TruthLens: Graph Analytics for Fake News Detection

Abstract

Fake news has evolved from a minor online problem to a potent instrument for influencing public opinion and undermining confidence in digital platforms. Conventional detection systems function similarly to linguistic spell checkers, but they frequently overlook the intricate, well-planned nature of disinformation campaigns. In order to close this gap, TruthLens proposes a hybrid, multimodal framework that integrates graph analytics, computer vision, and natural language processing (NLP). Our approach makes use of PyTorch Geometric (GNNs) to monitor the propagation of information in social networks, BERT for comprehensive semantic text analysis, and CLIP for consistency between text and images. TruthLens identifies structural anomalies that indicate bot networks and organized influence operations by examining not only what is said but also how and by whom it spreads. According to preliminary tests, this layered approach performs significantly better than conventional text-only models, providing both greater accuracy and lucid insights into the mechanisms of digital deception.