A Self-Learning Multimodal Approach for Fake News Detection

TL;DR

This paper presents a self-learning multimodal model that combines contrastive learning and large language models to effectively detect fake news using text and images, achieving over 85% accuracy.

Contribution

The paper introduces a novel self-learning multimodal fake news detection approach that leverages contrastive learning and LLMs without requiring labeled data.

Findings

Achieves over 85% accuracy, precision, recall, and F1-score.

Outperforms several state-of-the-art classification methods.

Effectively detects fake news using combined text and image analysis.

Abstract

The rapid growth of social media has resulted in an explosion of online news content, leading to a significant increase in the spread of misleading or false information. While machine learning techniques have been widely applied to detect fake news, the scarcity of labeled datasets remains a critical challenge. Misinformation frequently appears as paired text and images, where a news article or headline is accompanied by a related visuals. In this paper, we introduce a self-learning multimodal model for fake news classification. The model leverages contrastive learning, a robust method for feature extraction that operates without requiring labeled data, and integrates the strengths of Large Language Models (LLMs) to jointly analyze both text and image features. LLMs are excel at this task due to their ability to process diverse linguistic data drawn from extensive training corpora. Our experimental results on a public dataset demonstrate that the proposed model outperforms several state-of-the-art classification approaches, achieving over 85% accuracy, precision, recall, and F1-score. These findings highlight the model's effectiveness in tackling the challenges of multimodal fake news detection.