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Classification of cyberbullying messages using text, image and audio in social networks: a deep learning approach

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Abstract

People's physical presence in smart cities is being shifted to the cyber world via social networks. Bullying that occurs through virtual devices such as phones, laptops, and tablet and PCS is known as cyberbullying. Online harassment can occur via text messaging, messages, and software, as well as online in social networking sites, discussion boards, or online games where individuals may view, participate in, or distribute content, including sending, posting, or sharing negative, harmful, false, or mean content. It can include revealing personal or private details, causing embarrassment or humiliation. This paper intends to sift through social media posts in order to identify bullying comments in text, image, and video form.We propose a graph convolutional neural network(GCN) and a pre-trained Googlenet for text and image recognition, as well as a Mel-scale filter bank speech spectrogram and CNN network model for audio post classification. This study's main results are that using graph convolution neural networks and Googlenet, as well as audio post-processing using MFCC's, produce superior results including one dimensional representation.We use in the present environment,GCN and Mel-frequency cepstrum to represent text, image, and video input, yielding an accuracy of 96%. The approach is unique in its use of these techniques and has not been previously proposed for cyberbullying detection. The study's main contribution is its ability to achieve superior accuracy in identifying bullying comments, which makes it a valuable addition to the existing literature on cyberbullying detection.

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Data sharing is not applicable to this article as no datasets were generated or analyzed during the current study.

References

  1. Al-garadi MA, Varathan KD, Ravana SD (2016) Cybercrime detection in online communications: The experimental case of cyberbullying detection in the Twitter network, Comput Human Behav. https://doi.org/10.1016/j.chb.2016.05.051

  2. Alotaibi N, Alharthi M, Almazrouei M (2021) Detecting cyberbullying in social media using text and image analysis. Int Conf social computing and social media. https://doi.org/10.1007/978-3-030-79150-8_10

  3. Choi YJ, Jeon BJ, Kim HW (2020) Identification of key cyberbullies: A text mining and social network analysis approach. Telematics Inform 56:202. https://doi.org/10.1016/j.tele.101504

    Article  Google Scholar 

  4. Hang OC, Dahlan HM (2020) Cyberbullying Lexicon for Social Media. Int Conf Res Innov Inf Syst (ICRIIS). https://doi.org/10.1109/ICRIIS48246.2019.9073679

    Article  Google Scholar 

  5. Hani J, Nashaat M, Ahmed M, Emad Z, Amer E (2019) Social Media Cyberbullying Detection using Machine Learning. Int J Adv Comput Sci Appl (IJACSA), 10(5). https://doi.org/10.14569/IJACSA.2019.0100587

  6. Hassan M, Malik SA, Raza SA (2021) A Hybrid Method for Cyberbullying Detection on Social Media. Int J Adv Comput Sci Appl (IJACSA) 12(3):189–197. https://doi.org/10.14569/IJACSA.2021.0120323

    Article  Google Scholar 

  7. Huang Q, Singh KV, Atrey PK (2014) Cyber Bullying Detection Using Social and Textual Analysis. https://doi.org/10.1145/2661126.2661133.

  8. Kumari K, Prakash Singh J, Kumar Dwivedi Y, Rana N (2019) Towards Cyberbullying-free social media in smart cities: a unified multi-modal approach Soft Computing pp. 11059–11070. https://doi.org/10.1007/s00500-019-04550-x

  9. Li X, Jiang Y, Li H (2020) Cyberbullying Detection Using Graph Convolutional Networks in the journal IEEE Access. 8:72035–72043. https://doi.org/10.1109/ACCESS.2020.2998349 

  10. Lu N, Wu G, Zhang Z, Zheng Y, Ren Y, Choo KKR (2020) Cyberbullying detection in social media text based on character-level convolutional neural network with shortcuts. https://doi.org/10.1002/cpe.5627

  11. Ma CL, Zhang Y, Wang H (2022) A Deep Learning Approach for Cyberbullying Detection on Instagram,Expert systems with applications. https://doi.org/10.1016/j.eswa.2021.115371

  12. Modha S, Majumder P, Mandl T, Mandalia C (2020) Detecting and visualizing hate speech in social media: A cyber Watchdog for surveillance. Expert Syst Appl 161. https://doi.org/10.1016/j.eswa.2020.113725

  13. Nurrahmi H, Nurjanah D (2018) Indonesian Twitter Cyberbullying Detection using Text Classification and User Credibility, Int Conf Inf Commun Technol (ICOIACT). https://doi.org/10.1109/ICOIACT.8350758

  14. Sadiq S, Mehmood A, Ullah S, Ahmad M, Choi GS, On BW (2021) Aggression detection through deep neural model on Twitter. Future Gener Comput Syst 114:120–129. https://doi.org/10.1016/j.future.2020.07.050

    Article  Google Scholar 

  15. Sangwan SR, Bahita MPS (2020) Denigration Bullying Resolution using Wolf Search Optimized Online Reputation Rumour Detection. Procedia Comput Sci 173:305–314. https://doi.org/10.1016/j.procs.2020.06.036

  16. Shandilya S, Izonin, Kumar Shandilya S, Singh KK (2022) Mathematical modeling of bio-inspired frog leap optimization algorithm for transmission expansion planning. Math Biosci Eng 19(7):7232–7247. https://doi.org/10.3934/mbc.2022341

  17. Singh JP, Kumari K (2020) Identification of cyberbullying on multi-modal social media posts using genetic algorithm”. Emerg Telecommun Technol. https://doi.org/10.1002/ett.3907

    Article  Google Scholar 

  18. Srivastava A, Gupta R, Kumar A (2020) A machine learning approach for cyberbullying detection on social media. J Ambient Intell Humaniz Comput 11(8):3483–3497. https://doi.org/10.1007/s12652-020-02622-6

    Article  Google Scholar 

  19. Van Hee C, Jacobs G, Emmery C, Desmet B, Lefever E, Verhoeven B, De Pauw G (2018) Automatic detection of cyberbullying in social media text. PLoS ONE 13(10):e020379. https://doi.org/10.1371/journal.pone.0203794

    Article  Google Scholar 

  20. Yadav J, Kumar D, Chauhan D (2020) Cyberbullying Detection using Pre-Trained BERT Model, IntConf Electron Sustain Commun Syst (ICESC). https://doi.org/10.1109/ICESC48915.2020.9155700

  21. Zhao Z, Gao M, Luo F, Zhang Yi, Xiong Q (2020) LSHWE: Improving Similarity-Based Word Embedding with Locality Sensitive Hashing for Cyberbullying Detection. Int Joint Conf Neural Netw (IJCNN). https://doi.org/10.1109/IJCNN48605.2020.9207640

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Information Technology, Sona College of Technology, Salem, Tamil Nadu, India

    Sangeethapriya R & Akilandeswari J

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  1. Sangeethapriya R
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  2. Akilandeswari J
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Correspondence to Sangeethapriya R.

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R, S., J, A. Classification of cyberbullying messages using text, image and audio in social networks: a deep learning approach. Multimed Tools Appl 83, 2237–2266 (2024). https://doi.org/10.1007/s11042-023-15538-z

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  • DOI: https://doi.org/10.1007/s11042-023-15538-z

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