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AuthorHaouari, Fatima
AuthorAli, Zien Sheikh
AuthorElsayed, Tamer
Available date2020-07-09T21:13:32Z
Publication Date2019
Publication NameCEUR Workshop Proceedings
AbstractWith the proliferation of fake news and its prevalent impact on democracy, journalism, and public opinions, manual fact-checkers become unscalable to the volume and speed of fake news propagation. Automatic fact-checkers are therefore needed to prevent the negative impact of fake news in a fast and effective way. In this paper, we present our participation in Task 2 of CLEF-2019 CheckThat! Lab, which addresses the problem of finding evidence over the Web for verifying Arabic claims. We participated in all of the four subtasks and adopted a machine learning approach in each with different set of features that are extracted from both the claim and the corresponding retrieved Web search result pages. Our models, trained solely over the provided training data, for the different subtasks exhibited relatively-good performance. Our official results, on the testing data, show that our best performing runs achieved the best overall performance in subtasks A and B among 7 and 8 participating runs respectively. As for subtasks C and D, our best performing runs achieved the median overall performance among 6 and 9 participating runs respectively.
SubjectArabic Retrieval
SubjectFact Checking
SubjectLearning to Rank
SubjectWeb Classification
TitleBIGIR at CLEF 2019: Automatic verification of Arabic claims over the web
TypeConference Paper
Volume Number2380

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