TIB's Visual Analytics Group at MediaEval '20: Detecting Fake News on Corona Virus and 5G Conspiracy
This research is significant for social media platforms and the public to combat the spread of misinformation, particularly concerning critical public health issues like pandemics. It represents an incremental step in text-based fake news detection.
This paper addresses the problem of detecting fake news on social media, specifically focusing on COVID-19 and 5G conspiracy theories. The authors developed a text-based classification method using BERT embeddings and a shallow neural network to identify misinformation in tweets.
Fake news on social media has become a hot topic of research as it negatively impacts the discourse of real news in the public. Specifically, the ongoing COVID-19 pandemic has seen a rise of inaccurate and misleading information due to the surrounding controversies and unknown details at the beginning of the pandemic. The FakeNews task at MediaEval 2020 tackles this problem by creating a challenge to automatically detect tweets containing misinformation based on text and structure from Twitter follower network. In this paper, we present a simple approach that uses BERT embeddings and a shallow neural network for classifying tweets using only text, and discuss our findings and limitations of the approach in text-based misinformation detection.