# Analysis of Disinformation and Fake News Detection Using Fine-Tuned Large Language Model

arXiv: https://arxiv.org/abs/2309.04704 · HTML: https://mlsanity.com/paper/2309.04704/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 28 references that are in the map.
- First posted on arXiv 9 Sep 2023.

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

- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [Fake News Identification on Twitter with Hybrid CNN and RNN Models](https://mlsanity.com/paper/1806.11316/) (2018)
- [Methods of Informational Trends Analytics and Fake News Detection on Twitter](https://mlsanity.com/paper/2204.04891/) (2022)
- [Financial News Analytics Using Fine-Tuned Llama 2 GPT Model](https://mlsanity.com/paper/2308.13032/) (2023)
- [Forming Predictive Features of Tweets for Decision-Making Support](https://mlsanity.com/paper/2201.02049/) (2022)
- [Can Twitter Predict Royal Baby's Name ?](https://mlsanity.com/paper/1310.3500/) (2013)

## Built on by (most cited first, 10 of 10)

- [Emotion Detection for Misinformation: A Review](https://mlsanity.com/paper/2311.00671/) (2023)
- [User Modeling in the Era of Large Language Models: Current Research and Future Directions](https://mlsanity.com/paper/2312.11518/) (2023)
- [ConspEmoLLM: Conspiracy Theory Detection Using an Emotion-Based Large Language Model](https://mlsanity.com/paper/2403.06765/) (2024)
- [AI Approaches to Qualitative and Quantitative News Analytics on NATO Unity](https://mlsanity.com/paper/2505.06313/) (2025)
- [Multilevel Analysis of Cryptocurrency News using RAG Approach with Fine-Tuned Mistral Large Language Model](https://mlsanity.com/paper/2509.03527/) (2025)
- [Leveraging Parameter Efficient Training Methods for Low Resource Text Classification: A Case Study in Marathi](https://mlsanity.com/paper/2408.03172/) (2024)
- [Using GPT Models for Qualitative and Quantitative News Analytics in the 2024 US Presidental Election Process](https://mlsanity.com/paper/2410.15884/) (2024)
- [Latent Fact-Checking: Detecting Misinformation through Activation Engineering](https://mlsanity.com/paper/2608.06417/) (2026)
- [FakeSpotter: A content and strategy agnostic Viral Misinformation Detection Tool](https://mlsanity.com/paper/2609.19152/) (2026)
- [QLoRA Fine-Tuning of Ministral LLM for Sequence-to-Function Protein Annotation](https://mlsanity.com/paper/2609.24538/) (2026)

## Abstract (arXiv)

The paper considers the possibility of fine-tuning Llama 2 large language model (LLM) for the disinformation analysis and fake news detection. For fine-tuning, the PEFT/LoRA based approach was used. In the study, the model was fine-tuned for the following tasks: analysing a text on revealing disinformation and propaganda narratives, fact checking, fake news detection, manipulation analytics, extracting named entities with their sentiments. The obtained results show that the fine-tuned Llama 2 model can perform a deep analysis of texts and reveal complex styles and narratives. Extracted sentiments for named entities can be considered as predictive features in supervised machine learning models.