Survey of Fake News Datasets and Detection Methods in European and Asian Languages

The presence of fake news and “alternative facts” across the web is a global phenomenon that received considerable attention in recent years. Several researchers have made substantial efforts to automatically identify fake news articles based on linguistic features and neural network-based methods. However, automatic classification via machine and deep learning techniques demands a significant amount of annotated data. While several state-of-the-art datasets for the English language are available and commonly utilized for research, fake news detection in low-resource languages gained less attention. This study surveys the publicly available datasets of fake news in low/medium-resourced Asian and European languages. We also highlight the vacuum of datasets and methods in these languages. Moreover, we summarize the proposed methods and the metrics used to evaluate the classifiers in identifying fake news. This study is helpful for analysis of the available sources in the lower resource languages to solve fake news detection challenges.