💻 computer science

Among Us: Language of Conspiracy Theorists on Mainstream Reddit

This study analyzes a decade of Reddit data to demonstrate that while conspiracy theorists exhibit distinct linguistic patterns allowing for high-accuracy detection within specific communities, these signals are highly context-dependent and vary significantly across different forums, indicating that uniform moderation strategies are ineffective compared to tailored, community-specific approaches.

Original authors: Francesco Corso, Giuseppe Russo, Francesco Pierri, Gianmarco De Francisci Morales

Published 2026-04-22

📖 6 min read🧠 Deep dive

CC BY 4.0

Original authors: Francesco Corso, Giuseppe Russo, Francesco Pierri, Gianmarco De Francisci Morales

Original paper licensed under CC BY 4.0 (http://creativecommons.org/licenses/by/4.0/). ✨ This is an AI-generated explanation of the paper below. It is not written or endorsed by the authors. For technical accuracy, refer to the original paper. Read full disclaimer

The Big Idea: The "Chameleon" Conspiracy Theorist

Imagine you walk into a room full of people wearing different costumes. Some are dressed as chefs, some as astronauts, and some as clowns. Now, imagine there is a secret group of people who believe the moon is made of cheese.

Usually, we think these "cheese-believers" only talk about cheese when they are in their secret clubhouse. But this study asks a fascinating question: If a cheese-believer walks into the "Astronaut" room, do they still sound like a cheese-believer, or do they perfectly mimic the astronauts?

The researchers found that they do both. They have a unique "linguistic fingerprint" that gives them away, but they are also incredibly good at chameleon-like adaptation, changing their style to fit the room they are in.

How They Did It: The Digital Detective Work

The researchers (Francesco, Giuseppe, and their team) acted like digital detectives. Here is their game plan:

The Data: They looked at 500 million comments on Reddit over 10 years. That's like reading every single sentence spoken in a massive city for a decade.
The Groups:
- The "Suspects": People who had ever posted in r/conspiracy (the main hub for conspiracy theories).
- The "Regulars": People who posted in mainstream, normal subreddits like r/funny, r/science, or r/food, but never touched the conspiracy hub.
The Tool (LIWC): They didn't read the comments to understand the meaning (like "Is this about aliens?"). Instead, they used a software tool called LIWC (Linguistic Inquiry and Word Count). Think of this tool as a psychological X-ray. It doesn't care what you are saying; it cares how you are saying it.
- Does the person use a lot of angry words?
- Do they talk about death or illness?
- Do they use filler words like "um" or "you know"?
- Do they sound confident or anxious?

The Findings: The "Among Us" Moment

The study is titled "Among Us" because it's about spotting the "impostor" (the conspiracy theorist) hiding in plain sight among the "crewmates" (regular users).

1. The AI Can Spot Them (Most of the Time)

When the researchers trained a computer (a Random Forest model) to look at these "psychological X-rays," it became incredibly good at spotting the conspiracy users.

The Score: The computer was 87% accurate at telling a conspiracy theorist apart from a regular user just by looking at their language in normal forums.
The Analogy: Imagine a security guard at a party. Even if the spy is wearing a tuxedo and dancing with the crowd, the guard can spot them 87 times out of 100 just by the way they hold their drink or how they laugh.

2. The "One-Size-Fits-All" Trap

Here is the twist. The researchers tried to build one single master model to catch conspiracy theorists in every subreddit at once.

The Result: It failed. The master model was much worse (dropping accuracy by up to 17%) than building a specific detector for each room.
The Analogy: Imagine trying to use a single "Metal Detector" to find gold in a beach, a forest, and a mine.
- In the beach (a funny subreddit), the detector might get confused by the sand.
- In the forest (a science subreddit), the leaves might trigger false alarms.
- The Lesson: Conspiracy theorists change their "linguistic outfit" depending on where they are. In a science forum, they sound more analytical. In a food forum, they might sound more emotional. A single rulebook can't catch them everywhere.

3. The "Fingerprint" is Dynamic

The study found that these users aren't just repeating the same conspiracy script over and over. They are adapting.

