Detecting Abusive Language on Online Platforms: A Critical Analysis

Arora, Arnav; Nakov, Preslav; Hardalov, Momchil; Sarwar, Sheikh Muhammad; Nayak, Vibha; Dinkov, Yoan; Zlatkova, Dimitrina; Dent, Kyle; Ameya, Bhatawdekar,; Bouchard, Guillaume; Augenstein, Isabelle

doi:10.48550/arxiv.2103.00153

Cited by 2 publications

(2 citation statements)

References 6 publications

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“…There has been a large amount of research in recent years to address and detect the growing use of "offensive languages" and hate speech in different social platforms (see Nakov et al (2021) and Salminen et al (2020) for more details). Detecting offensive language has been the focus of many shared tasks such as OffensEval 2020 (Zampieri et al 2020) for five languages and OSACT 2020 (Mubarak et al 2020b) for Arabic.…”

Section: Related Workmentioning

confidence: 99%

Emojis as anchors to detect Arabic offensive language and hate speech

Mubarak,

Hassan,

Chowdhury

2023

Nat. Lang. Eng.

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We introduce a generic, language-independent method to collect a large percentage of offensive and hate tweets regardless of their topics or genres. We harness the extralinguistic information embedded in the emojis to collect a large number of offensive tweets. We apply the proposed method on Arabic tweets and compare it with English tweets—analyzing key cultural differences. We observed a constant usage of these emojis to represent offensiveness throughout different timespans on Twitter. We manually annotate and publicly release the largest Arabic dataset for offensive, fine-grained hate speech, vulgar, and violence content. Furthermore, we benchmark the dataset for detecting offensiveness and hate speech using different transformer architectures and perform in-depth linguistic analysis. We evaluate our models on external datasets—a Twitter dataset collected using a completely different method, and a multi-platform dataset containing comments from Twitter, YouTube, and Facebook, for assessing generalization capability. Competitive results on these datasets suggest that the data collected using our method capture universal characteristics of offensive language. Our findings also highlight the common words used in offensive communications, common targets for hate speech, specific patterns in violence tweets, and pinpoint common classification errors that can be attributed to limitations of NLP models. We observe that even state-of-the-art transformer models may fail to take into account culture, background, and context or understand nuances present in real-world data such as sarcasm.

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Section: Related Workmentioning

confidence: 99%

Emojis as anchors to detect Arabic offensive language and hate speech

Mubarak,

Hassan,

Chowdhury

2023

Nat. Lang. Eng.

View full text Add to dashboard Cite

show abstract

“…There has been a large amount of research in recent years to address and detect the growing use of 'offensive languages' and hate speech in different social platforms (see Nakov et al (2021) and Salminen et al (2020) for more details.). Detecting offensive language has been the focus of many shared tasks such as OffensEval 2020 (Zampieri et al (2020)) for five languages and OSACT 2020 (Mubarak et al (2020b)) for Arabic.…”

Section: Related Workmentioning

confidence: 99%

Emojis as Anchors to Detect Arabic Offensive Language and Hate Speech

Mubarak¹,

Hassan²,

Chowdhury³

2022

Preprint

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We introduce a generic, language-independent method to collect a large percentage of offensive and hate tweets regardless of their topics or genres. We harness the extralinguistic information embedded in the emojis to collect a large number of offensive tweets. We apply the proposed method on Arabic tweets and compare it with English tweets -analysing some cultural differences. We observed a constant usage of these emojis to represent offensiveness in throughout different timelines in Twitter. We manually annotate and publicly release the largest Arabic dataset for offensive, fine-grained hate speech, vulgar and violence content. Furthermore, we benchmark the dataset for detecting offense and hate speech using different transformer architectures and performed in-depth linguistic analysis. We evaluate our models on external datasets -a Twitter dataset collected using a completely different method, and a multi-platform dataset containing comments from Twitter, YouTube and Facebook, for assessing generalization capability. Competitive results on these datasets suggest that the data collected using our method captures universal characteristics of offensive language. Our findings also highlight the common words used in offensive communications; common targets for hate speech; specific patterns in violence tweets and pinpoints common classification errors due to the need to understand the context, consider culture and background and the presence of sarcasm among others.

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Detecting Abusive Language on Online Platforms: A Critical Analysis

Cited by 2 publications

References 6 publications

Emojis as anchors to detect Arabic offensive language and hate speech

Emojis as anchors to detect Arabic offensive language and hate speech

Emojis as Anchors to Detect Arabic Offensive Language and Hate Speech

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