UrduFake@FIRE2021: Shared Track on Fake News Identification in Urdu

TL;DR

This paper presents a shared task on fake news detection in Urdu, involving multiple teams and approaches, with the best system achieving an F-score of 0.679, highlighting progress in Urdu fake news identification.

Contribution

It introduces the second shared task on Urdu fake news detection, providing a benchmark dataset and evaluating diverse machine learning approaches for the task.

Findings

SGD classifier achieved the highest F-score of 0.679

Multiple teams participated with various features and models

Neural network architectures were explored for fake news detection

Abstract

This study reports the second shared task named as UrduFake@FIRE2021 on identifying fake news detection in Urdu language. This is a binary classification problem in which the task is to classify a given news article into two classes: (i) real news, or (ii) fake news. In this shared task, 34 teams from 7 different countries (China, Egypt, Israel, India, Mexico, Pakistan, and UAE) registered to participate in the shared task, 18 teams submitted their experimental results and 11 teams submitted their technical reports. The proposed systems were based on various count-based features and used different classifiers as well as neural network architectures. The stochastic gradient descent (SGD) algorithm outperformed other classifiers and achieved 0.679 F-score.