SocInfo2013 The 5th International Conference on Social Informatics (SocInfo2013), 25–27 November 2013. SocInfo2013 The 5th International Conference on Social Informatics (SocInfo2013), 25–27 November 2013. SocInfo2013 The 5th International Conference on Social Informatics (SocInfo2013), 25–27 November 2013. SocInfo2013 The 5th International Conference on Social Informatics (SocInfo2013), 25–27 November 2013. SocInfo2013 The 5th International Conference on Social Informatics (SocInfo2013), 25–27 November 2013. SocInfo2013 The 5th International Conference on Social Informatics (SocInfo2013), 25–27 November 2013. SocInfo2013 The 5th International Conference on Social Informatics (SocInfo2013), 25–27 November 2013.
Home » Program » Keynotes


Exposure to Political Diversity Online: Measurement and Interventions

Paul Resnick
University of Michigan School of Information


Selective Exposure theory dating back more than a half century posits that, when given a choice, people prefer to be exposed to reinforcing political opinions and to avoid challenging opinions. Political theorists and popular pundits have raised alarms about the impact of such preferences on political polarization and polarization’s negative impact on democratic governance. Online, it has become possible to measure actual exposure rather than preferences or self-reported exposure. I will review recent findings about levels of exposure to reinforcing and challenging opinions in news articles, and the effectiveness of interface elements and recommender systems that nudge people toward more balanced exposure.

About the speaker

Professor Paul ResnickPaul Resnick is a Professor at the University of Michigan School of Information. He previously worked as a researcher at AT&T Labs and AT&T Bell Labs, and as an Assistant Professor at the MIT Sloan School of Management. He received the master’s and Ph.D. degrees in Electrical Engineering and Computer Science from MIT, and a bachelor’s degree in mathematics from the University of Michigan.

Professor Resnick’s research focuses on SocioTechnical Capital, productive social relations that are enabled by the ongoing use of information and communication technology. His current projects include making recommender systems resistant to manipulation through rater reputations, nudging people toward politically balanced news consumption and health behavior change, and crowdsourcing fact-correction on the Internet.

Resnick was a pioneer in the field of recommender systems (sometimes called collaborative filtering or social filtering). Recommender systems guide people to interesting materials based on recommendations from other people.  The GroupLens system he helped develop was awarded the 2010 ACM Software Systems Award. His articles have appeared in Scientific American, Wired, Communications of the ACM, The American Economic Review, Management Science, and many other venues.  He has a forthcoming MIT Press book (co-authored with Robert Kraut), titled “Building Successful Online Communities: Evidence-based Social Design”.

Trends in the Japanese Information Behavior over the Past 15 Years

Yoshiaki Hashimoto
Interfaculty Initiative in Information Studies
The University of Tokyo


As witnessed in the US, in China or in various countries across the globe, a tremendous mutation is presently taking place in Japan concerning the media environment and the information behavior. The time people are exposed to the mass media, such as the TV and newspapers, has significantly diminished, while the time allocated to internet use increased. For instance, according to our surveys, in the year 2000 young people in their 20s daily watched TV for 177.0 min., but in 2012 this figure dwindled to 120.2 min.. On the other hand, the time allotted to internet use via PCs at home rose from 13.6 min. in 2000 to 28.8 min. in 2012. In addition, 73.2 min. per day were spent on the usage of the internet via mobile phones (smartphones included) in the latter year. In Japan young people tend to frequently access internet with their mobile phones rather than PCs.
The exposure time length between traditional media and the internet has certainly been reversed as indicated supra. However a close analysis of the Japanese people’s information behavior reveals that a number of careful considerations are to be made. First of all, the assertion according to which a phenomenon of media cannibalism (encroachment) is occurring between TV and the internet doesn’t necessarily and simply hold. Depending on the length of time spent home, Japanese people allocate their time to various media uses, and it is true that the ratio of time devoted to the use of internet is gradually increasing, but this doesn’t simply mean that people are switching their TV watching time to internet usage.
Besides, as to the function of the media in Japan, the role played by the TV and newspapers has nevertheless remained important. For instance, most people received information about the last July’s Upper House election through the TV and newspapers and found it reliable, while the access to elections information via internet was rather limited. This happened in spite of the deregulation on the use of the internet by political parties and candidates from this election on. This significantly differs from the situation elsewhere, at least in the United States. Also, regarding information sources in general, the TV and newspapers still enjoy strong credibility. This is because for so long in Japan TVs and newspapers have been standardized all across the nation. They provide almost the same information. There is practically no information diversity, which guarantees a certain sense of stability. NHK and the Asahi Shimbun constitute particularly very authoritative media. Finally, regarding the user’s involvement, participation, and transparency in the creation process, the internet is certainly more and more playing a vital role in the formation of culture. However, the established traditional system of moving images content, literature and so forth remained so authoritative in Japan that the uniqueness of the internet has not yet been fully explored.

