Analysis of Disinformation and Fake News Detection Using Fine-Tuned
  Large Language Model
Bohdan M. Pavlyshenko
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.