Language Mutations Sustain the Persistences of Conspiracy Theories on Social Media
Mirrored from arXiv — NLP / Computation & Language for archival readability. Support the source by reading on the original site.
Computer Science > Computation and Language
Title:Language Mutations Sustain the Persistences of Conspiracy Theories on Social Media
Abstract: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.
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2605.20050 [cs.CL] |
| (or arXiv:2605.20050v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2605.20050
arXiv-issued DOI via DataCite (pending registration)
|
Submission history
From: Calvin Yixiang Cheng [view email][v1] Tue, 19 May 2026 16:06:41 UTC (8,647 KB)
Access Paper:
- View PDF
- TeX Source
References & Citations
Bibliographic and Citation Tools
Code, Data and Media Associated with this Article
Demos
Recommenders and Search Tools
arXivLabs: experimental projects with community collaborators
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
More from arXiv — NLP / Computation & Language
-
The Annotation Scarcity Paradox in Low-Resource NLP Evaluation: A Decade of Acceleration and Emerging Constraints
May 20
-
Benchmarking Commercial ASR Systems on Code-Switching Speech: Arabic, Persian, and German
May 20
-
ReacTOD: Bounded Neuro-Symbolic Agentic NLU for Zero-Shot Dialogue State Tracking
May 20
-
Agent Meltdowns: The Road to Hell Is Paved with Helpful Agents
May 20
Discussion (0)
Sign in to join the discussion. Free account, 30 seconds — email code or GitHub.
Sign in →No comments yet. Sign in and be the first to say something.