Fake News Detection on Social Media using Geometric Deep Learning

Federico Monti, Fabrizio Frasca, Davide Eynard, Damon Mannion, Michael M. Bronstein

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 natural language processing algorithms are still missing. Recent studies
have empirically 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 convolutional neural networks 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.