Investigating the role of swear words in abusive language detection tasks

Abstract

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.

Towards understanding the role of content-based and contextualized features in detecting abuse on Twitter

“…Keyword or lexicon-based approaches employ word lists or dictionaries [15] , [16] . They compare every word with a lexicon that contains the sentiment or polarity associated with expressions [17] , [18] , [19] .…”

Privacy-Preserving Data Collection and Analysis for Smart Cities

“…Furthermore, we tallied the number of swear words used by each user. We sourced the list of swear words from https://www.noswearing.com/, which has been utilized in numerous studies (e.g., [ 4,31,46]). Data on the number of swear words sent by each user was gathered under .∈-LDP.…”

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