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.

Twitter Analysis for Depression on Social Networks based on Sentiment and Stress Detecting words that express negativity in a social media message is one step towards detecting depressive moods. To understand if a Twitter user could exhibit depression over a period of time, we applied techniques in stages to discover words that are negative in expression. Existing methods either use a single step or a data subset, whereas we applied a multistep approach which allowed us to identify potential users and then discover the words that expressed negativity by these users. We address some Twitter specific characteristics in our research. One of which is that Twitter data can be very large, hence our desire to be able to process the data efficiently. The other is that due to its enforced character limitation, the style of writing makes interpreting and obtaining the semantic meaning of the words more challenging. Results show that the sentiment of these words can be obtained and scored efficiently as the computation on these dataset were narrowed to only these selected users. We also obtained the stress scores which correlated well with negative sentiment expressed in the content. This work shows that by first identifying users and then using methods to discover words can be a very effective technique.