Measuring the Semantic Structure and Evolution of Conspiracy Theories

Manisha Keim; Sarmad Chandio; Osama Khalid; Rishab Nithyanand

cs.CLMar 27, 2026

Measuring the Semantic Structure and Evolution of Conspiracy Theories

Authors: Manisha Keim, Sarmad Chandio, Osama Khalid, Rishab Nithyanand

Organizations: University of Iowa, Iowa City

Abstract

Research on conspiracy theories has largely focused on belief formation, exposure, and diffusion, while paying less attention to how their meanings change over time. This gap persists partly because conspiracy-related terms are often treated as stable lexical markers, making it difficult to separate genuine semantic changes from surface-level vocabulary changes. In this paper, we measure the semantic structure and evolution of conspiracy theories in online political discourse. Using 169.9M comments from Reddit's r/politics subreddit spanning 2012--2022, we first demonstrate that conspiracy-related language forms coherent and semantically distinguishable regions of language space, allowing conspiracy theories to be treated as semantic objects. We then track how these objects evolve over time using aligned word embeddings, enabling comparisons of semantic neighborhoods across periods. Our analysis reveals that conspiracy theories evolve non-uniformly, exhibiting patterns of semantic stability, expansion, contraction, and replacement that are not captured by keyword-based approaches alone.

Figures & tables

Category	Concept Label	Conspiracy Description
		[ samples from semantic neighborhood ]
Staged Violence	Crisis actor	Victims and survivors of violent events are hired actors.
		[ hoaxer, paid actor ]
	False flag	Acts committed with the intent to pin blame on another party.
		[ Soros paid, inside job ]
	Sandy Hook	The school shooting was staged to advance gun control.

Table 1: Overview of conspiracies, their concept labels, and sample words/phrases from their identified neighborhoods.

Figure 1: t-SNE projection of Word2Vec vocabulary highlighting conspiracy concept regions

	Level 1	Level 2	Level 3
	(Closest)		(Farthest)
Average accuracy across all annotators
Accuracy (%)	60.8%	70.4%	72.9%
Bootstrap comparisons computed relative to Level 3 (farthest)
$\Delta_{L3}$	-12.6%	-4.0%	–
95% CI ΔL3	[ $-15.9$ , $-9.3$ ]	[ $-7.3$ , $-0.8$ ]	–

Table 2: Average annotator accuracy broken down by semantic distance levels from the conspiracy space and bootstrap-based 95% confidence intervals for differences in mean annotator accuracy at each level relative to Level 3 (farthest). The differences in annotator accuracy were statistically significant across all three levels.

Pattern	HAD	CSD	NCD	LO
Sem. Stability	Low	Low	Low	High
Sem. Narrowing	High	Low	Low
Sem. Replacement	High	High
Sem. (De)fragmentation			High	High
Lex. Replacement	Low	Low	Low	Low

Table 3: Rules for assigning conspiracy theories to evolutionary patterns based on semantic and lexical metrics. A ’*’ indicates that the metric may take any value.

Panel of Figure 2 — Figure 2: Relational reorganization of the conspiracy landscape across time. Cell values indicate the change in semantic similarity between conspiracy theories, blue cells indicate convergence and brown cells indicate divergence. Only statistically significant changes are shown.

Appendix figures & tables1 asset

Supplementary material from the paper’s appendix.

Appendix

Concept	Words per Level	Sample Words Removed
sandy_hook	[10264, 252, 41, 30 ]	accidentally_shot, james_holmes, jared_loughner, shooting_since
chemtrails	[10418, 460, 294, 278 ]	canola, cask, coral_reef, fished, lead_based, sprayed_roundup
crisis_actor	[4298, 26, 16 ]	audio_tape, feud, operation_chaos
birther	[4298, 69, 39 ]	bullshit_attack, called_romney, crazy_statement, mccain_pow, medium_went

Appendix

Table 4: Sample sub-clustering results for selected concept labels. Word counts at each iteration are shown in sequence; the final value denotes the final neighborhood size after recursive sub-clustering converges.

Semantic Change Detection

Semantic change detection identifies how meanings, concepts, or scene contents differ across time in text, images, and other data.

See all 62 papers

Language Mutations Sustain the Persistences of Conspiracy Theories on Social Media
May 19, 2026Calvin Yixiang Cheng, Dorian Quelle, Scott A. HaleSocial Media Analysis
Oxford Internet Institute, University of Oxford, 1 St Giles’, Oxford, OX1 3JS, United Kingdom · Meedan, San Francisco, 94105, USA · Department of Mathematics, University of Zurich, Zurich, 8057, Switzerland
When Words Divide: Diachronic Ideological Polarization in Political Discourse on Social Media
Aug 2, 2026Roy Yitzchak, Noa Lavie, Ella RabinovichSocial Media Analysis Political Discourse Analysis
The Academic College of Tel-Aviv Yaffo, Israel
Comment-level Topic Drift Analysis in the Reddit Corpus
Aug 19, 2026Steven Morse, Daniel Runfola, Trenton W. FordSemantic Change Detection Topic Modeling
William & Mary

May 19, 2026J/K move · Enter open · S save

Language Mutations Sustain the Persistences of Conspiracy Theories on Social Media

Calvin Yixiang Cheng, Dorian Quelle, Scott A. Hale

Oxford Internet Institute, University of Oxford, 1 St Giles’, Oxford, OX1 3JS, United Kingdom · Meedan, San Francisco, 94105, USA · Department of Mathematics, University of Zurich, Zurich, 8057, Switzerland

Social Media Analysis

Open paper AskPDF 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.

Abstract

Semantic Change Detection

Explore similar work

Language Mutations Sustain the Persistences of Conspiracy Theories on Social Media

When Words Divide: Diachronic Ideological Polarization in Political Discourse on Social Media

Comment-level Topic Drift Analysis in the Reddit Corpus