Detection of Hate Speech in COVID-19-Related Tweets in the Arab Region: Deep Learning and Topic Modeling Approach

Journal of Medical Internet Research
Raghad AlshalanShahad Alshalan

Abstract

The massive scale of social media platforms requires an automatic solution for detecting hate speech. These automatic solutions will help reduce the need for manual analysis of content. Most previous literature has cast the hate speech detection problem as a supervised text classification task using classical machine learning methods or, more recently, deep learning methods. However, work investigating this problem in Arabic cyberspace is still limited compared to the published work on English text. This study aims to identify hate speech related to the COVID-19 pandemic posted by Twitter users in the Arab region and to discover the main issues discussed in tweets containing hate speech. We used the ArCOV-19 dataset, an ongoing collection of Arabic tweets related to COVID-19, starting from January 27, 2020. Tweets were analyzed for hate speech using a pretrained convolutional neural network (CNN) model; each tweet was given a score between 0 and 1, with 1 being the most hateful text. We also used nonnegative matrix factorization to discover the main issues and topics discussed in hate tweets. The analysis of hate speech in Twitter data in the Arab region identified that the number of non-hate tweets greatly exceeded the number ...Continue Reading

References

May 26, 2016·Journal of Biomedical Informatics·Mohammed Ali Al-GaradiAbdelkodose M Al-Kabsi
Apr 10, 2018·American Journal of Infection Control·Lu TangDegui Zhi
Apr 15, 2020·Journal of Medical Internet Research·Alaa Abd-AlrazaqZubair Shah
Apr 25, 2020·Journal of Medical Internet Research·Ali FarooqA K M Najmul Islam
May 1, 2020·Journal of Medical Internet Research·Wasim AhmedFrancesc López Seguí

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Citations

Nov 4, 2021··Zaher Al AghbariTarek Elsaka

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Software Mentioned

Twitter search
Farasa
Twarc
GeoNames
sklearn

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