2013 EstimatingSharerReputationviaSo

• (Yang et al., 2013) ⇒ Jaewon Yang, Bee-Chung Chen, and Deepak Agarwal. (2013). “Estimating Sharer Reputation via Social Data Calibration.” In: Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ISBN:978-1-4503-2174-7 doi:10.1145/2487575.2487685

Subject Headings:

Cited By

• http://scholar.google.com/scholar?q=%222013%22+Estimating+Sharer+Reputation+via+Social+Data+Calibration
• http://dl.acm.org/citation.cfm?id=2487575.2487685&preflayout=flat#citedby

Quotes

Author Keywords

• Data mining; influential users; sharer reputation

Abstract

Online social networks have become important channels for users to share content with their connections and diffuse information. Although much work has been done to identify socially influential users, the problem of finding "reputable" sharers, who share good content, has received relatively little attention. Availability of such reputation scores can be useful or various applications like recommending people to follow, procuring high quality content in a scalable way, creating a content reputation economy to incentivize high quality sharing, and many more. To estimate sharer reputation, it is intuitive to leverage data that records how recipients respond (through clicking, liking, etc.) to content items shared by a sharer. However, such data is usually biased --- it has a selection bias since the shared items can only be seen and responded to by users connected to the sharer in most social networks, and it has a response bias since the response is usually influenced by the relationship between the sharer and the recipient (which may not indicate whether the shared content is good). To correct for such biases, we propose to utilize an additional data source that provides unbiased goodness estimates for a small set of shared items, and calibrate biased social data through a novel multi-level hierarchical model that describes how the unbiased data and biased data are jointly generated according to sharer reputation scores. The unbiased data also provides the ground truth for quantitative evaluation of different methods. Experiments based on such ground-truth data show that our proposed model significantly outperforms existing methods that estimate social influence using biased social data.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2013 EstimatingSharerReputationviaSo	Bee-Chung Chen Deepak Agarwal Jaewon Yang			Estimating Sharer Reputation via Social Data Calibration				10.1145/2487575.2487685		2013