UrduFake@FIRE2020: Shared Track on Fake News Identification in Urdu

• URL: http://arxiv.org/abs/2207.12406v1
• Date: Mon, 25 Jul 2022 03:46:51 GMT
• Title: UrduFake@FIRE2020: Shared Track on Fake News Identification in Urdu
• Authors: Maaz Amjad, Grigori Sidorov, Alisa Zhila, Alexander Gelbukh and Paolo Rosso
• Abstract summary: This paper gives the overview of the first shared task at FIRE 2020 on fake news detection in the Urdu language. The goal is to identify fake news using a dataset composed of 900 annotated news articles for training and 400 news articles for testing. The dataset contains news in five domains: (i) Health, (ii) Sports, (iii) Showbiz, (iv) Technology, and (v) Business.
• Score: 62.6928395368204
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper gives the overview of the first shared task at FIRE 2020 on fake news detection in the Urdu language. This is a binary classification task in which the goal is to identify fake news using a dataset composed of 900 annotated news articles for training and 400 news articles for testing. The dataset contains 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 network techniques. The best performing system achieved an F-score value of 0.90, showing that the BERT-based approach outperforms other machine learning classifiers.

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