Speakers
Title: On Content, Discussions, Opinions, and Deliberative Participation over Social Media Systems
Speaker: Dr. S. Felix Wu
Biographical Sketch: Prof. S. Felix Wu has been doing “experimental” system research, i.e., building prototype systems to justify and validate novel architectural concepts. Since 1995, he and his students/postdocs have built many experimental systems in the areas of fault tolerant network, IPSec/VPN security policy, attack source tracing, wireless network security, intrusion detection and response, visual information analytics, and, more recently, future Internet design. An article titled “Networking: Four ways to reinvent the Internet” published in Nature 463 (February 3rd, 2010, by Katharine Gammon) provided a brief but very nice cover about his primary thought on a Social-network-based future Internet architecture. During the past seven years, he has been pretending (and hoping) to know a little bit more about humanity science so he can claim that he is working on multidisciplinary research. He strongly believes that thoroughly considering the factor of human relationships is necessary for any IT innovation. Therefore, his primary research objective, before he retires, is to help and contribute to the information technology advancement that would truly help our human society. As an initial step, he recently released the SINCERE (Social Interactive Networking and Conversation Entropy Ranking Engine, sponsored by NSF) search engine under http://www.sincere.se, which is trying to help our Internet society to discover “interesting/unusual” discussions. Felix received his BS from Tunghai University, Taiwan, in 1985, both MS and PhD from Columbia University in 1989 and 1995, all in Computer Science. He has about 120+ academic publications, which means that he should probably focus much more on the depth and quality. He is currently a Professor with the Computer Science department at UC Davis.
Abstract: Social Media is changing many different aspects of our lives. By participating in online discussions, people exchange opinions on various topics or contents, shape their stances, and gradually build their own characteristics. In this talk, we will present a framework for identifying online user characteristics and understanding the formation of user deliberation and bias in online newsgroups. Under the SINCERE.se (Social Interactive Networks: Conversation Entropy Ranking Engine), we have designed a dynamic user like graph model to recognize user deliberation and bias automatically in online newsgroups. We evaluated our identi
cation results with linguistic features and implemented this model under SINCERE as a real-time service. By applying this model to large online newsgroups, we study the influence of early discussion context on the formation of user characteristics. Our conclusion is that the formation of user deliberation and bias is a product of situations, not simply dispositions: confronting disagreement in unfamiliar circumstances promotes more consideration of di erent opinions, while recurring conflict in familiar circumstances evokes close-minded behavior and bias. Based on this observation, we leveraged a supervised learning model to predict user deliberation and bias at their early life-stage. Our results show that knowing only the first three months of users’ interaction data generates an F1 accuracy level of around 70% in predicting user deliberation and bias in online newsgroups.
Title: System U: Computational Discovery of Personality Traits from Social Media to Deliver Hyper-Personalized Experience
Dr. Michelle Zhou
Biographical Sketch: Dr. Michelle Zhou is a Senior Research Manager at IBM Research – Almaden, where she manages the User Systems and Experience Research (USER) group. Zhou received a Ph.D. in Computer Science from Columbia University. Her expertise is in the interdisciplinary areas of intelligent user interaction, information visualization and smart visual analytics, and analytics-driven social computing and crowdsourcing. She has published over 70 peer-reviewed, refereed articles and filed over twenty patents in above areas. Zhou is active in several research communities, including Intelligent User Interfaces, Information Visualization and Visual Analytics, and Multimedia. She has co-organized/co-chaired conferences and workshops, and served on the technical program committees for key conferences in these areas. She is the general conference co-chair for ACM Recommender Systems 2014, was the general conference co-chair for ACM IUI 2007, and the technical program co-chair for ACM Multimedia 2009 and Intelligent User Interfaces 2010. Michelle has served on the editorial board of three journals, ACM Transactions on Multimedia Computing, Communications, Applications (TOMCCAP), ACM Transactions on Intelligent Systems and Technology (TIST), and ACM Transactions on Interactive Intelligent Systems. Zhou was named an ACM Distinguished Scientist in 2009.
Abstract: Hundreds of millions of people leave digital footprints in public (e.g., social media/social networking sites and review sites). We are developing System U, which uses psycholinguistic analytics to automatically derive one’s personality traits from their digital footprints. Such traits uniquely characterize an individual’s psychological, cognitive, and affective style and properties, and can then be used to make hyper-personalized recommendations to the individual and influence/intervene the actions of the individual. In this talk, I will give an overview of System U and describe how it automatically derives several types of personality traits from one’s tweets, including human basic value (one’s belief + motives) and fundamental needs (e.g., ideals vs. practical). Moreover, I will present a set of validation studies that assess how accurate the System U-derived traits are compared to “ground truth” and how these derived traits influence recommendations and people’s behavior in the real world. I will use live demos and concrete examples, ranging from precision marketing to individualized customer care, to demonstrate the applications of System U and discuss research directions in this space.
Title : Path to Mobile - A LinkedIn story
Biographical Sketch: Ganesh Srinivasan is Director of Engineering Mobile at LinkedIn - worlds largest professional network. He joined LinkedIn in 2011 and currently heads mobile development across all the phone platforms - Android, iOS and the mobile web. During this period, he has launched several key initiatives and guided the rapid mobile growth at LinkedIn. Prior to LinkedIn, Ganesh worked at Yahoo! in the messenger groups building the mobile presence for that product. He has worked on a wide range of projects spanning technologies in real time collaboration to e-commerce systems.
Abstract: Every social network has faced numerous challenges with the huge shift of traffic to mobile. How to design for this shift? What should be the system architecture? How to scale? How to measure success? These are some of the common questions that every social network has gone through. This session will walk you through LinkedIn’s story of transforming into a mobile social network and how it dealt with the questions raised above.
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