Media Effects and Society

Perse, Elizabeth M.; Lambe, Jennifer

doi:10.4324/9780203854693

Cited by 50 publications

(41 citation statements)

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“…In the era of ever-growing amounts of user-generated online content it is becoming increasingly difficult to scale up moderation efforts (Nobata et al, 2016). However, the need for moderation is rapidly increasing due to escalated toxic behavior online, enabled by "hiding" behind anonymous profiles, lack of physical contact between participants (i.e., the communication is then typically perceived as less personal), and lack of direct negative societal consequences (Perse and Lambe, 2016). Consequently, research on automated methods for detecting abusive language in user-generated content is becoming increasingly important.…”

Section: Introductionmentioning

confidence: 99%

XHate-999: Analyzing and Detecting Abusive Language Across Domains and Languages

Glavaš¹,

Karan²,

Vulić³

2020

Proceedings of the 28th International Conference on Computational Linguistics

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We present XHATE-999, a multi-domain and multilingual evaluation data set for abusive language detection. By aligning test instances across six typologically diverse languages, XHATE-999 for the first time allows for disentanglement of the domain transfer and language transfer effects in abusive language detection. We conduct a series of domain-and language-transfer experiments with state-of-the-art monolingual and multilingual transformer models, setting strong baseline results and profiling XHATE-999 as a comprehensive evaluation resource for abusive language detection. Finally, we show that domain-and language-adaptation, via intermediate masked language modeling on abusive corpora in the target language, can lead to substantially improved abusive language detection in the target language in the zero-shot transfer setups.

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Section: Introductionmentioning

confidence: 99%

XHate-999: Analyzing and Detecting Abusive Language Across Domains and Languages

Glavaš¹,

Karan²,

Vulić³

2020

Proceedings of the 28th International Conference on Computational Linguistics

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“…Fighting abusive language online is becoming more and more important in a world where online social media plays a significant role in shaping people's minds (Perse and Lambe, 2016). Nevertheless, major social media companies like Twitter find it difficult to tackle this problem (Meyer, 2016), as the huge number of posts cannot be mediated with only human resources.…”

Section: Introductionmentioning

confidence: 99%

One-step and Two-step Classification for Abusive Language Detection on Twitter

Park¹,

Fung²

2017

Proceedings of the First Workshop on Abusive Language Online

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Automatic abusive language detection is a difficult but important task for online social media. Our research explores a twostep approach of performing classification on abusive language and then classifying into specific types and compares it with one-step approach of doing one multi-class classification for detecting sexist and racist languages. With a public English Twitter corpus of 20 thousand tweets in the type of sexism and racism, our approach shows a promising performance of 0.827 Fmeasure by using HybridCNN in one-step and 0.824 F-measure by using logistic regression in two-steps.

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“…The president's agenda leads the media agenda, which further lead the public agenda; the president's attitude leads the media attitude, which further leads the public attitude (although with weaker evidence). A school of media sociologists argues that mass media may have little influence in the modern area (Klapper 1960;Perse and Lambe 2016), the results of this study indicate otherwise. The processes, nevertheless, are more complex than discussions in the literature.…”

Section: Discussionmentioning

confidence: 53%