Collaborative detection of cyberbullying behavior in Twitter data

Abstract

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.