Following Trendsetters: Collective Decisions in Online Social Networks

Author

Sakamoto, Yasuaki

Author_Institution

Stevens Inst. of Technol., Hoboken, NJ, USA

fYear

2012

fDate

4-7 Jan. 2012

Firstpage

764

Lastpage

773

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;

fLanguage

English

Publisher

ieee

Conference_Titel

System Science (HICSS), 2012 45th Hawaii International Conference on

Conference_Location

Maui, HI

ISSN

1530-1605

Print_ISBN

978-1-4577-1925-7

Electronic_ISBN

1530-1605

Type

conf

DOI

10.1109/HICSS.2012.283

Filename

6148987