2013 ConfluenceConformityInfluencein
- (Tang et al., 2013) ⇒ Jie Tang, Sen Wu, and Jimeng Sun. (2013). “Confluence: Conformity Influence in Large Social Networks.” In: Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ISBN:978-1-4503-2174-7 doi:10.1145/2487575.2487691
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- http://scholar.google.com/scholar?q=%222013%22+Confluence%3A+Conformity+Influence+in+Large+Social+Networks
- http://dl.acm.org/citation.cfm?id=2487575.2487691&preflayout=flat#citedby
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Abstract
Conformity is a type of social influence involving a change in opinion or behavior in order to fit in with a group. Employing several social networks as the source for our experimental data, we study how the effect of conformity plays a role in changing users' online behavior. We formally define several major types of conformity in individual, peer, and group levels. We propose Confluence model to formalize the effects of social conformity into a probabilistic model. Confluence can distinguish and quantify the effects of the different types of conformities. To scale up to large social networks, we propose a distributed learning method that can construct the Confluence model efficiently with near-linear speedup. Our experimental results on four different types of large social networks, i.e., Flickr, Gowalla, Weibo and Co-Author, verify the existence of the conformity phenomena. Leveraging the conformity information, Confluence can accurately predict actions of users. Our experiments show that Confluence significantly improves the prediction accuracy by up to 5-10% compared with several alternative methods.
References
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| Author | volume | Date Value | title | type | journal | titleUrl | doi | note | year | |
|---|---|---|---|---|---|---|---|---|---|---|
| 2013 ConfluenceConformityInfluencein | Jie Tang Jimeng Sun Sen Wu | Confluence: Conformity Influence in Large Social Networks | 10.1145/2487575.2487691 | 2013 |