Improving Hate Speech Type and Target Detection with Hateful Metaphor Features

Lemmens, Jens; Markov, Ilia; Daelemans, Walter

doi:10.18653/v1/2021.nlp4if-1.2

Cited by 11 publications

(10 citation statements)

References 21 publications

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“…Some studies aim at answering the research question how various target groups are referred to. As an example, Lemmens et al [54] analyzed the language of hateful Dutch comments regarding classes of metaphoric terms, including body parts, products, animals, or mental conditions. Such a closed world approach, however, does not permit the identification of targets that were not known at the development time of the HOF detection system.…”

Section: Target Classificationmentioning

confidence: 99%

“…Such a closed world approach, however, does not permit the identification of targets that were not known at the development time of the HOF detection system. This is to some degree addressed by Silva et al [55], who developed a rule-based method to identify target mentions that are then, similarly to Lemmens et al [54], compared regarding the expressions that are used.…”

Section: Target Classificationmentioning

confidence: 99%

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Multi-Task Learning with Sentiment, Emotion, and Target Detection to Recognize Hate Speech and Offensive Language

Plaza-del-Arco¹,

Halat²,

Padó³

et al. 2021

Preprint

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The recognition of hate speech and offensive language (HOF) is commonly formulated as a classification task which asks models to decide if a text contains HOF. This task is challenging because of the large variety of explicit and implicit ways to verbally attack a target person or group. In this paper, we investigate whether HOF detection can profit by taking into account the relationships between HOF and similar concepts: (a) HOF is related to sentiment analysis because hate speech is typically a negative statement and expresses a negative opinion; (b) it is related to emotion analysis, as expressed hate points to the author experiencing (or pretending to experience) anger while the addressees experience (or are intended to experience) fear. (c) Finally, one constituting element of HOF is the (explicit or implicit) mention of a targeted person or group. On this basis, we hypothesize that HOF detection shows improvements when being modeled jointly with these concepts, in a multi-task learning setup. We base our experiments on existing data sets for each of these concepts (sentiment, emotion, target of HOF) and evaluate our models as a participant (as team IMS-SINAI) in the HASOC FIRE 2021 English Subtask 1A: "Subtask 1A: Identifying Hate, offensive and profane content from the post". Based on model-selection experiments in which we consider multiple available resources and submissions to the shared task, we find that the combination of the CrowdFlower emotion corpus, the SemEval 2016 Sentiment Corpus, and the OffensEval 2019 target detection data leads to an F1 =.7947 in a multi-head multi-task learning model based on BERT, in comparison to .7895 of a plain BERT model. On the HASOC 2019 test data, this result is more substantial with an increase by 2pp in F1 (from 0.78 F1 to 0.8 F1) and a considerable increase in recall. Across both data sets (2019, 2021), the recall is particularly increased for the class of HOF (6pp for the 2019 data and 3pp for the 2021 data), showing that MTL with emotion, sentiment, and target identification is an appropriate approach for early warning systems that might be deployed in social media platforms.

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Section: Target Classificationmentioning

confidence: 99%

Section: Target Classificationmentioning

confidence: 99%

Multi-Task Learning with Sentiment, Emotion, and Target Detection to Recognize Hate Speech and Offensive Language

Plaza-del-Arco¹,

Halat²,

Padó³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…[Zampieri et al, 2019] and [Chuang et al, 2021]) as well as accurately determining the category of different types of hate speech / offensive language (e.g. [Zampieri, 2021] and [Lemmens et al, 2021]), or even identifying the targets of offensive language (e.g. [Lemmens et al, 2021], [Shvets et al, 2021]).…”

Section: Introductionmentioning

confidence: 99%

“…[Zampieri, 2021] and [Lemmens et al, 2021]), or even identifying the targets of offensive language (e.g. [Lemmens et al, 2021], [Shvets et al, 2021]). Usually this involves supervised machine learning and even the creation of language models fine-tuned for hate-speech (e.g.…”

Section: Introductionmentioning

confidence: 99%

Hate speech, Censorship, and Freedom of Speech: The Changing Policies of Reddit

Wickham

Öhman

2022

Journal of Data Mining &Amp; Digital Humanities

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This paper examines the shift in focus on content policies and user attitudes on the social media platform Reddit. We do this by focusing on comments from general Reddit users from five posts made by admins (moderators) on updates to Reddit Content Policy. All five concern the nature of what kind of content is allowed to be posted on Reddit, and which measures will be taken against content that violates these policies. We use topic modeling to probe how the general discourse for Redditors has changed around limitations on content, and later, limitations on hate speech, or speech that incites violence against a particular group. We show that there is a clear shift in both the contents and the user attitudes that can be linked to contemporary societal upheaval as well as newly passed laws and regulations, and contribute to the wider discussion on hate speech moderation.

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“…To our knowledge, only metaphorical and stereotyped information have been exploited for abusive language detection. Interestingly, Lemmens et al [2021] proved the contribution of hateful metaphors as features for the identification of the type and target of hate speech in Dutch Facebook comments in models based on classical machine learning and transformers. Whereas Lavergne et al [2020] exploit the multi-annotation proposed in HaSpeeDe2020 about the presence of hate speech and stereotype in tweets to train a multi-task learning-based model reaching the best score in hate speech detection in tweets.…”

Section: Open Challenge: Implicit Abusementioning

confidence: 98%

Sarcasm and Implicitness in Abusive Language Detection: A Multilingual Perspective

Frenda¹

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At the end of this work, I would like to thank all people who supported me during this new step of my professional and personal life. During this journey, various professional figures guided and inspired me. Thanks to Professor Viviana Patti and Professor Paolo Rosso, who patiently shaped the researcher that I am today. Thanks to Professor Manuel Montes-y-Gómez and Professor Rafael Valencia García, who from the beginning showed me how to look deeply and differently at data. Thanks to Professor Noriko Kando for hosting me at National Institute of Informatics in Tokyo, and introducing me to her fascinating culture. Thanks to Professor Mariona Taulé Delor for receiving me in Barcelona, and being always friendly and professionally available to help me. Thanks to reviewers, Professor Rafael

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Improving Hate Speech Type and Target Detection with Hateful Metaphor Features

Cited by 11 publications

References 21 publications

Multi-Task Learning with Sentiment, Emotion, and Target Detection to Recognize Hate Speech and Offensive Language

Multi-Task Learning with Sentiment, Emotion, and Target Detection to Recognize Hate Speech and Offensive Language

Hate speech, Censorship, and Freedom of Speech: The Changing Policies of Reddit

Sarcasm and Implicitness in Abusive Language Detection: A Multilingual Perspective

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