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Social media has greatly enabled people to participate in online activitiesat an unprecedented rate. However, this unrestricted access also exacerbatesthe spread of misinformation and fake news online which might cause confusionand chaos unless being detected early for its mitigation. Given the rapidlyevolving nature of news events and the limited amount of annotated data,state-of-the-art systems on fake news detection face challenges due to the lackof large numbers of annotated training instances that are hard to come by forearly detection. In this work, we exploit multiple weak signals from differentsources given by user and content engagements (referred to as weak socialsupervision), and their complementary utilities to detect fake news. We jointlyleverage the limited amount of clean data along with weak signals from socialengagements to train deep neural networks in a meta-learning framework toestimate the quality of different weak instances. Experiments on realworlddatasets demonstrate that the proposed framework outperforms state-of-the-artbaselines for early detection of fake news without using any user engagementsat prediction time.

Leveraging Multi-Source Weak Social Supervision for Early Detection of Fake News