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        <title>Aggression Detection in Social Media using Deep Neural Networks</title>
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        <namePart type="given">Sreekanth</namePart>
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            <title>Proceedings of the First Workshop on Trolling, Aggression and Cyberbullying (TRAC-2018)</title>
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            <namePart type="given">Ritesh</namePart>
            <namePart type="family">Kumar</namePart>
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            <namePart type="given">Atul</namePart>
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    <abstract>With the rise of user-generated content in social media coupled with almost non-existent moderation in many such systems, aggressive contents have been observed to rise in such forums. In this paper, we work on the problem of aggression detection in social media. Aggression can sometimes be expressed directly or overtly or it can be hidden or covert in the text. On the other hand, most of the content in social media is non-aggressive in nature. We propose an ensemble based system to classify an input post to into one of three classes, namely, Overtly Aggressive, Covertly Aggressive, and Non-aggressive. Our approach uses three deep learning methods, namely, Convolutional Neural Networks (CNN) with five layers (input, convolution, pooling, hidden, and output), Long Short Term Memory networks (LSTM), and Bi-directional Long Short Term Memory networks (Bi-LSTM). A majority voting based ensemble method is used to combine these classifiers (CNN, LSTM, and Bi-LSTM). We trained our method on Facebook comments dataset and tested on Facebook comments (in-domain) and other social media posts (cross-domain). Our system achieves the F1-score (weighted) of 0.604 for Facebook posts and 0.508 for social media posts.</abstract>
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%0 Conference Proceedings
%T Aggression Detection in Social Media using Deep Neural Networks
%A Madisetty, Sreekanth
%A Sankar Desarkar, Maunendra
%Y Kumar, Ritesh
%Y Ojha, Atul Kr.
%Y Zampieri, Marcos
%Y Malmasi, Shervin
%S Proceedings of the First Workshop on Trolling, Aggression and Cyberbullying (TRAC-2018)
%D 2018
%8 August
%I Association for Computational Linguistics
%C Santa Fe, New Mexico, USA
%F madisetty-sankar-desarkar-2018-aggression
%X With the rise of user-generated content in social media coupled with almost non-existent moderation in many such systems, aggressive contents have been observed to rise in such forums. In this paper, we work on the problem of aggression detection in social media. Aggression can sometimes be expressed directly or overtly or it can be hidden or covert in the text. On the other hand, most of the content in social media is non-aggressive in nature. We propose an ensemble based system to classify an input post to into one of three classes, namely, Overtly Aggressive, Covertly Aggressive, and Non-aggressive. Our approach uses three deep learning methods, namely, Convolutional Neural Networks (CNN) with five layers (input, convolution, pooling, hidden, and output), Long Short Term Memory networks (LSTM), and Bi-directional Long Short Term Memory networks (Bi-LSTM). A majority voting based ensemble method is used to combine these classifiers (CNN, LSTM, and Bi-LSTM). We trained our method on Facebook comments dataset and tested on Facebook comments (in-domain) and other social media posts (cross-domain). Our system achieves the F1-score (weighted) of 0.604 for Facebook posts and 0.508 for social media posts.
%U https://aclanthology.org/W18-4415
%P 120-127

Markdown (Informal)

[Aggression Detection in Social Media using Deep Neural Networks](https://aclanthology.org/W18-4415) (Madisetty & Sankar Desarkar, TRAC 2018)

• Aggression Detection in Social Media using Deep Neural Networks (Madisetty & Sankar Desarkar, TRAC 2018)

ACL

• Sreekanth Madisetty and Maunendra Sankar Desarkar. 2018. Aggression Detection in Social Media using Deep Neural Networks. In Proceedings of the First Workshop on Trolling, Aggression and Cyberbullying (TRAC-2018), pages 120–127, Santa Fe, New Mexico, USA. Association for Computational Linguistics.