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Performance Analysis of Various Classifiers for Social Intimidating Activities Detection

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Advances in Computing and Data Sciences (ICACDS 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1440))

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Abstract

The emergence of social networks is at a great boom today. Every big news before telecasting on television comes to these forums, therefore raises many dilemmas due to misinterpretation regarding the freedom of speaking. One of this trouble is social intimidation that is very disturbing misbehavior that can cause troubling consequences for the victim. Existing works of social intimidation focuses on only one or two topics of harassment. The main aim of this study is to analyze the hub of social intimidation i.e. twitter, consisting of 25,000 tweets covering five topics of harassment i.e. sexism, racism, appearance related, political and intellectual. Moreover, five machine learning and four deep learning techniques were used namely sequential minimal optimization (SMO), random forest, multinomial naïve bayes, logistic regression (LR), decision tree J48, CNN-CB, CNN-GRU, CNN-LRCN and CNN-Bi-LSTM. Each of the classifiers are evaluated using accuracy, precision, recall and f-measure as a performance metric on the dataset. Results indicate the dominance of CNN-Bi-LSTM and logistic regression among all classifiers used.

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References

  1. Patchin, J.W., Hinduja, S.: It is time to teach safe sexting. J. Adolesc. Health 66(2), 140–143 (2020)

    Article  Google Scholar 

  2. Patchin, J.W., Hinduja, S.: Sextortion among adolescents: results from a national survey of US youth. Sex. Abuse 32(1), 30–54 (2020)

    Article  Google Scholar 

  3. Samghabadi, N.S., Monroy, A.P.L., Solorio, T.: Detecting early signs of cyberbullying in social media. In: Proceedings of the Second Workshop on Trolling, Aggression and Cyberbullying, pp. 144–149 (2020)

    Google Scholar 

  4. Galán-García, P., Puerta, J.G.D.L., Gómez, C.L., Santos, I., Bringas, P.G.: Supervised machine learning for the detection of troll profiles in twitter social network: application to a real case of cyberbullying. Log. J. IGPL 24(1), 42–53 (2016)

    MathSciNet  Google Scholar 

  5. Sheeba, J.I., Devaneyan, S.P.: Cyberbully detection using intelligent techniques. Int. J. Data Min. Emerg. Technol. 2(2), 86–94 (2016)

    Article  Google Scholar 

  6. Balakrishnan, V., Khan, S., Arabnia, H.R.: Improving cyberbullying detection using Twitter users’ psychological features and machine learning. Comput. Secur. 90, 101710 (2020)

    Article  Google Scholar 

  7. Abbass, Z., Ali, Z., Ali, M., Akbar, B., Saleem, A.: A framework to predict social crime through Twitter tweets by using machine learning. In: 2020 IEEE 14th International Conference on Semantic Computing (ICSC), pp. 363–368. IEEE (2020)

    Google Scholar 

  8. Birjali, M., Beni-Hssane, A., Erritali, M.: Machine learning and semantic sentiment analysis based algorithms for suicide sentiment prediction in social networks. Procedia Comput. Sci. 113, 65–72 (2017)

    Article  Google Scholar 

  9. Frommholz, I., Al-Khateeb, H.M., Potthast, M., Ghasem, Z., Shukla, M., Short, E.: On textual analysis and machine learning for cyberstalking detection. Datenbank-Spektrum 16(2), 127–135 (2016)

    Article  Google Scholar 

  10. García-Díaz, J.A., Cánovas-García, M., Colomo-Palacios, R., Valencia-García, R.: Detecting misogyny in Spanish tweets. An approach based on linguistics features and word embeddings. Future Gen. Comput. Syst. 114, 506–518 (2021)

    Article  Google Scholar 

  11. Salminen, J., Hopf, M., Chowdhury, S.A., Jung, S.-G., Almerekhi, H., Jansen, B.J.: Developing an online hate classifier for multiple social media platforms. HCIS 10(1), 1–34 (2020). https://doi.org/10.1186/s13673-019-0205-6

    Article  Google Scholar 

  12. Drishya, S.V., Saranya, S., Sheeba, J.I., Devaneyan, S.P.: Cyberbully image and text detection using convolutional neural networks. CiiT Int. J. Fuzzy Syst. 11(2), 25–30 (2019)

    Google Scholar 

  13. Gambäck, B., Sikdar, U.K.: Using convolutional neural networks to classify hate-speech. In: Proceedings of the First Workshop on Abusive Language Online, pp. 85–90 (2017)

    Google Scholar 

  14. Bu, S.J., Cho, S.B.: A hybrid deep learning system of CNN and LRCN to detect cyberbullying from SNS comments. In: de Cos Juez, F. et al. (eds.) Hybrid Artificial Intelligent Systems. HAIS 2018. LNCS, vol .10870, pp. 561–572. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-92639-1_47

