Multilingual Detection of Fake News Spreaders via Sparse Matrix Factorization

Fake news is an emerging problem in online news and social media. Efficient detection of fake news spreaders and spurious accounts across multiple languages is becoming an interesting research problem, and is the key focus of this paper. Our proposed solution to PAN 2020 fake news spreaders challenge models the accounts responsible for spreading the fake news by accounting for different types of textual features, decomposed via sparse matrix factorization, to obtain easy-to-learn-from, compact representations, including the information from multiple languages. The key contribution of this work is the exploration of how powerful and scalable matrix factorization-based classification can be in a multilingual setting, where the learner is presented with the data from multiple languages simultaneously. Finally, we explore the joint latent space, where patterns from individual languages are maintained. The proposed approach scored second on the 2020 PAN shared task for identification of fake news spreaders.