Hate Speech Criteria: A Modular Approach to Task-Specific Hate Speech Definitions

Urja, Khurana,; Vermeulen, Ivar; Nalisnick, Eric; Noorloos, Marloes Van; Fokkens, Antske

doi:10.18653/v1/2022.woah-1.17

Cited by 6 publications

(8 citation statements)

References 27 publications

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“…This is not a trivial task given that existing anno- 2). We expanded the framework for developing annotation guidelines for hate speech by Khurana et al (2022b) with suggestions for aspects which could be taken into account for annotating data sets of abusive, offensive, uncivil or toxic comments based on our findings of the previous sections (Figure 3).…”

Section: Discussionmentioning

confidence: 99%

“…1 Contact: pia.pachinger@tuwien.ac.at Khurana et al (2022a) proposed a framework consisting of the aspects target group, dominance of target group, perpetrator characteristics, type of negative group reference, and potential consequences. This framework should provide the means to classify data sets on hate speech in a unified manner, but for now it has not been expanded on more subtle forms of abuse such as toxic speech.…”

Section: Introductionmentioning

confidence: 99%

“…Figure 3: Aspects which can be taken into account when annotating abusive, offensive, toxic or uncivil comments.The scheme is an expansion of a proposed scheme for hate speech annotation byKhurana et al (2022b). Aspects proposed in referenced papers in the table of Section 2 and instances found in the analyzed papers (Section 5) were used for expanding the framework.…”

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confidence: 99%

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Proceedings of the First Workshop on Cross-Cultural Considerations in NLP (C3NLP)

2023

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This paper investigates the performance of massively multilingual neural machine translation (NMT) systems in translating Yorùbá greetings (ε kú 1 ), which are a big part of Yorùbá language and culture, into English. To evaluate these models, we present IkiniYorùbá, a Yorùbá-English translation dataset containing some Yorùbá greetings, and sample use cases. We analysed the performance of different multilingual NMT systems including Google Translate and NLLB and show that these models struggle to accurately translate Yorùbá greetings into English. In addition, we trained a Yorùbá-English model by finetuning an existing NMT model on the training split of IkiniYorùbá and this achieved better performance when compared to the pre-trained multilingual NMT models, although they were trained on a large volume of data. * Equal contribution. 1 For simplicity of notation in the title, we make use of εthe Beninese Yorùbá letter representation of E . (which is used in Nigeria), and provides the context of greeting.Source: E . kú ojúmó . , e . sì kú déédé àsìkò yìí. Target: Good morning and compliment for this period.NLLB: You have died, and you have died to this hour. Google Translate: Die every day, and die at this time. Our Model: Good morning and compliment for this time.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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confidence: 99%

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Proceedings of the First Workshop on Cross-Cultural Considerations in NLP (C3NLP)

2023

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“…One interesting aspect of our data is that, unlike prior studies, we focus on the nature of speech that can provoke hate, regardless of whether the speech itself includes hatred. That is, while prior studies in general have only emphasized the propagation of hatred through a chain of hate speech, we claim that even non-hate speech can be the cause of hatred (Anderson and Barnes, 2022;Khurana et al, 2022). For instance, in Table 3, we further categorize HIS into hate HIS and non-hate HIS.…”

Section: Data Descriptionmentioning

confidence: 95%

Uncovering the Root of Hate Speech: A Dataset for Identifying Hate Instigating Speech

Park,

Shim,

Lee

2023

Findings of the Association for Computational Linguistics: EMNLP 2023

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While many prior studies have applied computational approaches, such as machine learning, to detect and moderate hate speech, only scant attention has been paid to the task of identifying the underlying cause of hate speech. In this study, we introduce the concept of hate instigating speech, which refers to a specific type of textual posts on online platforms that stimulate or provoke others to engage in hate speech. The identification of hate instigating speech carries substantial practical implications for effective hate speech moderation. Rather than targeting individual instances of hate speech, by focusing on their roots, i.e., hate instigating speech, it becomes possible to significantly reduce the volume of content that requires review for moderation. Additionally, targeting hate instigating speech enables early prevention of the spread and propagation of hate speech, further enhancing the effectiveness of moderation efforts. However, several challenges hinder researchers from addressing the identification of hate instigating speech. First, there is a lack of comprehensive datasets specifically annotated for hate instigation, making it difficult to train and evaluate computational models effectively. Second, the subtle and nuanced nature of hate instigating speech (e.g., seemingly non-offensive texts serve as catalysts for triggering hate speech) makes it difficult to apply off-the-shelf machine learning models to the problem. To address these challenges, in this study, we have developed and released a multilingual dataset specifically designed for the task of identifying hate instigating speech. Specifically, it encompasses both English and Korean, allowing for a comprehensive examination of hate instigating speech across different linguistic contexts. We have applied existing machine learning models to our dataset and the results demonstrate that the extant models alone are insufficient for effectively detecting hate instigating speech. This finding highlights the need for further attention from the academic community to address this specific challenge. We expect our study and dataset to inspire researchers to explore innovative methods that can enhance the accuracy of hate instigating speech detection, ultimately contributing to more effective moderation and prevention of hate speech propagation online.

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“…Figure3: Aspects which can be taken into account when annotating abusive, offensive, toxic or uncivil comments. The scheme is an expansion of a proposed scheme for hate speech annotation byKhurana et al (2022b). Aspects proposed in referenced papers in the table of Section 2 and instances found in the analyzed papers (Section 5) were used for expanding the framework.…”

mentioning

confidence: 99%

Toward Disambiguating the Definitions of Abusive, Offensive, Toxic, and Uncivil Comments

Pachinger,

Hanbury,

Neidhardt

et al. 2023

Proceedings of the First Workshop on Cross-Cultural Considerations in NLP (C3NLP)

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The definitions of abusive, offensive, toxic and uncivil comments used for annotating corpora for automated content moderation are highly intersected and researchers call for their disambiguation. We summarize the definitions of these terms as they appear in 23 papers across different fields. We compare examples given for uncivil, offensive, and toxic comments, attempting to foster more unified scientific resources. Additionally, we stress that the term incivility that frequently appears in social science literature has hardly been mentioned in the literature we analyzed that focuses on computational linguistics and natural language processing.

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Hate Speech Criteria: A Modular Approach to Task-Specific Hate Speech Definitions

Cited by 6 publications

References 27 publications

Proceedings of the First Workshop on Cross-Cultural Considerations in NLP (C3NLP)

Proceedings of the First Workshop on Cross-Cultural Considerations in NLP (C3NLP)

Uncovering the Root of Hate Speech: A Dataset for Identifying Hate Instigating Speech

Toward Disambiguating the Definitions of Abusive, Offensive, Toxic, and Uncivil Comments

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