Inferring Web communities from link topology
Proceedings of the ninth ACM conference on Hypertext and hypermedia : links, objects, time and space---structure in hypermedia systems: links, objects, time and space---structure in hypermedia systems
Authoritative sources in a hyperlinked environment
Journal of the ACM (JACM)
Item selection by "hub-authority" profit ranking
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
SIAM Journal on Discrete Mathematics
Maximizing the spread of influence through a social network
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Fast discovery of connection subgraphs
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Neighborhood Formation and Anomaly Detection in Bipartite Graphs
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Detecting reviewer bias through web-based association mining
Proceedings of the 2nd ACM workshop on Information credibility on the web
Search engine predilection towards news media providers
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Semi-supervised correction of biased comment ratings
Proceedings of the 21st international conference on World Wide Web
Identify Online Store Review Spammers via Social Review Graph
ACM Transactions on Intelligent Systems and Technology (TIST)
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In this paper, we investigate how deviation in evaluation activities may reveal bias on the part of reviewers and controversy on the part of evaluated objects. We focus on a 'data-centric approach' where the evaluation data is assumed to represent the 'ground truth'. The standard statistical approaches take evaluation and deviation at face value. We argue that attention should be paid to the subjectivity of evaluation, judging the evaluation score not just on 'what is being said' (deviation), but also on 'who says it' (reviewer) as well as on 'whom it is said about' (object). Furthermore, we observe that bias and controversy are mutually dependent, as there is more bias if there is higher deviation on a less controversial object. To address this mutual dependency, we propose a reinforcement model to identify bias and controversy. We test our model on real-life data to verify its applicability.