Abstract
Online social media bullying has become extremely detrimental in recent times with the proliferation of smartphones and the wide popularity of social networks among people. The number of people being victimized as a result of cyberbullying is increasing day-by-day and the researchers are continuously thriving to develop new techniques to detect online social media bullying in order to curb this serious social menace. Current research in this domain focuses on the detection of cyberbullying and only a few works have addressed the problem on how to prevent cyberbullying before it occurs. Considering this aspect, our work mainly concentrates on the prevention of online bullying utilizing the concepts of Cognitive psychology and Intent analysis. We propose a cognitive psychological approach inspired by the Theory of Planned Behavior to understand the psychological factors in a person prompting him/her to perform online bullying and prevent him/her from posting bully comments. Initially, we use sentiment analysis to determine whether the content posted by a user includes bully comments and subsequently Intent analysis is carried out to identify the intention hidden in the text posted by the user and warns him/her about the future consequences if that message contains any negative intention to harm others. The classification of the text data into bully and non-bully comments is employed using different machine learning algorithms of which Naïve Bayes yielded the best result. Later on, cognitive psychology is applied to understand the intention of a user posting or sharing a bully text and thereby diverting his attempt to perform online bullying.
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Mohan, S., Valsaladevi, I., Thampi, S.M. (2019). “Think Before You Post”: A Cognitive Psychological Approach for Limiting Bullying in Social Media. In: Wang, G., El Saddik, A., Lai, X., Martinez Perez, G., Choo, KK. (eds) Smart City and Informatization. iSCI 2019. Communications in Computer and Information Science, vol 1122. Springer, Singapore. https://doi.org/10.1007/978-981-15-1301-5_33
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DOI: https://doi.org/10.1007/978-981-15-1301-5_33
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