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INNSBDDL 2019: Recent Advances in Big Data and Deep Learning pp 370–379Cite as

Convolutional Neural Networks for Twitter Text Toxicity Analysis

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Part of the book series: Proceedings of the International Neural Networks Society ((INNS,volume 1))

Abstract

Toxic comment classification is an emerging research field with several studies that have address several tasks in the detection of unwanted messages on communication platforms. Although sentiment analysis is an accurate approach for observing the crowd behavior, it is incapable of discovering other types of information in text, such as toxicity, which can usually reveal hidden information. Towards this direction, a model for temporal tracking of comments toxicity is proposed using tweets related to the hashtag under study. More specifically, a classifier is trained for toxic comments prediction using a Convolutional Neural Network model. Next, given a hashtag all relevant tweets are parsed and used as input in the classifier, hence, the knowledge about toxic texts is transferred to a new dataset for categorization. In the meantime, an adapted change detection approach is applied for monitoring the toxicity trend changes over time within the hashtag tweets. Our experimental results showed that toxic comment classification on twitter conversations can reveal significant knowledge and changes in the toxicity are accurately identified over time.

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Notes

  1. 1.

    https://www.kaggle.com/c/jigsaw-toxic-comment-classification-challenge.

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Acknowledgment

We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan X Pascal GPU used for this research. This project has received funding from the Hellenic Foundation for Research and Innovation (HFRI) and the General Secretariat for Research and Technology (GSRT), under grant agreement No 1901.

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Authors and Affiliations

  1. Department of Computer Science and Biomedical Informatics, University of Thessaly, Lamia, Greece

    Spiros V. Georgakopoulos, Sotiris K. Tasoulis, Aristidis G. Vrahatis & Vassilis P. Plagianakos

Authors
  1. Spiros V. Georgakopoulos
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  2. Sotiris K. Tasoulis
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  3. Aristidis G. Vrahatis
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  4. Vassilis P. Plagianakos
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Corresponding author

Correspondence to Vassilis P. Plagianakos .

Editor information

Editors and Affiliations

  1. Department of Informatics, Bioengineering, Robotics, and Systems Engineering, University of Genova, Genoa, Italy

    Luca Oneto

  2. Department of Mathematics, University of Padova, Padua, Italy

    Nicolò Navarin

  3. Department of Mathematics, University of Padova, Padua, Italy

    Alessandro Sperduti

  4. Department of Informatics, Bioengineering, Robotics, and Systems Engineering, University of Genova, Genoa, Italy

    Davide Anguita

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Georgakopoulos, S.V., Tasoulis, S.K., Vrahatis, A.G., Plagianakos, V.P. (2020). Convolutional Neural Networks for Twitter Text Toxicity Analysis. In: Oneto, L., Navarin, N., Sperduti, A., Anguita, D. (eds) Recent Advances in Big Data and Deep Learning. INNSBDDL 2019. Proceedings of the International Neural Networks Society, vol 1. Springer, Cham. https://doi.org/10.1007/978-3-030-16841-4_38

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