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    <titleInfo>
        <title>An Efficient BERT Based Approach to Detect Aggression and Misogyny</title>
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    <name type="personal">
        <namePart type="given">Sandip</namePart>
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            <title>Proceedings of the 18th International Conference on Natural Language Processing (ICON)</title>
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        <name type="personal">
            <namePart type="given">Sivaji</namePart>
            <namePart type="family">Bandyopadhyay</namePart>
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            <namePart type="given">Sobha</namePart>
            <namePart type="given">Lalitha</namePart>
            <namePart type="family">Devi</namePart>
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            <namePart type="given">Pushpak</namePart>
            <namePart type="family">Bhattacharyya</namePart>
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            <publisher>NLP Association of India (NLPAI)</publisher>
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                <placeTerm type="text">National Institute of Technology Silchar, Silchar, India</placeTerm>
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    <abstract>Social media is bustling with ever growing cases of trolling, aggression and hate. A huge amount of social media data is generated each day which is insurmountable for manual inspection. In this work, we propose an efficient and fast method to detect aggression and misogyny in social media texts. We use data from the Second Workshop on Trolling, Aggression and Cyber Bullying for our task. We employ a BERT based model to augment our data. Next we employ Tf-Idf and XGBoost for detecting aggression and misogyny. Our model achieves 0.73 and 0.85 Weighted F1 Scores on the 2 prediction tasks, which are comparable to the state of the art. However, the training time, model size and resource requirements of our model are drastically lower compared to the state of the art models, making our model useful for fast inference.</abstract>
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        <url>https://aclanthology.org/2021.icon-main.60</url>
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        <date>2021-12</date>
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            <start>493</start>
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%0 Conference Proceedings
%T An Efficient BERT Based Approach to Detect Aggression and Misogyny
%A Dutta, Sandip
%A Majumder, Utso
%A Naskar, Sudip
%Y Bandyopadhyay, Sivaji
%Y Devi, Sobha Lalitha
%Y Bhattacharyya, Pushpak
%S Proceedings of the 18th International Conference on Natural Language Processing (ICON)
%D 2021
%8 December
%I NLP Association of India (NLPAI)
%C National Institute of Technology Silchar, Silchar, India
%F dutta-etal-2021-efficient
%X Social media is bustling with ever growing cases of trolling, aggression and hate. A huge amount of social media data is generated each day which is insurmountable for manual inspection. In this work, we propose an efficient and fast method to detect aggression and misogyny in social media texts. We use data from the Second Workshop on Trolling, Aggression and Cyber Bullying for our task. We employ a BERT based model to augment our data. Next we employ Tf-Idf and XGBoost for detecting aggression and misogyny. Our model achieves 0.73 and 0.85 Weighted F1 Scores on the 2 prediction tasks, which are comparable to the state of the art. However, the training time, model size and resource requirements of our model are drastically lower compared to the state of the art models, making our model useful for fast inference.
%U https://aclanthology.org/2021.icon-main.60
%P 493-498

Markdown (Informal)

[An Efficient BERT Based Approach to Detect Aggression and Misogyny](https://aclanthology.org/2021.icon-main.60) (Dutta et al., ICON 2021)

• An Efficient BERT Based Approach to Detect Aggression and Misogyny (Dutta et al., ICON 2021)

ACL

• Sandip Dutta, Utso Majumder, and Sudip Naskar. 2021. An Efficient BERT Based Approach to Detect Aggression and Misogyny. In Proceedings of the 18th International Conference on Natural Language Processing (ICON), pages 493–498, National Institute of Technology Silchar, Silchar, India. NLP Association of India (NLPAI).