    Chapter  Google Scholar 

  15. Zhang, Z., Robinson, D., Tepper, J.: Detecting hate speech on twitter using a convolution-gru based deep neural network. In: Gangemi, A., et al. (eds.) ESWC 2018. LNCS, vol. 10843, pp. 745–760. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-93417-4_48

    Chapter  Google Scholar 

  16. Sadiq, S., Mehmood, A., Ullah, S., Ahmad, M., Choi, G.S., On, B.W.: Aggression detection through deep neural models on twitter. Futur. Gener. Comput. Syst. 114, 120–129 (2021)

    Article  Google Scholar 

  17. Al-Ajlan, M.A., Ykhlef, M.: Deep learning algorithm for cyberbullying detection. Int. J. Adv. Comput. Sci. Appl. 9(9), 199–205 (2018)

    Google Scholar 

  18. Cheng, L., Shu, K., Wu, S., Silva, Y.N., Hall, D.L., Liu, H.: Unsupervised cyberbullying detection via time-informed gaussian mixture model. ArXiv preprint arXiv: 2008.02642 (2020)

    Google Scholar 

  19. Zinovyeva, E., Härdle, W.K., Lessmann, S.: Antisocial online behavior detection using deep learning. Decis. Support Syst. 138, 113362 (2020)

    Article  Google Scholar 

  20. Rezvan, M., Shekarpour, S., Balasuriya, L., Thirunarayan, K., Shalin, V.L., Sheth, A.: A quality type-aware annotated corpus and lexicon for harassment research. In: Proceedings of the 10th ACM Conference on Web Science, pp. 33–36 (2018)

    Google Scholar 

  21. Garg, A., Gupta, A., Gowda, D., Singh, S., Kim, C.: Hierarchical multi-stage word-to-grapheme named entity corrector for automatic speech recognition. In: Proceedings of Interspeech, vol. 2020, pp. 1793–1797 (2020)

    Google Scholar 

  22. Mageshwari, V., Aroquiaraj, L.: An efficient feature extraction method for mining social media. Int. J. Sci. Technol. Res. 8(11), 640–643 (2019)

    Google Scholar 

  23. Hinduja, S., Patchin, J.W.: Cyberbullying: an exploratory analysis of factors related to offending and victimization. Deviant Behav. 29, 129–156 (2008)

    Article  Google Scholar 

  24. Altay, E.V., Alatas, B.: Detection of cyberbullying in social networks using machine learning methods. In: 2018 International Congress on Big Data, Deep Learning and Fighting Cyber Terrorism (IBIGDELFT), pp. 87–91. IEEE (2018)

    Google Scholar 

  25. Raza, M.O., Memon, M., Bhatti, S., Bux, R.: Detecting cyberbullying in social commentary using supervised machine learning. In: Arai, K., Kapoor, S., Bhatia, R. (eds.) FICC 2020. AISC, vol. 1130, pp. 621–630. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-39442-4_45

    Chapter  Google Scholar 

  26. Pawar, R., Raje, R.R.: Multilingual cyberbullying detection system. In: 2019 IEEE International Conference on Electro Information Technology (EIT), pp. 040–044. IEEE (2019)

    Google Scholar 

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Amity University Uttar Pradesh, Sec-125, Noida, UP, India

    Mansi Mahendru & Sanjay Kumar Dubey

Authors
  1. Mansi Mahendru
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  2. Sanjay Kumar Dubey
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Corresponding author

Correspondence to Sanjay Kumar Dubey .

Editor information

Editors and Affiliations

  1. University of KwaZulu-Natal, Durban, South Africa

    Mayank Singh

  2. Jaypee University of Engineering and Technology, Guna, Madhya Pradesh, India

    Vipin Tyagi

  3. Jaypee University of Information Technology, Waknaghat, Himachal Pradesh, India

    P. K. Gupta

  4. Institute of Information Theory and Automation, Prague, Czech Republic

    Jan Flusser

  5. University of Ottawa, Ottawa, ON, Canada

    Tuncer Ören

  6. MVPS's Karmaveer Adv. Baburao Ganpatrao Thakare College of Engineering, Nashik, Maharashtra, India

    V. R. Sonawane

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Mahendru, M., Dubey, S.K. (2021). Performance Analysis of Various Classifiers for Social Intimidating Activities Detection. In: Singh, M., Tyagi, V., Gupta, P.K., Flusser, J., Ören, T., Sonawane, V.R. (eds) Advances in Computing and Data Sciences. ICACDS 2021. Communications in Computer and Information Science, vol 1440. Springer, Cham. https://doi.org/10.1007/978-3-030-81462-5_46

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  • DOI: https://doi.org/10.1007/978-3-030-81462-5_46

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-81461-8

  • Online ISBN: 978-3-030-81462-5

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