If they are in r/food, they might talk about "hidden ingredients" with a tone of suspicion, but they use the vocabulary of a foodie.
If they are in r/science, they might question the "official data" using scientific-sounding words, but with a specific pattern of doubt.
The Takeaway: Their "conspiracy mindset" is like a virus that mutates to survive in different environments.

Why Does This Matter?

For Moderators (The Bouncers)

If you are running a social media platform, you can't just use one giant filter to catch bad actors.

Bad Strategy: "Block anyone who uses the word 'secret'." (This will catch innocent people talking about birthday surprises).
Good Strategy: You need context-aware tools. You need to know that "suspicion" looks different in a politics forum than it does in a gardening forum.

For Us (The Regular Users)

It turns out that "ideological echo chambers" (where people only talk to those who agree with them) aren't as isolated as we think.

Conspiracy theorists are mixing into our daily lives. They are posting in r/aww (cute animals) and r/food.
They aren't just hiding in the dark corners of the internet; they are wearing our clothes and speaking our language, but with a subtle, detectable "glitch" in their style.

The Ethical Warning (The "Big Brother" Problem)

The authors end with a serious warning. Just because we can build a machine to detect these linguistic patterns doesn't mean we should use it to spy on individuals.

The Risk: If a platform starts flagging people as "potential conspiracy theorists" just because they use a few specific words in a normal conversation, it could lead to unfair censorship or stigmatization.
The Lesson: We need to be careful. Detecting a pattern is useful for understanding society, but using it to punish individuals without proof of harm is dangerous.

Summary in One Sentence

Conspiracy theorists are like master chameleons who can blend into any online community, but they leave behind a subtle, detectable "linguistic scent" that changes depending on the room they are in, making them hard to catch with a single rule but easy to spot with a tailored, context-aware approach.

1. Problem Statement

The paper addresses the challenge of understanding how users active in fringe conspiracy communities (specifically r/conspiracy) express themselves when participating in mainstream, general-interest online spaces (e.g., humor, news, hobbies).

While extensive research exists on detecting conspiracy content within conspiracy forums, it remains unclear whether the linguistic patterns associated with a "conspiracy mindset" are:

Context-dependent: Confined only to conspiracy-specific spaces.
Intrinsic: A stable, detectable feature of the user's communication style that persists even when they discuss neutral topics (e.g., cooking, science, or movies) in mainstream subreddits.

The core research question is: Do users who engage with conspiracy theories display distinctive, detectable linguistic patterns in mainstream online communities compared to non-engaged users?

2. Methodology

Data Collection

Source: Pushshift Reddit dataset (public archive).
Scope: Over 500 million comments spanning 10 years (2013–2023).
Target Groups:
- Positive Class: Users who posted at least one comment in r/conspiracy (approx. 980k unique users).
- Negative Class: Users active in 22 top mainstream subreddits (e.g., r/funny, r/science, r/aww) who have no history in r/conspiracy.
Preprocessing:
- Excluded bots and users with fewer than 20 comments in a specific mainstream subreddit to ensure signal strength.
- Balanced datasets by randomly sampling an equal number of negative-class users for each positive-class user per subreddit.

Feature Extraction

Tool: LIWC-22 (Linguistic Inquiry and Word Count), a dictionary-based tool for psycholinguistic analysis.
Features: Extracted 110 linguistic and psycholinguistic features per comment (e.g., emotional tone, cognitive processes, social concerns, punctuation).
Aggregation: For each user in a specific subreddit, the feature vectors of all their comments were averaged to create a single user-level embedding representing their psycholinguistic profile in that context.

Experimental Design

Model: Random Forest Classifier (chosen for robustness and interpretability).
Task: Binary classification (Conspiracy-engaged vs. Non-engaged) performed independently for each of the 22 mainstream subreddits.
Validation:
- 5-fold cross-validation with hyperparameter tuning.
- Permutation Tests: 100 label shuffles to establish statistical significance ( $p < 0.001$ ).
- Robustness Checks:
  - Tested alternative positive classes (r/MensRights, r/AskReddit) to ensure signals weren't generic to any ideological or general forum.
  - Temporal Analysis: Checked if accuracy changed based on the timing or volume of prior activity.
  - Socio-demographic Control: Used subreddit embeddings (Waller & Anderson, 2021) to ensure results weren't driven by demographic biases (age, gender, politics).

