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        <title>Combining Shallow and Deep Learning for Aggressive Text Detection</title>
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    <name type="personal">
        <namePart type="given">Viktor</namePart>
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        <namePart type="given">Mladen</namePart>
        <namePart type="family">Karan</namePart>
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            <title>Proceedings of the First Workshop on Trolling, Aggression and Cyberbullying (TRAC-2018)</title>
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        <name type="personal">
            <namePart type="given">Ritesh</namePart>
            <namePart type="family">Kumar</namePart>
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        <name type="personal">
            <namePart type="given">Atul</namePart>
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            <namePart type="family">Ojha</namePart>
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    <abstract>We describe the participation of team TakeLab in the aggression detection shared task at the TRAC1 workshop for English. Aggression manifests in a variety of ways. Unlike some forms of aggression that are impossible to prevent in day-to-day life, aggressive speech abounding on social networks could in principle be prevented or at least reduced by simply disabling users that post aggressively worded messages. The first step in achieving this is to detect such messages. The task, however, is far from being trivial, as what is considered as aggressive speech can be quite subjective, and the task is further complicated by the noisy nature of user-generated text on social networks. Our system learns to distinguish between open aggression, covert aggression, and non-aggression in social media texts. We tried different machine learning approaches, including traditional (shallow) machine learning models, deep learning models, and a combination of both. We achieved respectable results, ranking 4th and 8th out of 31 submissions on the Facebook and Twitter test sets, respectively.</abstract>
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%0 Conference Proceedings
%T Combining Shallow and Deep Learning for Aggressive Text Detection
%A Golem, Viktor
%A Karan, Mladen
%A Šnajder, Jan
%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 golem-etal-2018-combining
%X We describe the participation of team TakeLab in the aggression detection shared task at the TRAC1 workshop for English. Aggression manifests in a variety of ways. Unlike some forms of aggression that are impossible to prevent in day-to-day life, aggressive speech abounding on social networks could in principle be prevented or at least reduced by simply disabling users that post aggressively worded messages. The first step in achieving this is to detect such messages. The task, however, is far from being trivial, as what is considered as aggressive speech can be quite subjective, and the task is further complicated by the noisy nature of user-generated text on social networks. Our system learns to distinguish between open aggression, covert aggression, and non-aggression in social media texts. We tried different machine learning approaches, including traditional (shallow) machine learning models, deep learning models, and a combination of both. We achieved respectable results, ranking 4th and 8th out of 31 submissions on the Facebook and Twitter test sets, respectively.
%U https://aclanthology.org/W18-4422
%P 188-198

Markdown (Informal)

[Combining Shallow and Deep Learning for Aggressive Text Detection](https://aclanthology.org/W18-4422) (Golem et al., TRAC 2018)

• Combining Shallow and Deep Learning for Aggressive Text Detection (Golem et al., TRAC 2018)

ACL

• Viktor Golem, Mladen Karan, and Jan Šnajder. 2018. Combining Shallow and Deep Learning for Aggressive Text Detection. In Proceedings of the First Workshop on Trolling, Aggression and Cyberbullying (TRAC-2018), pages 188–198, Santa Fe, New Mexico, USA. Association for Computational Linguistics.