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Beyond Hostile Linguistic Cues: The Gravity of Online Milieu for Hate Speech Detection in Arabic

Published: 12 September 2019 Publication History

Abstract

Religious Hate speech poses grave dangers for the cohesion of a democratic society, the protection of human rights and the rule of law. While previous work has shown that linguistic features can be effectively used for text categorization in Arabic, employing information coming from users` social networks has not yet been explored for such complex user characteristics. Systems relying on language information tend to have low precision because they tend to rely on messages containing particular terms indicating hate speech. In this paper, we study the novel problem of exploiting social context for detection of religious hate speech in Arabic tweets, given information extracted from their online milieu by learning a low-dimensional vector representation of users.

References

[1]
lbadi, Nuha, Maram Kurdi, and Shivakant Mishra. "Are they Our Brothers? Analysis and Detection of Religious Hate Speech in the Arabic Twittersphere." 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). IEEE, 2018.
[2]
rover, Aditya, and Jure Leskovec. "node2vec: Scalable feature learning for networks." Proceedings of the 22nd ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 2016.
[3]
bu Bakr Soliman, Kareem Eisa, and Samhaa R. El-Beltagy, ?AraVec: A set of Arabic Word Embedding Models for use in Arabic NLP", in proceedings of the 3rd International Conference on Arabic Computational Linguistics (ACLing 2017), Dubai, UAE, 2017.
[4]
shra, Pushkar, et al. "Author profiling for abuse detection." Proceedings of the 27th International Conference on Computational Linguistics. 2018.

Cited By

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  • (2022)A review on abusive content automatic detection: approaches, challenges and opportunitiesPeerJ Computer Science10.7717/peerj-cs.11428(e1142)Online publication date: 9-Nov-2022
  • (2020)Arten-Net: An Emotion Classification System for Art (Student Consortium)2020 IEEE Sixth International Conference on Multimedia Big Data (BigMM)10.1109/BigMM50055.2020.00053(302-306)Online publication date: Sep-2020
  • (2020)Trajectory Similarity Assessment On Road Networks Via Embedding Learning2020 IEEE Sixth International Conference on Multimedia Big Data (BigMM)10.1109/BigMM50055.2020.00012(1-8)Online publication date: Sep-2020

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Published In

cover image ACM Conferences
HT '19: Proceedings of the 30th ACM Conference on Hypertext and Social Media
September 2019
326 pages
ISBN:9781450368858
DOI:10.1145/3342220
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 12 September 2019

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Author Tags

  1. arabic-nlp
  2. hate speech
  3. social media
  4. social networks

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HT '19 Paper Acceptance Rate 20 of 68 submissions, 29%;
Overall Acceptance Rate 378 of 1,158 submissions, 33%

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Cited By

View all
  • (2022)A review on abusive content automatic detection: approaches, challenges and opportunitiesPeerJ Computer Science10.7717/peerj-cs.11428(e1142)Online publication date: 9-Nov-2022
  • (2020)Arten-Net: An Emotion Classification System for Art (Student Consortium)2020 IEEE Sixth International Conference on Multimedia Big Data (BigMM)10.1109/BigMM50055.2020.00053(302-306)Online publication date: Sep-2020
  • (2020)Trajectory Similarity Assessment On Road Networks Via Embedding Learning2020 IEEE Sixth International Conference on Multimedia Big Data (BigMM)10.1109/BigMM50055.2020.00012(1-8)Online publication date: Sep-2020

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