Fake News Detection on Social Media using Geometric Deep Learning

Abstract

Social media are nowadays one of the main news sources for millions of people around the globe due to their low cost, easy access and rapid dissemination. This however comes at the cost of dubious trustworthiness and significant risk of exposure to 'fake news', intentionally written to mislead the readers. Automatically detecting fake news poses challenges that defy existing content-based analysis approaches. One of the main reasons is that often the interpretation of the news requires the knowledge of political or social context or 'common sense', which current NLP algorithms are still missing. Recent studies have shown that fake and real news spread differently on social media, forming propagation patterns that could be harnessed for the automatic fake news detection. Propagation-based approaches have multiple advantages compared to their content-based counterparts, among which is language independence and better resilience to adversarial attacks. In this paper we show a novel automatic fake news detection model based on geometric deep learning. The underlying core algorithms are a generalization of classical CNNs to graphs, allowing the fusion of heterogeneous data such as content, user profile and activity, social graph, and news propagation. Our model was trained and tested on news stories, verified by professional fact-checking organizations, that were spread on Twitter. Our experiments indicate that social network structure and propagation are important features allowing highly accurate (92.7% ROC AUC) fake news detection. Second, we observe that fake news can be reliably detected at an early stage, after just a few hours of propagation. Third, we test the aging of our model on training and testing data separated in time. Our results point to the promise of propagation-based approaches for fake news detection as an alternative or complementary strategy to content-based approaches.

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Cited by in corpus (37)

• Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges
• A Generalization of Transformer Networks to Graphs
• A Survey of Convolutional Neural Networks: Analysis, Applications, and Prospects
• FANG: Leveraging Social Context for Fake News Detection Using Graph Representation
• Benchmarking Graph Neural Networks
• SIGN: Scalable Inception Graph Neural Networks
• Temporal Graph Networks for Deep Learning on Dynamic Graphs
• Topology comparison of Twitter diffusion networks effectively reveals misleading information
• Graph Neural Networks with Continual Learning for Fake News Detection from Social Media
• On Representation Knowledge Distillation for Graph Neural Networks
• Graph Convolutional Neural Networks with Node Transition Probability-based Message Passing and DropNode Regularization
• Profiling Fake News Spreaders on Social Media through Psychological and Motivational Factors
• Misinformation Has High Perplexity
• Graph Neural Networks with Learnable Structural and Positional Representations
• Scalable and Adaptive Graph Neural Networks with Self-Label-Enhanced training
• Fake News Early Detection: An Interdisciplinary Study
• Addressing contingency in algorithmic (mis)information classification: Toward a responsible machine learning agenda
• Efficient Robustness Certificates for Discrete Data: Sparsity-Aware Randomized Smoothing for Graphs, Images and More
• Explainable Rumor Detection using Inter and Intra-feature Attention Networks
• Utilising Graph Machine Learning within Drug Discovery and Development
• FakeSwarm: Improving Fake News Detection with Swarming Characteristics
• Knowledge Enhanced Multi-modal Fake News Detection
• A multi-layer approach to disinformation detection on Twitter
• Combating Disinformation in a Social Media Age
• Meet The Truth: Leverage Objective Facts and Subjective Views for Interpretable Rumor Detection
• A Framework for Joint Unsupervised Learning of Cluster-Aware Embedding for Heterogeneous Networks
• Learning to Detect Few-Shot-Few-Clue Misinformation
• Misinformation detection in Luganda-English code-mixed social media text
• User Preference-aware Fake News Detection
• GeoGraph: Learning graph-based multi-view object detection with geometric cues end-to-end
• Neural Trees for Learning on Graphs
• Combining exogenous and endogenous signals with a semi-supervised co-attention network for early detection of COVID-19 fake tweets
• An Experimental Study of the Transferability of Spectral Graph Networks
• AN-GCN: An Anonymous Graph Convolutional Network Defense Against Edge-Perturbing Attack
• Fake News Data Collection and Classification: Iterative Query Selection for Opaque Search Engines with Pseudo Relevance Feedback
• Assessing Centrality Without Knowing Connections
• A Model-Based Derivative-Free Approach to Black-Box Adversarial Examples: BOBYQA