# Language Mutations Sustain the Persistences of Conspiracy Theories on Social Media

arXiv: https://arxiv.org/abs/2605.20050 · HTML: https://mlsanity.com/paper/2605.20050/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 75 references that are in the map.
- First posted on arXiv 19 May 2026.

## Builds on (most cited first, 7 of 7)

- [Generating Scientific Claims for Zero-Shot Scientific Fact Checking](https://mlsanity.com/paper/2203.12990/) (2022)
- [Claim Matching Beyond English to Scale Global Fact-Checking](https://mlsanity.com/paper/2106.00853/) (2021)
- [An automated pipeline for the discovery of conspiracy and conspiracy theory narrative frameworks: Bridgegate, Pizzagate and storytelling on the web](https://mlsanity.com/paper/2008.09961/) (2020)
- [A Universal Prompting Strategy for Extracting Process Model Information from Natural Language Text using Large Language Models](https://mlsanity.com/paper/2407.18540/) (2024)
- [Analyzing Misinformation Claims During the 2022 Brazilian General Election on WhatsApp, Twitter, and Kwai](https://mlsanity.com/paper/2401.02395/) (2024)
- [The Half-Life of a Tweet](https://mlsanity.com/paper/2302.09654/) (2023)
- [ConspEmoLLM: Conspiracy Theory Detection Using an Emotion-Based Large Language Model](https://mlsanity.com/paper/2403.06765/) (2024)

## Abstract (arXiv)

This study investigates how language mutations affect the persistent diffusion of conspiracy theories on social media. Drawing on a three-year dataset of conspiracy-related posts from X, and applying computational linguistic analysis alongside survival modelling, we find that conspiracy claims with greater semantic mutations have substantially longer lifespans. Mutations in psycholinguistic properties, including pronouns, social reference words, cognitive process terms, risk- and health- related vocabularies, are associated with extended lifespans. Mutations in actor, action and target (AAT) categories are associated with longer lifespans as well. Qualitative analysis identifies two predominant mutation patterns: simplification and assimilation, at both linguistic and AAT structural levels. Taken together, the results advance our understanding of how language mutations contribute to conspiracy persistence online and shed lights on longitudinal content moderation strategies. We argue that content moderation should consider the mutability of conspiracy claims and focus on the core claims that can address their potential variations.