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HATDO: hybrid Archimedes Tasmanian devil optimization CNN for classifying offensive comments and non-offensive comments

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A Correction to this article was published on 15 March 2024

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Abstract

The evolution of social media and online platforms created more opportunities to express one’s thoughts without any burden. This emergence paved the way to construct more intelligent systems but by taking advantage of these platforms some people display their vengeance by harassing or abusing comments. The attackers leak sensitive information about an individual or an organization through the internet using abusive words, hate comments, threatening information, fake news, etc. So, online shaming detection with an optimum classification mechanism becomes necessary to avoid such negative consequences. In this paper, a novel online shaming detection mechanism using a Convolutional Neural Network Bidirectional Gated Recurrent Unit-based Hybrid Archimedes Tasmanian Devil (CNNBiGRU-HATD) approach is proposed to detect and classify the offensive comments and non-offensive comments. If the comment seems offensive, it is further classified into six categories according to shaming types such as abusive, doxing, Passing Judgment, Ethnic/Religious, Joke/Sarcasm, and Whataboutery. The utilized four Twitter-based datasets are initially preprocessed and feature extracted to improve the classification performance of the CNNBiGRU-HATD approach. After accurate classification, the information from the classified shaming types is collected and authenticated. The proposed approach is simulated using the Python platform. To validate the potential capability of the proposed CNNBiGRU-HATD approach, it is related to the existing methods by various parameters. The experimental result reveals that the proposed CNNBiGRU-HATD method achieves higher accuracy of 96.5% for the labeled hate speech detection dataset.

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Data availability

The data that support the findings of this study are available from the corresponding author upon reasonable request.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, SRM Institute of Science and Technology, Ramapuram, Chennai, Tamil Nadu, India

    B. Aarthi & Balika J. Chelliah

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  1. B. Aarthi
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  2. Balika J. Chelliah
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All authors agreed on the content of the study. AB & BJC collected all the data for analysis. AB & BJC agreed on the methodology. AB & BJC completed the analysis based on agreed steps. Results and conclusions are discussed and written together. All authors read and approved the final manuscript.

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Correspondence to B. Aarthi.

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Aarthi, B., Chelliah, B.J. HATDO: hybrid Archimedes Tasmanian devil optimization CNN for classifying offensive comments and non-offensive comments. Neural Comput & Applic 35, 18395–18415 (2023). https://doi.org/10.1007/s00521-023-08657-z

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