# Classifying Conspiratorial Narratives At Scale: False Alarms and Erroneous Connections

arXiv: https://arxiv.org/abs/2404.00141 · HTML: https://mlsanity.com/paper/2404.00141/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 78 references that are in the map.
- First posted on arXiv 29 Mar 2024.

## Builds on (most cited first, 17 of 17)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Large Language Models are Zero-Shot Reasoners](https://mlsanity.com/paper/2205.11916/) (2022)
- [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://mlsanity.com/paper/1910.01108/) (2019)
- [ALBERT: A Lite BERT for Self-supervised Learning of Language Representations](https://mlsanity.com/paper/1909.11942/) (2019)
- [DeBERTa: Decoding-enhanced BERT with Disentangled Attention](https://mlsanity.com/paper/2006.03654/) (2020)
- [Calibrate Before Use: Improving Few-Shot Performance of Language Models](https://mlsanity.com/paper/2102.09690/) (2021)
- [What Makes Good In-Context Examples for GPT-3?](https://mlsanity.com/paper/2101.06804/) (2021)
- [Prompt Programming for Large Language Models: Beyond the Few-Shot Paradigm](https://mlsanity.com/paper/2102.07350/) (2021)
- [Can ChatGPT Understand Too? A Comparative Study on ChatGPT and Fine-tuned BERT](https://mlsanity.com/paper/2302.10198/) (2023)
- [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)
- [Science vs Conspiracy: collective narratives in the age of (mis)information](https://mlsanity.com/paper/1408.1667/) (2014)
- [Debunking in a World of Tribes](https://mlsanity.com/paper/1510.04267/) (2015)
- [Characterizing Social Imaginaries and Self-Disclosures of Dissonance in Online Conspiracy Discussion Communities](https://mlsanity.com/paper/2107.10204/) (2021)
- [Characterizing Reddit Participation of Users Who Engage in the QAnon Conspiracy Theories](https://mlsanity.com/paper/2203.07433/) (2022)

## Built on by (most cited first, 5 of 5)

- [Assessing the Impact of Conspiracy Theories Using Large Language Models](https://mlsanity.com/paper/2412.07019/) (2024)
- [GenAI vs. Human Fact-Checkers: Accurate Ratings, Flawed Rationales](https://mlsanity.com/paper/2502.14943/) (2025)
- [Belief Is All You Need: Modeling Narrative Archetypes in Conspiratorial Discourse](https://mlsanity.com/paper/2512.10105/) (2025)
- [AILS-NTUA at SemEval-2026 Task 10: Agentic LLMs for Psycholinguistic Marker Extraction and Conspiracy Endorsement Detection](https://mlsanity.com/paper/2603.04921/) (2026)
- [Agentic Detection of Online Conspiracies](https://mlsanity.com/paper/2609.30250/) (2026)

## Abstract (arXiv)

Online discussions frequently involve conspiracy theories, which can contribute to the proliferation of belief in them. However, not all discussions surrounding conspiracy theories promote them, as some are intended to debunk them. Existing research has relied on simple proxies or focused on a constrained set of signals to identify conspiracy theories, which limits our understanding of conspiratorial discussions across different topics and online communities. This work establishes a general scheme for classifying discussions related to conspiracy theories based on authors' perspectives on the conspiracy belief, which can be expressed explicitly through narrative elements, such as the agent, action, or objective, or implicitly through references to known theories, such as chemtrails or the New World Order. We leverage human-labeled ground truth to train a BERT-based model for classifying online CTs, which we then compared to the Generative Pre-trained Transformer machine (GPT) for detecting online conspiratorial content. Despite GPT's known strengths in its expressiveness and contextual understanding, our study revealed significant flaws in its logical reasoning, while also demonstrating comparable strengths from our classifiers. We present the first large-scale classification study using posts from the most active conspiracy-related Reddit forums and find that only one-third of the posts are classified as positive. This research sheds light on the potential applications of large language models in tasks demanding nuanced contextual comprehension.