Leveraging Multi-Source Weak Social Supervision for Early Detection of Fake News
This work addresses the challenge of limited annotated data for early fake news detection on social media, which is crucial for mitigating misinformation spread, but it is incremental in leveraging weak supervision.
The paper tackles the problem of early fake news detection by exploiting multiple weak signals from user and content engagements, along with limited clean data, in a meta-learning framework. The proposed method outperforms state-of-the-art baselines on real-world datasets without requiring user engagements at prediction time.
Social media has greatly enabled people to participate in online activities at an unprecedented rate. However, this unrestricted access also exacerbates the spread of misinformation and fake news online which might cause confusion and chaos unless being detected early for its mitigation. Given the rapidly evolving 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 lack of large numbers of annotated training instances that are hard to come by for early detection. In this work, we exploit multiple weak signals from different sources given by user and content engagements (referred to as weak social supervision), and their complementary utilities to detect fake news. We jointly leverage the limited amount of clean data along with weak signals from social engagements to train deep neural networks in a meta-learning framework to estimate the quality of different weak instances. Experiments on realworld datasets demonstrate that the proposed framework outperforms state-of-the-art baselines for early detection of fake news without using any user engagements at prediction time.