Feature Importance Analysis

SHAP (SHapley Additive exPlanations): Used to quantify the contribution of each LIWC feature to the model's decision.
Clustering: Hierarchical clustering (UPGMA) and PCA were applied to SHAP values to determine if linguistic "fingerprints" are consistent across communities or vary by context.

3. Key Contributions

Discovery of Cross-Context Linguistic Signatures: The study provides empirical evidence that conspiracy-engaged users exhibit distinctive psycholinguistic patterns even in completely unrelated, mainstream topics (e.g., discussing food or movies).
Context-Dependency of Expression: It demonstrates that while these users are distinguishable, their linguistic expression is highly dynamic. There is no single "global" conspiracy language; rather, users adapt their style to the specific norms of the community they are currently inhabiting.
Limitations of Global Models: The paper proves that a single aggregate model trained on all data fails to capture these nuances, performing significantly worse than community-specific models.
Methodological Framework: Establishes a rigorous pipeline for detecting fringe subcultures in mainstream spaces using psycholinguistic features and interpretable machine learning.

4. Key Results

Classification Performance

High Accuracy: The Random Forest classifier achieved a median accuracy of 87% (ranging from 78% to 95%) across the 22 mainstream subreddits.
Statistical Significance: Results were highly significant ( $p < 0.001$ ) compared to random baselines (50%).
Control Groups:
- Models trained on r/MensRights (ideologically adjacent) achieved lower accuracy (Median 69%).
- Models trained on r/AskReddit (general interest) achieved the lowest accuracy (Median 60%).
- Conclusion: The linguistic signals of conspiracy engagement are more distinct than those of general ideological or interest-based communities.

The "Global vs. Local" Gap

A Single Aggregate Model (trained on data from all subreddits combined) achieved only 76% accuracy.
Community-specific models outperformed the global model by 2.5 to 17 percentage points.
Implication: Conspiracy users do not maintain a uniform "conspiratorial" tone; they shift their register to match the local community norms while retaining subtle, detectable deviations.

Feature Analysis (SHAP)

No Universal Fingerprint: Feature importance varied significantly across subreddits.
- Example: In r/science, "mental" (reasoning/cognitive processes) was a key discriminator.
- Example: In r/food and r/DIY, "emo_anger" was a strong predictor.
Recurring Themes: Despite variability, negative emotions (anger, anxiety) and negative themes (death, illness) frequently appeared as discriminators, though their specific impact (positive or negative) depended on the subreddit context.

Temporal and Demographic Robustness

Temporal Stability: Classification accuracy remained stable regardless of when the user posted or how many comments they made prior to joining r/conspiracy, suggesting the linguistic style is a stable trait rather than a result of recent exposure.
Demographics: Socio-demographic differences (inferred via subreddit embeddings) showed no meaningful correlation with classification accuracy, confirming the results are driven by linguistic style, not demographic bias.

5. Significance and Implications

For Content Moderation

"One-Size-Fits-All" is Flawed: The study argues against using a single global classifier to detect conspiracy content across the entire platform. Effective detection requires context-aware strategies that account for the specific linguistic norms of each community.
Early Detection: Since these linguistic signals exist in mainstream spaces, it may be possible to identify at-risk users before they fully radicalize or migrate to fringe forums.

For Social Science

Blurring of Echo Chambers: The findings challenge the idea that ideological signaling is strictly compartmentalized within radical forums. Mainstream spaces act as "mixing grounds" where distinct linguistic identities remain visible.
Adaptive Behavior: It highlights the sophisticated ability of users to adapt their language to social contexts while maintaining underlying cognitive or psychological traits.

Ethical Considerations

Risk of Profiling: The authors warn that these techniques could be misused for individual-level ideological profiling in non-political spaces, potentially leading to unjustified scrutiny or stigmatization.
Recommendation: The results should not be used for preemptive content removal or de-platforming, as conspiracy theories are resilient to suppression, and heavy-handed enforcement may reinforce beliefs.

Conclusion

The paper concludes that conspiracy-engaged users possess a detectable, yet context-dependent, linguistic signature. While they adapt to the norms of mainstream communities, their psycholinguistic profile remains distinct enough to be identified with high accuracy. This necessitates a shift in moderation strategies from global, static filters to dynamic, community-specific approaches.