Title :
Following Trendsetters: Collective Decisions in Online Social Networks
Author :
Sakamoto, Yasuaki
Author_Institution :
Stevens Inst. of Technol., Hoboken, NJ, USA
Abstract :
The convenience of sharing information online led to a tremendous amount of information available to Web users. The present work examines how people process information in online social networks, using Digg as an example. In Digg, users submit and vote for news stories they like, and the collective decisions of the users determine which news stories become prominent. How do Digg users scan the sea of submissions for stories they like? The results from the statistical analyses and computer simulations of Digg users´ voting behavior reveal that users filter out stories using the choices of trendsetters, rather than using the majority decisions. Stories that trendsetters like attract many followers and gain vast popularity.
Keywords :
Internet; decision making; human computer interaction; social networking (online); statistical analysis; Digg; Web; collective decisions; computer simulations; following trendsetters; information processing; information sharing; online social networks; statistical analyses; Communities; Computational modeling; Data models; Humans; Peer to peer computing; Predictive models; Social network services; Collective decisions; computational modeling; followers; online communities; social network analysis; trendsetters;
Conference_Titel :
System Science (HICSS), 2012 45th Hawaii International Conference on
Conference_Location :
Maui, HI
Print_ISBN :
978-1-4577-1925-7
Electronic_ISBN :
1530-1605
DOI :
10.1109/HICSS.2012.283
