TIB's Visual Analytics Group at MediaEval '20: Detecting Fake News on Corona Virus and 5G Conspiracy

TL;DR

This paper presents a simple BERT-based neural network approach for detecting COVID-19 and 5G conspiracy fake news on Twitter, highlighting its effectiveness and limitations in text-based misinformation detection.

Contribution

The paper introduces a straightforward BERT embedding and shallow neural network method for classifying misinformation tweets, focusing on COVID-19 and 5G conspiracy theories.

Findings

Effective in classifying misinformation using only text.

Identifies limitations of text-only approach.

Provides insights into fake news detection challenges.

Abstract

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.