Cyberbullying is a serious threat to both the short and long-term well-being of social media users. Addressing this problem in online environments demands the ability to automatically detect cyberbullying and to identify the roles that participants assume in social interactions. As cyberbullying occurs within online communities, it is also vital to understand the group dynamics that support bullying behavior. To this end, we propose a socio-linguistic model which jointly detects cyberbullying content in messages, discovers latent text categories, identifies participant roles and exploits social interactions. While our method makes use of content that is labeled as bullying, it does not require category, role or relationship labels. Furthermore, as bullying labels are often subjective, noisy and inconsistent, an important contribution of our paper is effective methods for leveraging inconsistent labels. Rather than discard inconsistent labels, we evaluate different methods for learning from them, demonstrating that incorporating uncertainty allows for better generalization. Our proposed socio-linguistic model achieves an 18% improvement over state-of-the-art methods.

We will demonstrate a conversational products recommendation agent. This system shows how we combine research in personalized recommendation systems with research in dialogue systems to build a virtual sales agent. Based on new deep learning technologies we developed, the virtual agent is capable of learning how to interact with users, how to answer user questions, what is the next question to ask, and what to recommend when chatting with a human user. Normally a descent conversational agent for a particular domain requires tens of thousands of hand labeled conversational data or hand written rules. This is a major barrier when launching a conversation agent for a new domain. We will explore and demonstrate the effectiveness of the learning solution even when there is no hand written rules or hand labeled training data.

As the increasing of popularity of social web, cyber bullying has become a more and more serious issue among children. Bullying causes huge negative effects on children, even suicide. SocialFilter is a realtime system that helps parents and educators track children's messages on Twitter, especially in order to detect whether they have been bullied or bullying others. The aim of the system is 4 I's, identity of bullies, inference of bullying message, influence of bully behavior, and intervention. We solve this problem by using machine learning technique. The current system is tracking tens of thousands of active children users on Twitter and automatically detect bullying messages at real time.

As highly structured documents with rich metadata (such as products, movies, etc.) become increasingly prevalent, searching those documents has become an important IR problem. Unfortunately existing work on document summarization, especially in the context of search, has been mainly focused on unstructured documents, and little attention has been paid to highly structured documents. Due to the different characteristics of structured and unstructured documents, the ideal approaches for document summarization might be different. In this paper, we study the problem of summarizing highly structured documents in a search context. We propose a new summarization approach based on query-specific facet selection. Our approach aims to discover the important facets hidden behind a query using a machine learning approach, and summarizes retrieved documents based on those important facets. In addition, we propose to evaluate summarization approaches based on a utility function that measures how well the summaries assist users in interacting with the search results. Furthermore, we develop a game on Mechanical Turk to evaluate different summarization approaches. The experimental results show that the new summarization approach significantly outperforms two existing ones.