature importance analysis (SHAP values) → Cross-community similarity analysis.

Key Designs

  1. Large-Scale Longitudinal Data Construction:

    • Function: Provide reliable long-term linguistic behavior data
    • Mechanism: Extract approximately 510 million comments from the Pushshift Reddit dataset spanning 2013–2023, covering 980K users of r/conspiracy and 22 mainstream communities. Bots and low-activity users (<20 comments) are excluded
    • Design Motivation: Sufficient comment volume is needed to build stable user linguistic profiles; short-term or sparse data may be dominated by noise

  2. Community-Specific Classification Experiments:

    • Function: Test whether linguistic distinguishability is consistent across communities
    • Mechanism: Train independent Random Forest classifiers for each mainstream community, with positive class = users who have commented in r/conspiracy, negative class = randomly sampled equal-sized ordinary users. Repeat 5 times with random sampling to reduce variance. Permutation tests validate statistical significance
    • Design Motivation: Classifiers are not the end goal but rather proxy tools to quantify linguistic distinguishability

  3. SHAP Feature Importance Analysis and Cross-Community Clustering:

    • Function: Reveal which linguistic features are most discriminative and whether discrimination patterns are similar across communities
    • Mechanism: Compute SHAP values for each community model to obtain 110-dimensional feature importance vectors, then analyze cross-community pattern similarity using cosine similarity + hierarchical clustering
    • Design Motivation: If all communities use the same features for discrimination, there exists a global "conspiracy language"; if features vary by community, linguistic expression is context-adaptive

Loss & Training

Random Forest with grid search and 5-fold cross-validation for hyperparameter tuning, 80/20 train-test split. Feature normalization performed only on training data. 100 permutation tests evaluate statistical significance.

Key Experimental Results

Main Results

Metric Value Note
Average classification accuracy 87% Binary classification across 20+ communities
Community-specific vs. global Up to +17pp Community-specific models significantly outperform global models
Statistical significance p<0.01 Permutation tests significant for all communities

Ablation Study

Config Key Metric Note
Activity threshold Higher activity → better results More comments → more stable linguistic profiles
r/AskReddit as positive Accuracy ~random General community users cannot be distinguished (negative control)
r/MensRights as positive Medium accuracy Ideological communities also have some discriminative power

Key Findings

  • Conspiracy users' language is indeed detectable in mainstream spaces—87% average accuracy far exceeds random
  • No single global model can capture these patterns—community-specific models outperform global models by up to 17 percentage points
  • This indicates conspiracy users' linguistic expression is dynamically adaptive—while exhibiting distinctive features, they adjust according to community norms
  • r/AskReddit users (negative control) cannot be distinguished, validating the specificity of the effect

Highlights & Insights

  • "Distinguishable but context-dependent" is a nuanced finding—it both supports the existence of a "conspiratorial mindset" and shows it is not a simple global label
  • Direct implications for content moderation strategies—unified detection models are insufficient; community-customized approaches are needed
  • Using LIWC psycholinguistic features (rather than topic words) ensures analysis targets linguistic style rather than discussion content

Limitations & Future Work

  • Equating "having commented in r/conspiracy" with "being a conspiracy believer" may be overly broad
  • LIWC's dictionary-based approach may miss emerging linguistic patterns
  • Only one platform (Reddit) is analyzed; patterns on other social media may differ
  • Future work can combine content analysis and style analysis for more fine-grained research
  • vs Content detection methods: Focuses on linguistic style rather than content, revealing deeper cognitive characteristics
  • vs User pathway research: Does not track how users enter conspiracy communities, but analyzes their behavior in mainstream spaces
  • vs Community detection: Reveals cross-community behavioral adaptability, complementing community boundary research

Rating

  • Novelty: ⭐⭐⭐⭐ Studies conspiracy users' cross-community behavior from a linguistic style perspective
  • Experimental Thoroughness: ⭐⭐⭐⭐⭐ 500 million comments, 10-year longitudinal data, 22 communities, statistical testing
  • Writing Quality: ⭐⭐⭐⭐ Rigorous research design with thorough negative controls
  • Value: ⭐⭐⭐⭐ Practical guidance for social media governance and conspiracy theory research