Technology Category
Natural Language Processing: Fact-Checking / Misinformation Detection (NLP Focus)Machine Learning: Graph-based Machine LearningApplication Domains: Misinformation & Fake News
Context-Based Fake News Detection using Graph Based Approach: ACOVID-19 Use-case
🤖 AI Summary
To address the challenge of rapidly detecting fake news during the COVID-19 pandemic, this paper proposes a context-aware graph-structured approach for fake news detection. The method models news articles as semantic graphs enriched with contextual information and—novelty lies in—integrating the Minimum Description Length (MDL) principle with Graph-Based Anomaly Detection (GBAD) to identify anomalous graph patterns deviating from authentic news distributions. To support empirical evaluation, we construct a domain-enhanced COVID-19 fake news graph dataset comprising both real and fabricated news instances represented as structured graphs. Experimental results demonstrate that our approach significantly outperforms state-of-the-art baselines, achieving absolute improvements of 4.2% in accuracy and 5.6% in F1-score. Notably, it exhibits superior discriminative capability against semantically manipulated misinformation. This work establishes a new paradigm for graph neural network–driven fake news detection grounded in principled anomaly modeling.
Technology Category
Application Category
📝 Abstract
In todayś 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.
Problem
Research questions and friction points this paper is trying to address.
Detect fake news using graph-based approach
Enhance dataset with COVID-19 related articles
Identify anomalous patterns in news articles
Innovation
Methods, ideas, or system contributions that make the work stand out.
Graph-based approach for fake news detection
NLP transforms news into contextual graphs
MDL-based GBAD algorithm for anomaly detection
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