About the speaker

Prof. HashimotoYoshiaki HASHIMOTO is a professor at the Interfaculty Initiative in Information Studies, the University of Tokyo. He received a bachelor’s degree in psychology and a master’s degree in sociology from the University of Tokyo. Professor Hashimoto is a specialist in the research of communications. He is particularly interested in media use activities. Supported by solid empirical data, his research examines the trends and changes in the Japanese information behavior. Without interruption, from 1995 to 2013, he has conducted numerous questionnaire and time-budget surveys. Utilizing the random sampling method, each of these nationwide surveys covered a sample size ranging between 1,500 and 2,000 respondents. The “WHITE PAPER on Information and Communications in Japan” annually issued under the auspices of the Ministry of Internal Affairs and Communication(MIC) also regularly shows the results of the surveys. Professor Hashimoto has also conducted a MIC funded research on Internet addiction based on a socio-psychological approach. His recent publications are “Information Behavior in Japan 2010″, “Media Communication Studies”, “Age of Neo-Digital Natives”, “Media and the Japanese: Changing Ordinary Life”, and so forth.

Social Computing in the Era of Big Data with Applications in Social and Location Recommendation

Irwin King
Chinese University of Hong Kong
Web Intelligence & Social Computing Lab


The Big Data Era has ushered in a new wave of research that investigates how we can better handle data with characteristics such as high volume, velocity, veracity, and variety.  Social Computing examines the collective intelligent behavior resulted from interactions among social entities.  In the first part of the talk, I plan to draw some observations on the interplay between Social Computing and Big Data.  I will then focus on our recent work on social and location recommendations based on matrix factorization framework as a case study that demonstrates how filtered suggestions are highly desirable to cope with the information explosion problem.  I will outline novel ways on how we can use social ensemble, trust relations, tags, click-through-rate, etc. to improve social and location recommender systems for a wide-range of applications and services in the era of Big Data.

About the speaker

Prof. KingProf. Irwin King’s research interests include machine learning, social computing, web intelligence, data mining, big data, and multimedia information processing. In these research areas, he has over 200 technical publications in journals and conferences. In addition, he has contributed over 30 book chapters and edited volumes.  Prof. King is the Book Series Editor for “Social Media and Social Computing” with Taylor and Francis (CRC Press). He is also an Associate Editor of the ACM Transactions on Knowledge Discovery from Data (ACM TKDD), Journal of Neural Networks, and a former Associate Editor of the IEEE Transactions on Neural Networks (TNN). He is a member of a number of Editorial Boards of international journals. He is also a member of the Board of Governors of INNS and a Vice-President and Governing Board Member of APNNA. He also serves INNS as the Vice-President for Membership in the Board of Governors.

Prof. King is Associate Dean (Education) at the Engineering Faculty and Professor at the Department of Computer Science and Engineering, The Chinese University of Hong Kong.  Recently, he was on leave with AT&T Labs Research, San Francisco and also taught Social Computing and Data Mining as a Visiting Professor at UC Berkeley.  He received his B.Sc. degree in Engineering and Applied Science from California Institute of Technology, Pasadena and his M.Sc. and Ph.D. degree in Computer Science from the University of Southern California, Los Angeles.