Browsing by Subject "cyberbullying"

Now showing 1 - 3 of 3
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    Collaborative detection of cyberbullying behavior in Twitter data
    (2017) Mangaonkar, Amrita; Raje, Rajeev
    As the size of Twitter© data is increasing, so are undesirable behaviors of its users. One such undesirable behavior is cyberbullying, which could lead to catastrophic consequences. Hence, it is critical to efficiently detect cyberbullying behavior by analyzing tweets, in real-time if possible. Prevalent approaches to identifying cyberbullying are mainly stand-alone, and thus, are time-consuming. This thesis proposes a new approach called distributed-collaborative approach for cyberbullying detection. It contains a network of detection nodes, each of which is independent and capable of classifying tweets it receives. These detection nodes collaborate with each other in case they need help in classifying a given tweet. The study empirically evaluates various collaborative patterns, and it assesses the performance of each pattern in detail. Results indicate an improvement in recall and precision of the detection mechanism over the stand- alone paradigm. Further, this research analyzes scalability of the approach by increasing the number of nodes in the network. The empirical results obtained from experimentation show that the system is scalable. The study performed also incorporates the experiments that analyze behavior distributed-collaborative approach in case of failures in the system. Additionally, the proposed thesis tests this approach on a different domain, such as politics, to explore the possibility of the generalization of results.
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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.