The anatomy of a large-scale hypertextual Web search engine
WWW7 Proceedings of the seventh international conference on World Wide Web 7
Authoritative sources in a hyperlinked environment
Journal of the ACM (JACM)
Maximizing the spread of influence through a social network
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Feedback effects between similarity and social influence in online communities
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
LIBLINEAR: A Library for Large Linear Classification
The Journal of Machine Learning Research
Explore/Exploit Schemes for Web Content Optimization
ICDM '09 Proceedings of the 2009 Ninth IEEE International Conference on Data Mining
TwitterRank: finding topic-sensitive influential twitterers
Proceedings of the third ACM international conference on Web search and data mining
Mining topic-level influence in heterogeneous networks
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Identifying topical authorities in microblogs
Proceedings of the fourth ACM international conference on Web search and data mining
Using graded-relevance metrics for evaluating community QA answer selection
Proceedings of the fourth ACM international conference on Web search and data mining
Mark my words!: linguistic style accommodation in social media
Proceedings of the 20th international conference on World wide web
User reputation in a comment rating environment
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Ranking individuals and groups by influence propagation
PAKDD'11 Proceedings of the 15th Pacific-Asia conference on Advances in knowledge discovery and data mining - Volume Part II
TWITOBI: A Recommendation System for Twitter Using Probabilistic Modeling
ICDM '11 Proceedings of the 2011 IEEE 11th International Conference on Data Mining
Effects of user similarity in social media
Proceedings of the fifth ACM international conference on Web search and data mining
Participation in an online mathematics community: differentiating motivations to add
Proceedings of the ACM 2012 conference on Computer Supported Cooperative Work
Online sampling of high centrality individuals in social networks
PAKDD'10 Proceedings of the 14th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I
Using content and interactions for discovering communities in social networks
Proceedings of the 21st international conference on World Wide Web
Magnet community identification on social networks
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Transparent user models for personalization
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Finding trendsetters in information networks
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Vote calibration in community question-answering systems
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
Understanding and managing cascades on large graphs
Proceedings of the VLDB Endowment
On using category experts for improving the performance and accuracy in recommender systems
Proceedings of the 21st ACM international conference on Information and knowledge management
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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.