Identification and Classification of Cybercrimes using Text Mining Technique Cybercrimes involve all the crimes where internet is used as an access medium and committed through some electronic device such as computers and mobile phones. Unavailability of datasets, hidden identity of predators and the privacy of the victims are the main factors for limiting the past research in cyberbullying detection. Considering these factors, an effective text mining approach using machine learning algorithms is proposed to proactively detect bullying text. The dataset collected from myspace.com and PrevertedJustice.com has been used to evaluate the systemu0027s performance. Three types of feature namely textual, behavioral and demographic features are extracted from the dataset as compared to earlier study over the same dataset where only textual features were considered. Textual features include certain bullying words that if exists within the text may lead to a true outcome for cyberbullying. Personality trait features are extracted for the user if it is involving once in bullying may bully in future too. While demographic features extracted from dataset include age, gender and location. The system is evaluated through different performance measures for both classifiers used and the performance of Support Vector Machine classifier is found better than the Bernoulli NB with an overall 87.14% of classification accuracy.

Detecting Cyberbullying and Cyberaggression in Social Media Cyberbullying and cyberaggression are increasingly worrisome phenomena affecting people across all demographics. More than half of young social media users worldwide have been exposed to such prolonged andor coordinated digital harassment. Victims can experience a wide range of emotions, with negative consequences such as embarrassment, depression, isolation from other community members, which embed the risk to lead to even more critical consequences, such as suicide attempts.  :66,this work, we take the first concrete steps to understand the characteristics of abusive behavior in Twitter, one of todayu0027s largest social media platforms. We analyze 1.2 million users and 2.1 million tweets, comparing users participating in discussions around seemingly normal topics like the NBA, to those more likely to be haterelated, such as the Gamergate controversy, or the gender pay inequality at the BBC station. We also explore specific manifestations of abusive behavior, i.e., cyberbullying and cyberaggression, in one of the haterelated communities (Gamergate). We present a robust methodology to distinguish bullies and aggressors from normal Twitter users by considering text, user, and networkbased attributes. Using various stateoftheart machine learning algorithms, we classify these accounts with over 90% accuracy and AUC. Finally, we discuss the current status of Twitter user accounts marked as abusive by our methodology, and study the performance of potential mechanisms that can be used by Twitter to suspend users in the future.