Context-Based Fake News Detection using Graph Based Approach: ACOVID-19 Use-case

TL;DR

This paper introduces a novel graph-based method utilizing NLP and anomaly detection to identify fake news, specifically applied to COVID-19 related articles, demonstrating effectiveness in distinguishing genuine from fake news.

Contribution

It presents a new contextual graph-based approach combining NLP and MDL-based anomaly detection for fake news identification, tailored to COVID-19 news articles.

Findings

Effective detection of fake COVID-19 news articles

Enhanced dataset with real and fake news for better testing

Graph-based approach uncovers complex patterns in news data

Abstract

In today\'s digital world, fake news is spreading with immense speed. Its a significant concern to address. In this work, we addressed that challenge using novel graph based approach. We took dataset from Kaggle that contains real and fake news articles. To test our approach we incorporated recent covid-19 related news articles that contains both genuine and fake news that are relevant to this problem. This further enhances the dataset as well instead of relying completely on the original dataset. We propose a contextual graph-based approach to detect fake news articles. We need to convert news articles into appropriate schema, so we leverage Natural Language Processing (NLP) techniques to transform news articles into contextual graph structures. We then apply the Minimum Description Length (MDL)-based Graph-Based Anomaly Detection (GBAD) algorithm for graph mining. Graph-based methods are particularly effective for handling rich contextual data, as they enable the discovery of complex patterns that traditional query-based or statistical techniques might overlook. Our proposed approach identifies normative patterns within the dataset and subsequently uncovers anomalous patterns that deviate from these established norms.