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.

本文利用几何深度学习模型对推特上的新闻内容、用户画像、行为，社交网络结构与传播进行深度融合，基于传播模式的方法可以高效地提取特征，提高了对于虚假新闻的识别率，在短时间内便可可靠地便可侦测出，同时与现存的基于内容分析的方法有互补性和多个优势。