# ConspEmoLLM-v2: A robust and stable model to detect sentiment-transformed conspiracy theories

arXiv: https://arxiv.org/abs/2505.14917 · HTML: https://mlsanity.com/paper/2505.14917/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 35 references that are in the map.
- First posted on arXiv 20 May 2025.

## Builds on (most cited first, 16 of 16)

- [Large Language Models for Data Annotation and Synthesis: A Survey](https://mlsanity.com/paper/2402.13446/) (2024)
- [A Survey on LLM-Generated Text Detection: Necessity, Methods, and Future Directions](https://mlsanity.com/paper/2310.14724/) (2023)
- [YuE: Scaling Open Foundation Models for Long-Form Music Generation](https://mlsanity.com/paper/2503.08638/) (2025)
- [Fake News in Sheep's Clothing: Robust Fake News Detection Against LLM-Empowered Style Attacks](https://mlsanity.com/paper/2310.10830/) (2023)
- [EmoLLMs: A Series of Emotional Large Language Models and Annotation Tools for Comprehensive Affective Analysis](https://mlsanity.com/paper/2401.08508/) (2024)
- [On the Creativity of Large Language Models](https://mlsanity.com/paper/2304.00008/) (2023)
- [False Information on Web and Social Media: A Survey](https://mlsanity.com/paper/1804.08559/) (2018)
- [CollabStory: Multi-LLM Collaborative Story Generation and Authorship Analysis](https://mlsanity.com/paper/2406.12665/) (2024)
- [Emotion Detection for Misinformation: A Review](https://mlsanity.com/paper/2311.00671/) (2023)
- [Adaptive Ensembles of Fine-Tuned Transformers for LLM-Generated Text Detection](https://mlsanity.com/paper/2403.13335/) (2024)
- [Investigating Memorization of Conspiracy Theories in Text Generation](https://mlsanity.com/paper/2101.00379/) (2021)
- [RAEmoLLM: Retrieval Augmented LLMs for Cross-Domain Misinformation Detection Using In-Context Learning Based on Emotional Information](https://mlsanity.com/paper/2406.11093/) (2024)
- [Learning to Rewrite: Generalized LLM-Generated Text Detection](https://mlsanity.com/paper/2408.04237/) (2024)
- [Does ChatGPT Have a Poetic Style?](https://mlsanity.com/paper/2410.15299/) (2024)
- [VaxGuard: A Multi-Generator, Multi-Type, and Multi-Role Dataset for Detecting LLM-Generated Vaccine Misinformation](https://mlsanity.com/paper/2503.09103/) (2025)
- [Rumor Detection by Multi-task Suffix Learning based on Time-series Dual Sentiments](https://mlsanity.com/paper/2502.14383/) (2025)

## Built on by (most cited first, 1 of 1)

- [ConspirED: A Dataset for Cognitive Traits of Conspiracy Theories and Large Language Model Safety](https://mlsanity.com/paper/2508.20468/) (2025)

## Abstract (arXiv)

Despite the many benefits of large language models (LLMs), they can also cause harm, e.g., through automatic generation of misinformation, including conspiracy theories. Moreover, LLMs can also ''disguise'' conspiracy theories by altering characteristic textual features, e.g., by transforming their typically strong negative emotions into a more positive tone. Although several studies have proposed automated conspiracy theory detection methods, they are usually trained using human-authored text, whose features can vary from LLM-generated text. Furthermore, several conspiracy detection models, including the previously proposed ConspEmoLLM, rely heavily on the typical emotional features of human-authored conspiracy content. As such, intentionally disguised content may evade detection. To combat such issues, we firstly developed an augmented version of the ConDID conspiracy detection dataset, ConDID-v2, which supplements human-authored conspiracy tweets with versions rewritten by an LLM to reduce the negativity of their original sentiment. The quality of the rewritten tweets was verified by combining human and LLM-based assessment. We subsequently used ConDID-v2 to train ConspEmoLLM-v2, an enhanced version of ConspEmoLLM. Experimental results demonstrate that ConspEmoLLM-v2 retains or exceeds the performance of ConspEmoLLM on the original human-authored content in ConDID, and considerably outperforms both ConspEmoLLM and several other baselines when applied to sentiment-transformed tweets in ConDID-v2. The project will be available at https://github.com/lzw108/ConspEmoLLM.