Improved Micro-Blog Classification for Detecting Abusive Arabic Twitter Accounts

Abozinadah, Ehab; Jones, James H.

doi:10.5121/ijdkp.2016.6602

Cited by 27 publications

(12 citation statements)

References 18 publications

(19 reference statements)

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“…They created their own test set and made it publicly available. Then in [58] Abozinadah and H. Jones, Jr. enhanced the previous work by proposing a lexicon that is fed by an Arabic word correction method to enhance the detection of such abusive words. A third work by Abozinadah is [59] which used statistical learning approach for the detection process to overcome the limitation in the BOW approach presented in other previous works.…”

Section: Arabic Anti-social Behaviour Detectionmentioning

confidence: 99%

Detection of Hate Speech in Social Networks: A Survey on Multilingual Corpus

Al-Dossari¹

2019

Computer Science &Amp; Information Technology(CS &Amp; IT)

115

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In social media platforms, hate speech can be a reason of "cyber conflict" which can affect social life in both of individual-level and country-level. Hateful and antagonistic content propagated via social networks has the potential to cause harm and suffering on an individual basis and lead to social tension and disorder beyond cyber space. However, social networks cannot control all the content that users post. For this reason, there is a demand for automatic detection of hate speech. This demand particularly raises when the content is written in complex languages (e.g. Arabic). Arabic text is known with its challenges, complexity and scarcity of its resources. This paper will present a background on hate speech and its related detection approaches. In addition, the recent contributions on hate speech and its related anti-social behaviour topics will be reviewed. Finally, challenges and recommendations for the Arabic hate speech detection problem will be presented.

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Section: Arabic Anti-social Behaviour Detectionmentioning

confidence: 99%

Detection of Hate Speech in Social Networks: A Survey on Multilingual Corpus

Al-Dossari¹

2019

Computer Science &Amp; Information Technology(CS &Amp; IT)

115

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“…Sometimes, people knowingly misspell a censor word by adding additional character or deleting one or more character to avoid flame detection. Such kind of behaviour has been reported from Arabic-language tweets [16,17]. Eventually such forms of communication degrade the sense of camaraderie among friends, business people and others.…”

Section: Flaming In Chatting Applications and Flame Detector Toolmentioning

confidence: 79%

Development of Security Detection Model for the Security of Social Blogs and Chatting from Hostile Users

Gupta¹,

nitin²

2017

IJCSIT

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“…The detection of offensive language that includes personal attacks, demeaning comments, or hateful language is left for future work. Unlike previous work on obscenity and offensive language detection for different languages, such as English (Mahmud et al, 2008;Spertus, 2007;Xiang et al, 2012) and German (Ross et al, 2016), very limited previous work for this task was done for Arabic (Abozinadah et al, 2016).…”

Section: Introductionmentioning

confidence: 98%

Abusive Language Detection on Arabic Social Media

Mubarak¹,

Darwish²,

Magdy³

2017

Proceedings of the First Workshop on Abusive Language Online

224

147

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In this paper, we present our work on detecting abusive language on Arabic social media. We extract a list of obscene words and hashtags using common patterns used in offensive and rude communications. We also classify Twitter users according to whether they use any of these words or not in their tweets. We expand the list of obscene words using this classification, and we report results on a newly created dataset of classified Arabic tweets (obscene, offensive, and clean). We make this dataset freely available for research, in addition to the list of obscene words and hashtags. We are also publicly releasing a large corpus of classified user comments that were deleted from a popular Arabic news site due to violations the site's rules and guidelines.

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Improved Micro-Blog Classification for Detecting Abusive Arabic Twitter Accounts

Abstract: ABSTRACT

Cited by 27 publications

References 18 publications

Detection of Hate Speech in Social Networks: A Survey on Multilingual Corpus

Detection of Hate Speech in Social Networks: A Survey on Multilingual Corpus

Development of Security Detection Model for the Security of Social Blogs and Chatting from Hostile Users

Abusive Language Detection on Arabic Social Media

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