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

• URL: http://arxiv.org/abs/2207.11893v1
• Date: Mon, 25 Jul 2022 03:41:32 GMT
• Title: Overview of the Shared Task on Fake News Detection in Urdu at FIRE 2020
• Authors: Maaz Amjad, Grigori Sidorov, Alisa Zhila, Alexander Gelbukh and Paolo Rosso
• Abstract summary: Task was posed as a binary classification task, in which the goal is to differentiate between real and fake news. We provided a dataset divided into 900 annotated news articles for training and 400 news articles for testing. 42 teams from 6 different countries (India, China, Egypt, Germany, Pakistan, and the UK) registered for the task.
• Score: 62.6928395368204
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This overview paper describes the first shared task on fake news detection in Urdu language. The task was posed as a binary classification task, in which the goal is to differentiate between real and fake news. We provided a dataset divided into 900 annotated news articles for training and 400 news articles for testing. The dataset contained news in five domains: (i) Health, (ii) Sports, (iii) Showbiz, (iv) Technology, and (v) Business. 42 teams from 6 different countries (India, China, Egypt, Germany, Pakistan, and the UK) registered for the task. 9 teams submitted their experimental results. The participants used various machine learning methods ranging from feature-based traditional machine learning to neural networks techniques. The best performing system achieved an F-score value of 0.90, showing that the BERT-based approach outperforms other machine learning techniques

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