16th-20th December 2020, Hyderabad, India.

The 2020 "Fake News Detection in the

Urdu Language" Task

CICLing 2020 UrduFake track @ FIRE 2020

Task Description

The dissemination of Fake news always beat out the truth with significant growth. Fake news and false rumors are spreading further and faster, reaching more people, and penetrating deeper into social networks. We propose the task titled “Fake News Detection in the Urdu Language", which aims at identifying deceiving news articles in the Urdu language spread via digital media. Urdu fake news detection has been investigated (Amjad al.,2020), and we want better results at the level of English language and more methods. The objective of organizing this task is to address the problem of detecting deceiving information in Urdu language using digital media text.


  1. A participant team may participate in the task with as many participants as the team wants.

  2. A team can submit only 3 different runs as they want. However, the best run will be considered for final ranking.

  3. Apart from sending their runs, each team is given also the possibility of submitting a detailed description of their algorithm(s). The format in which runs are to be submitted will be detailed later.

  4. Participants are NOT allowed to use any internet searches during the execution of the algorithms.

Related Work

  1. Maaz Amjad, Grigori Sidorov, Alisa Zhila, Helena Gómez-Adorno, Alexander Gelbukh, Ilia Voronkov, Bend the Truth: A Benchmark Dataset for Fake News Detection in Urdu and Its Evaluation, Journal of Intelligent & Fuzzy Systems (2019).

  1. J.P. Posadas-Durán, Helena Gómez-Adorno, Grigori Sidorov and J. Jaime Moreno Escobar, Detection of Fake News in a New Corpus for the Spanish Language, Journal of Intelligent & Fuzzy Systems (2018).

  2. Pérez-Rosas, B. Kleinberg, A. Lefevre and R. Mihalcea, Automatic Detection of Fake News, in: Proceedings of the 27th International Conference on Computational Linguistics, Association for Computational Linguistics, Santa Fe, New Mexico, USA, 2018, pp. 3391–3401. https://www.aclweb.org/anthology/C18-1287.

  3. Ghanem, B., Rosso, P., and Rangel, F. (2020). An Emotional Analysis of False Information in Social Media and News Articles. ACM Transactions on Internet Technology (TOIT), 20(2), pp. 1-18. https://dl.acm.org/doi/10.1145/3381750

  1. Giachanou A., Rosso P., Crestani F. (2019)- Leveraging Emotional Signals for Credibility Detection. In: Proc. of the 42nd Int. ACM SIGIR Conf. on Research and Development in Information Retrieval (SIGIR ’19), July 21–25, Paris, France