Browsing by Author "Pawar, Rohit"

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    Cyberbullying Detection System with Multiple Server Configurations
    (IEEE, 2018-05) Pawar, Rohit; Agrawal, Yash; Joshi, Akshay; Gorrepati, Ranadheer; Raje, Rajeev R.; Computer and Information Science, School of Science
    Due to the proliferation of online networking, friendships and relationships - social communications have reached a whole new level. As a result of this scenario, there is an increasing evidence that social applications are frequently used for bullying. State-of-the-art studies in cyberbullying detection have mainly focused on the content of the conversations while largely ignoring the users involved in cyberbullying. To encounter this problem, we have designed a distributed cyberbullying detection system that will detect bullying messages and drop them before they are sent to the intended receiver. A prototype has been created using the principles of NLP, Machine Learning and Distributed Systems. Preliminary studies conducted with it, indicate a strong promise of our approach.
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    Multilingual Cyberbullying Detection System
    (IEEE, 2019-05) Pawar, Rohit; Raje, Rajeev R.; Computer and Information Science, School of Science
    As the use of social media has evolved in recent times, so has the ability to cyberbully victims using it. The last decade has witnessed a surge of cyberbullying-this bullying is not only limited to English but also happens in other languages. A large number of mobile device users are in Asian countries such as India. Such a large audience is a fertile ground for cyberbullies -hence, it is very important to detect cyberbullying in multiple languages. Most of the current approaches to identify cyberbullying are focused on English text, and a very few approaches are venturing into other languages. This paper proposes a Multilingual Cyberbullying Detection System for detection of cyberbullying in two Indian languages- Hindi and Marathi. We have developed a prototype that operates across data sets created for these two languages. Using this prototype, we have carried out experiments to detect cyberbullying in these two languages. The results of our experiments show an accuracy up-to 97% and Fl-score up-to 96% on many datasets for both the languages.