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        <title>Aggression Detection in Social Media: Using Deep Neural Networks, Data Augmentation, and Pseudo Labeling</title>
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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>
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    <abstract>With the advent of the read-write web which facilitates social interactions in online spaces, the rise of anti-social behaviour in online spaces has attracted the attention of researchers. In this paper, we address the challenge of automatically identifying aggression in social media posts. Our team, saroyehun, participated in the English track of the Aggression Detection in Social Media Shared Task. On this task, we investigate the efficacy of deep neural network models of varying complexity. Our results reveal that deep neural network models require more data points to do better than an NBSVM linear baseline based on character n-grams. Our improved deep neural network models were trained on augmented data and pseudo labeled examples. Our LSTM classifier receives a weighted macro-F1 score of 0.6425 to rank first overall on the Facebook subtask of the shared task. On the social media sub-task, our CNN-LSTM model records a weighted macro-F1 score of 0.5920 to place third overall.</abstract>
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%0 Conference Proceedings
%T Aggression Detection in Social Media: Using Deep Neural Networks, Data Augmentation, and Pseudo Labeling
%A Aroyehun, Segun Taofeek
%A Gelbukh, Alexander
%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 aroyehun-gelbukh-2018-aggression
%X With the advent of the read-write web which facilitates social interactions in online spaces, the rise of anti-social behaviour in online spaces has attracted the attention of researchers. In this paper, we address the challenge of automatically identifying aggression in social media posts. Our team, saroyehun, participated in the English track of the Aggression Detection in Social Media Shared Task. On this task, we investigate the efficacy of deep neural network models of varying complexity. Our results reveal that deep neural network models require more data points to do better than an NBSVM linear baseline based on character n-grams. Our improved deep neural network models were trained on augmented data and pseudo labeled examples. Our LSTM classifier receives a weighted macro-F1 score of 0.6425 to rank first overall on the Facebook subtask of the shared task. On the social media sub-task, our CNN-LSTM model records a weighted macro-F1 score of 0.5920 to place third overall.
%U https://aclanthology.org/W18-4411/
%P 90-97

Markdown (Informal)

[Aggression Detection in Social Media: Using Deep Neural Networks, Data Augmentation, and Pseudo Labeling](https://aclanthology.org/W18-4411/) (Aroyehun & Gelbukh, TRAC 2018)

• Aggression Detection in Social Media: Using Deep Neural Networks, Data Augmentation, and Pseudo Labeling (Aroyehun & Gelbukh, TRAC 2018)

ACL

• Segun Taofeek Aroyehun and Alexander Gelbukh. 2018. Aggression Detection in Social Media: Using Deep Neural Networks, Data Augmentation, and Pseudo Labeling. In Proceedings of the First Workshop on Trolling, Aggression and Cyberbullying (TRAC-2018), pages 90–97, Santa Fe, New Mexico, USA. Association for Computational Linguistics.