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Cyberbullying is a phenomenon which negatively affects the individuals, the victims suffer from various mental issues, ranging from depression, loneliness, anxiety to low self-esteem. In parallel with the pervasive use of social media, cyberbullying is becoming more and more prevalent. Traditional mechanisms to fight against cyberbullying include the use of standards and guidelines, human moderators, and blacklists based on the profane words. However, these mechanisms fall short in social media and cannot scale well. Therefore, it is necessary to develop a principled learning framework to automatically detect cyberbullying behaviors. However, it is a challenging task due to short, noisy and unstructured content information and intentional obfuscation of the abusive words or phrases by social media users. Motivated by sociological and psychological findings on bullying behaviors and the correlation with emotions, we propose to leverage sentiment information to detect cyberbullying behaviors in social media by proposing a sentiment informed cyberbullying detection framework. Experimental results on two real-world, publicly available social media datasets show the superiority of the proposed framework. Further studies validate the effectiveness of leveraging sentiment information for cyberbullying detection.

Social networking or social media sites are involvedin our daily life. With the increasing popularity of socialmedia sites like Twitter and Facebook, people are using theirsocial network to find answers to their questions. Not everysocial media site can answer a user's question. Different socialmedia sites have different strength such as the StackOverflowcommunity has specific interest in programming and softwaredevelopment, whereas the TripAdvisor community has specificinterest in topic of Travel. Hence, we need a framework toidentify social media sites, which can best answer user's questions. In this paper, we demonstrate system named SocialAnswer which can rank social media sites for user's querybased on the content similarity of social media site and user'squery using ensemble of search engine query results.