Work

Harnessing Large Language Models and Deep Neural Networks for Fake News Detection

Year: 2025

Type: article

Abstract: The spread of fake news threatens trust in both traditional and digital media. Early detection methods, based on linguistic patterns and handcrafted features, struggle to identify more sophisticated misinformation. Large language models (LLMs) offer promising solutions by capturing complex text patterns, but challenges remain in ensuring their accuracy and generalizability. This study evaluates LLM-based feature extraction for fake news detection across multiple datasets. We compare BERT-based text representations, introduce a method for extracting factual segments from news articles, and create two new datasets with fact-based features. Additionally, we explore graph-based text representations using LLMs to capture relationships within news content. By integrating these approaches, we improve fake news detection, making it more accurate and interpretable. Our findings provide insights into how LLMs and graph-based techniques can enhance misinformation detection.

Source: Information

DOI: 10.3390/info16040297

Authors: Eleftheria Papageorgiou, Iraklis Varlamis, Christos Chronis

Institution: Harokopio University of Athens

Language: English

Topic: Misinformation and Its Impacts

Subfield: Sociology and Political Science

Field: Social Sciences

Domain: Social Sciences

Open Access status: gold

Estimated APC paid: $1,930

Funders: National and Kapodistrian University of Athens, Harokopio University