Implicitly Abusive Language – What does it actually look like and why are we not getting there?

Wiegand, Michael; Ruppenhofer, Josef; Eder, Elisabeth

doi:10.18653/v1/2021.naacl-main.48

Cited by 30 publications

(37 citation statements)

References 43 publications

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“…The detection of implicitly abusive language, i.e. abusive language that is not conveyed by abusive words, has been recently recognised as one of the most prominent challenges in the field (Wiegand et al, 2021;Caselli et al, 2020). Implicit abuse is in rather direct contrast with lexicon-based methods, based on the use of lists of swear words.…”

Section: Discussionmentioning

confidence: 99%

Investigating the role of swear words in abusive language detection tasks

Pamungkas

Basile

Patti

2022

Lang Resources & Evaluation

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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.

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Section: Discussionmentioning

confidence: 99%

Investigating the role of swear words in abusive language detection tasks

Pamungkas

Basile

Patti

2022

Lang Resources & Evaluation

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“…Pinpointing the specific span of offensive remark is essential, as not all sentences or comments that include such span are necessarily offensive. The clause and its relation with other elements of the context can facilitate the model to correctly identify implicit and nuanced offense as well (Wiegand et al, 2021). Moreover, the spans may be used to train explainable models, as is shown in recent works (Mathew et al, 2020;Lei et al, 2016).…”

Section: Span Annotationmentioning

confidence: 97%

KOLD: Korean Offensive Language Dataset

Jeong¹,

Oh²,

Ahn³

et al. 2022

Preprint

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Warning: this paper contains content that may be offensive or upsettingAlthough large attention has been paid to the detection of hate speech, most work has been done in English, failing to make it applicable to other languages. To fill this gap, we present a Korean offensive language dataset (KOLD), 40k comments labeled with offensiveness, target, and targeted group information. We also collect two types of span, offensive and target span that justifies the decision of the categorization within the text. Comparing the distribution of targeted groups with the existing English dataset, we point out the necessity of a hate speech dataset fitted to the language that best reflects the culture. Trained with our dataset, we report the baseline performance of the models built on top of large pretrained language models. We also show that title information serves as context and is helpful to discern the target of hatred, especially when they are omitted in the comment.

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“…Sap et al ( 2020 ) annotated a large corpus of abusive online posts for the implied stereotypical meaning and showed that the current generative models struggle to effectively reproduce human interpretations of the stereotypical views expressed in implicit abuse. The current state of the field is summarized by Wiegand et al ( 2021 ), who identified stereotypes as one of the sub-types of implicitly abusive language that is not learned well by current abusive language detection models and that requires new datasets with a revised task formulation, data sampling strategies, and annotation schemes.…”

Section: Background and Related Workmentioning

confidence: 99%

Computational Modeling of Stereotype Content in Text

Fraser

Kiritchenko

Nejadgholi

2022

Front. Artif. Intell.

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Stereotypes are encountered every day, in interpersonal communication as well as in entertainment, news stories, and on social media. In this study, we present a computational method to mine large, naturally occurring datasets of text for sentences that express perceptions of a social group of interest, and then map these sentences to the two-dimensional plane of perceived warmth and competence for comparison and interpretation. This framework is grounded in established social psychological theory, and validated against both expert annotation and crowd-sourced stereotype data. Additionally, we present two case studies of how the model might be used to answer questions using data “in-the-wild,” by collecting Twitter data about women and older adults. Using the data about women, we are able to observe how sub-categories of women (e.g., Black women and white women) are described similarly and differently from each other, and from the superordinate group of women in general. Using the data about older adults, we show evidence that the terms people use to label a group (e.g., old people vs. senior citizens) are associated with different stereotype content. We propose that this model can be used by other researchers to explore questions of how stereotypes are expressed in various large text corpora.

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Implicitly Abusive Language – What does it actually look like and why are we not getting there?

Cited by 30 publications

References 43 publications

Investigating the role of swear words in abusive language detection tasks

Investigating the role of swear words in abusive language detection tasks

KOLD: Korean Offensive Language Dataset

Computational Modeling of Stereotype Content in Text

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