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    <titleInfo>
        <title>On the Identification and Forecasting of Hate Speech in Inceldom</title>
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    <name type="personal">
        <namePart type="given">Paolo</namePart>
        <namePart type="family">Gajo</namePart>
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    <name type="personal">
        <namePart type="given">Arianna</namePart>
        <namePart type="family">Muti</namePart>
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    <name type="personal">
        <namePart type="given">Katerina</namePart>
        <namePart type="family">Korre</namePart>
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            <roleTerm authority="marcrelator" type="text">author</roleTerm>
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    <name type="personal">
        <namePart type="given">Silvia</namePart>
        <namePart type="family">Bernardini</namePart>
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            <roleTerm authority="marcrelator" type="text">author</roleTerm>
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    <name type="personal">
        <namePart type="given">Alberto</namePart>
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        <dateIssued>2023-09</dateIssued>
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            <title>Proceedings of the 14th International Conference on Recent Advances in Natural Language Processing</title>
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        <name type="personal">
            <namePart type="given">Ruslan</namePart>
            <namePart type="family">Mitkov</namePart>
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        <name type="personal">
            <namePart type="given">Galia</namePart>
            <namePart type="family">Angelova</namePart>
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            <publisher>INCOMA Ltd., Shoumen, Bulgaria</publisher>
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    <abstract>Spotting hate speech in social media posts is crucial to increase the civility of the Web and has been thoroughly explored in the NLP community. For the first time, we introduce a multilingual corpus for the analysis and identification of hate speech in the domain of inceldom, built from incel Web forums in English and Italian, including expert annotation at the post level for two kinds of hate speech: misogyny and racism. This resource paves the way for the development of mono- and cross-lingual models for (a) the identification of hateful (misogynous and racist) posts and (b) the forecasting of the amount of hateful responses that a post is likely to trigger. Our experiments aim at improving the performance of Transformer-based models using masked language modeling pre-training and dataset merging. The results show that these strategies boost the models’ performance in all settings (binary classification, multi-label classification and forecasting), especially in the cross-lingual scenarios.</abstract>
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        <date>2023-09</date>
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%0 Conference Proceedings
%T On the Identification and Forecasting of Hate Speech in Inceldom
%A Gajo, Paolo
%A Muti, Arianna
%A Korre, Katerina
%A Bernardini, Silvia
%A Barrón-Cedeño, Alberto
%Y Mitkov, Ruslan
%Y Angelova, Galia
%S Proceedings of the 14th International Conference on Recent Advances in Natural Language Processing
%D 2023
%8 September
%I INCOMA Ltd., Shoumen, Bulgaria
%C Varna, Bulgaria
%F gajo-etal-2023-identification
%X Spotting hate speech in social media posts is crucial to increase the civility of the Web and has been thoroughly explored in the NLP community. For the first time, we introduce a multilingual corpus for the analysis and identification of hate speech in the domain of inceldom, built from incel Web forums in English and Italian, including expert annotation at the post level for two kinds of hate speech: misogyny and racism. This resource paves the way for the development of mono- and cross-lingual models for (a) the identification of hateful (misogynous and racist) posts and (b) the forecasting of the amount of hateful responses that a post is likely to trigger. Our experiments aim at improving the performance of Transformer-based models using masked language modeling pre-training and dataset merging. The results show that these strategies boost the models’ performance in all settings (binary classification, multi-label classification and forecasting), especially in the cross-lingual scenarios.
%U https://aclanthology.org/2023.ranlp-1.42
%P 373-384

Markdown (Informal)

[On the Identification and Forecasting of Hate Speech in Inceldom](https://aclanthology.org/2023.ranlp-1.42) (Gajo et al., RANLP 2023)

• On the Identification and Forecasting of Hate Speech in Inceldom (Gajo et al., RANLP 2023)

ACL

• Paolo Gajo, Arianna Muti, Katerina Korre, Silvia Bernardini, and Alberto Barrón-Cedeño. 2023. On the Identification and Forecasting of Hate Speech in Inceldom. In Proceedings of the 14th International Conference on Recent Advances in Natural Language Processing, pages 373–384, Varna, Bulgaria. INCOMA Ltd., Shoumen, Bulgaria.