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This overview paper describes the first shared task on fake news detection inUrdu language. The task was posed as a binary classification task, in which thegoal is to differentiate between real and fake news. We provided a datasetdivided into 900 annotated news articles for training and 400 news articles fortesting. The dataset contained news in five domains: (i) Health, (ii) Sports,(iii) Showbiz, (iv) Technology, and (v) Business. 42 teams from 6 differentcountries (India, China, Egypt, Germany, Pakistan, and the UK) registered forthe task. 9 teams submitted their experimental results. The participants usedvarious machine learning methods ranging from feature-based traditional machinelearning to neural networks techniques. The best performing system achieved anF-score value of 0.90, showing that the BERT-based approach outperforms othermachine learning techniques

Overview of the Shared Task on Fake News Detection in Urdu at FIRE 2020