Abstract
Online sexual harassment is defined as unwanted sexual conduct on any digital platform and it is recognised as a form of sexual violence which can make a person feel threatened, exploited, coerced, humiliated, upset, sexualised or discriminated against. With our work we contend that such content can be leveraged to create models that could automatically detect such malicious online behaviour and thereby, ban such users from posting such content in the future, without having to wait for the other users to report them and thereby, create a safe space on social media. A major attribute of our proposed model is that it focuses on how sexual harassment can be hard to classify on social media. These spaces, unlike a formal environment, have no rigid set of rules or code of conduct to adhere to and therefore it can be very difficult to draw the line between a joke and a more malicious comment. To be able to discern the differences between such analogous statements we must have a model which can read and understand the context clues to better classify. In our paper we have worked with state-of-the-art Machine Learning and Deep Learning models and conducted extensive comparison to find the most effective model to better realise this vision of fair space by achieving the most accurate predictions.
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Basu, P., Singha Roy, T., Tiwari, S., Mehta, S. (2021). CyberPolice: Classification of Cyber Sexual Harassment. In: Marreiros, G., Melo, F.S., Lau, N., Lopes Cardoso, H., Reis, L.P. (eds) Progress in Artificial Intelligence. EPIA 2021. Lecture Notes in Computer Science(), vol 12981. Springer, Cham. https://doi.org/10.1007/978-3-030-86230-5_55
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