Name
Abstract | ||
|---|---|---|
Traditional online social network sites use a single monolithic "friends" relationship to link users. However, users may have more in common with strangers, suggesting the use of a "similarity network" to recommend content. This paper examines the usefulness of this distinction in propagating new content. Using both macroscopic and microscopic social dynamics, we present an analysis of Essembly, an ideological social network that semantically distinguishes between friends and ideological allies and nemeses. Although users have greater similarity with their allies than their friends and nemeses, surprisingly, the allies network does not affect voting behavior, despite being as large as the friends network. In contrast, users are influenced differently by their friends and nemeses, indicating that people use these networks for distinct purposes. We suggest resulting design implications for social content aggregation services and recommender systems. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1145/1357054.1357183 | CHI |
Keywords | Field | DocType |
allies network,similarity network,greater similarity,traditional online social network,ideological social network,new content,ideological ally,microscopic social dynamic,social content aggregation service,friends network,recommender system,social dynamic,social network,social networks,voting behavior | Recommender system,Internet privacy,World Wide Web,Social network,Computer science,Ideology,Social dynamics,Voting behavior | Conference |
Citations | PageRank | References |
55 | 4.89 | 9 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Michael J. Brzozowski | 1 | 241 | 13.00 |
| Tad Hogg | 2 | 1684 | 463.50 |
| Gabor Szabo | 3 | 521 | 33.21 |