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.
A participant team may participate in the task with as many participants as the team wants.
A team can submit only 3 different runs as they want. However, the best run will be considered for final ranking.
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.
Participants are NOT allowed to use any internet searches during the execution of the algorithms.
Amjad, Maaz, Grigori Sidorov, Alisa Zhila, Helena Gómez-Adorno, Ilia Voronkov, and Alexander Gelbukh. “Bend the truth”: Benchmark dataset for fake news detection in urdu language and its evaluation." Journal of Intelligent & Fuzzy Systems Preprint, pp. 1-13. (2020).
Amjad, Maaz, Grigori Sidorov, and Alisa Zhila. "Data Augmentation using Machine Translation for Fake News Detection in the Urdu Language." In Proceedings of The 12th Language Resources and Evaluation Conference, pp. 2537-2542. (2020).
Amjad, Maaz, Grigori Sidorov, Alisa Zhila, Alexander Gelbukh, and Paolo Rosso. "Overview of the shared task on fake news detection in Urdu at fire 2020." In CEUR Workshop Proceedings, pp. 434-446. (2020).
Amjad, Maaz, Grigori Sidorov, Alisa Zhila, Alexander Gelbukh, and Paolo Rosso. "UrduFake@ FIRE2020: Shared Track on Fake News Identification in Urdu." In Forum for Information Retrieval Evaluation, pp. 37-40. (2020).
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