Abstract
Sexual harassment is a serious social illness that has seeped into our digital societies as well. Research on sexual harassment in social media has been gaining popularity of late, due to the genuine long-term effects such harassment can have on an individual like depression, withdrawal, loss of self-confidence, self-destructive tendencies, and propagation of a conducive environment for sexual assault. The approaches to tackle this issue using technology range from simple lexicon-based models to sophisticated machine learning models. Lexicon-based systems utilize dictionaries that contain offensive words that are characteristic of sexual harassment. Rule-based approaches need to be supplemented with logical reasoning in order to define rules which can be matched with the text to distinguish sexual harassment. Machine learning approaches develop predictive models that can detect sexually offensive messages. This paper holistically studies approaches used to solve the problem of classification of sexual harassment in cyberspace and aims to determine the best directions for future research in this field. It also studies the methods of analyzing the social network in terms of the structure of ties between users that can be used to study specific user patterns and characteristics.
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Nagar, A.R., Bhat, M.R., Sneha Priya, K., Rajeshwari, K. (2021). A Holistic Study on Approaches to Prevent Sexual Harassment on Twitter. In: Joshi, A., Khosravy, M., Gupta, N. (eds) Machine Learning for Predictive Analysis. Lecture Notes in Networks and Systems, vol 141. Springer, Singapore. https://doi.org/10.1007/978-981-15-7106-0_8
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