Social Networks: Leveraging User Social Data to Empower Collective Intelligence

Online social networks such as Facebook, Twitter or LinkedIn have become extremely popular and ubiquitous. However, users are facing problems such as the disconnected nature of social Websites, user privacy issues and the huge amount of information available for browsing. Consequently, a considerable part of interesting information remains ignored by the users. This chapter presents a possible answer to this problem based on a new approach consisting of aggregating relevant information from social networks to empower the collective intelligence shared by a given group of users. The solution is built on a user-centered approach, the main benefit of which is that each user can delegate to the system the tasks of aggregating his/her scattered social data and extracting information relevant to the topics of interest of the group. Moreover, the user is provided with helpful features to make the best decisions for choosing the information he/she is ready to share.

[1]  Mor Naaman,et al.  Is it really about me?: message content in social awareness streams , 2010, CSCW '10.

[2]  Sang-Won Lee,et al.  On social Web sites , 2010, Inf. Syst..

[3]  Matthew K. O. Lee,et al.  Online social networks: Why do students use facebook? , 2011, Comput. Hum. Behav..

[4]  Kwan Hui Lim,et al.  Finding twitter communities with common interests using following links of celebrities , 2012, MSM '12.

[5]  Ahmad Abdel-Hafez,et al.  A Survey of User Modelling in Social Media Websites , 2013, Comput. Inf. Sci..

[6]  Yutaka Matsuo,et al.  Earthquake shakes Twitter users: real-time event detection by social sensors , 2010, WWW '10.

[7]  Qi He,et al.  TwitterRank: finding topic-sensitive influential twitterers , 2010, WSDM '10.

[8]  Federica Cena,et al.  User model interoperability: a survey , 2011, User Modeling and User-Adapted Interaction.

[9]  Antonino Nocera,et al.  Recommendation of similar users, resources and social networks in a Social Internetworking Scenario , 2011, Inf. Sci..

[10]  Lior Rokach,et al.  Facebook single and cross domain data for recommendation systems , 2013, User Modeling and User-Adapted Interaction.

[11]  Ilaria Torre,et al.  User data distributed on the social web: how to identify users on different social systems and collecting data about them , 2010, HetRec '10.

[12]  Kurt Rohloff,et al.  An Evaluation of Triple-Store Technologies for Large Data Stores , 2007, OTM Workshops.

[13]  Damon Horowitz,et al.  The anatomy of a large-scale social search engine , 2010, WWW '10.

[14]  Hosung Park,et al.  What is Twitter, a social network or a news media? , 2010, WWW '10.

[15]  Lawrence Birnbaum,et al.  Social media-driven news personalization , 2012, RSWeb@RecSys.

[16]  Julita Vassileva,et al.  SocConnect: A personalized social network aggregator and recommender , 2013, Inf. Process. Manag..

[17]  Johan Bollen,et al.  Twitter mood predicts the stock market , 2010, J. Comput. Sci..