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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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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