Fake News Detection in Social Media using Graph Neural Networks and NLP Techniques: A COVID-19 Use-case
This work addresses the problem of misinformation detection on social media, which is a critical issue for public health and societal trust, particularly during events like the COVID-19 pandemic. The solutions are incremental, building on existing NLP and GNN techniques.
This paper tackles the problem of detecting fake news related to COVID-19 and 5G conspiracy theories on Twitter. For text-based detection, their BERT-based method achieved an F1-score of 0.566% for ternary classification and 0.693% for binary classification. For structure-based detection, their Graph Neural Network approach achieved an average ROC of 0.95%.
The paper presents our solutions for the MediaEval 2020 task namely FakeNews: Corona Virus and 5G Conspiracy Multimedia Twitter-Data-Based Analysis. The task aims to analyze tweets related to COVID-19 and 5G conspiracy theories to detect misinformation spreaders. The task is composed of two sub-tasks namely (i) text-based, and (ii) structure-based fake news detection. For the first task, we propose six different solutions relying on Bag of Words (BoW) and BERT embedding. Three of the methods aim at binary classification task by differentiating in 5G conspiracy and the rest of the COVID-19 related tweets while the rest of them treat the task as ternary classification problem. In the ternary classification task, our BoW and BERT based methods obtained an F1-score of .606% and .566% on the development set, respectively. On the binary classification, the BoW and BERT based solutions obtained an average F1-score of .666% and .693%, respectively. On the other hand, for structure-based fake news detection, we rely on Graph Neural Networks (GNNs) achieving an average ROC of .95% on the development set.