Investigating the role of swear words in abusive language detection tasks.
Swearing plays an ubiquitous role in everyday conversations among humans, both in oral and textual communication, and occurs frequently in social media texts, typically featured by informal language and spontaneous writing. Such occurrences can be linked to an abusive context, when they contribute t...
| Publicado en: | Language Resources & Evaluation Vol. 57; no. 1; pp. 155 - 189 |
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| Autores principales: | , , |
| Formato: | Artículo |
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Springer Nature
Mar2023
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| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=162506759&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 162506759 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 1574020X 179V jtl: Language Resources & Evaluation issn: 1574020X maglogo: N pubinfo: dt: Mar2023 vid: 57 iid: 1 pid: 237 pub: Springer Nature artinfo: ui: 162506759 10.1007/s10579-022-09582-8 ppf: 155 ppct: 34 formats: fmt: – @attributes: type: T – @attributes: type: P size: 646KB tig: atl: Investigating the role of swear words in abusive language detection tasks. aug: au: Pamungkas, Endang Wahyu Basile, Valerio Patti, Viviana affil: Dipartimento di Informatica, University of Turin, Turin, Italy Department of Informatics Engineering, Universitas Muhammadiyah Surakarta, 57162, Surakarta, Central Java, Indonesia su: X Corp. Oral communication Sentiment analysis Hate Internet content moderation Social skills Language & languages sug: subj: X Corp. Oral communication Sentiment analysis Hate Internet content moderation Social skills Language & languages keyword: Abusive language detection Content moderation Hate speech detection Social media Swear words abusiveness ab: Swearing plays an ubiquitous role in everyday conversations among humans, both in oral and textual communication, and occurs frequently in social media texts, typically featured by informal language and spontaneous writing. Such occurrences can be linked to an abusive context, when they contribute to the expression of hatred and to the abusive effect, causing harm and offense. However, swearing is multifaceted and is often used in casual contexts, also with positive social functions. In this study, we explore the phenomenon of swearing in Twitter conversations, by automatically predicting the abusiveness of a swear word in a tweet as the main investigation perspective. We developed the Twitter English corpus SWAD (Swear Words Abusiveness Dataset), where abusive swearing is manually annotated at the word level. Our collection consists of 2577 instances in total from two phases of manual annotation. We developed models to automatically predict abusive swearing, to provide an intrinsic evaluation of SWAD and confirm the robustness of the resource. We model this prediction task as three different tasks, namely sequence labeling, text classification, and target-based swear word abusiveness prediction. We experimentally found that our intention to model the task similarly to aspect-based sentiment analysis leads to promising results. Subsequently, we employ the classifier to improve the prediction of abusive language in several standard benchmarks. The results of our experiments show that additional abusiveness feature of the swear words is able to improve the performance of abusive language detection models in several benchmark datasets. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y custom: Language Resources & Evaluation is a copyright of Springer, 2023. All Rights Reserved. item: Language Resources & Evaluation holder: Springer Nature dt: @attributes: year: 2023 holdings: @attributes: islocal: N |
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