Expertise networks in online communities: structure and algorithms

By Jun Zhang, Mark S. Ackerman, Lada A. Adamic. The Web Conference, 2007

924 citations recorded by the source corpus.

Subfields: Computer Science Applications, Information Systems, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/2f6b2b009637d3135e0595c5b312364c33f45d48

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In Computer Science Applications

In Information Systems

In Statistical and Nonlinear Physics

Citations in the corpus

Recommender Systems (2016, positive): “Methods for leveraging the personalized PageRank algorithm in various forms of social recommender systems are discussed in [16, 81, 350, 602, 640, 663].”
Expert Finding for Community-Based Question Answering via Ranking Metric Network Learning (2016, background): “The problem of expert finding is to choose the right experts for answering the questions posted by the users, which has attracted considerable attention recently [Xu et al., 2012; Zhao et al., 2015; Guo et al., 2008; Bouguessa et al., 2008; Zhang et al., 2007; Yang et al., 2013].”
Enhancing Expert Finding Using Organizational Hierarchies (2009, methodology): “One deficiency of these algorithms is that their performance depends significantly on characteristics of the network [31].”
Knowledge sharing and yahoo answers: everyone knows something (2008, methodology): “We rated these questions into 5 levels of expertise (as discussed in [23]).”
Harvesting ambient geospatial information from social media feeds (2011, background): “This behavior is conﬁrming observed blogosphere characteristics (e.g. Shi et al. 2007) and comparable behavioral patterns observed in online forums (see Zhang et al. 2007) or ﬁle sharing sites (e.g. Adar and Huberman 2000).”
Learning to recognize reliable users and content in social media with coupled mutual reinforcement (2009, background): “[17] analyze data from an on-line forum, seeking to identify users with high expertise.”
Early detection of potential experts in question answering communities (2011, methodology): “The experts constitute a small percentage of the community members and are responsible for a large percentage of the answers [9].”
Design lessons from the fastest q&a site in the west (2011, methodology): “For example, network analysis algorithms e.g. , HITS [16], have been used to characterize user activity and identify users with high expertise for the Java Forum [27] and Yahoo!”
Finding the right facts in the crowd: factoid question answering over social media (2008, background): “[24] analyze data from an on-line forum, seeking to identify users with high expertise.”
Finding high-quality content in social media (2008, background): “[32] analyze data from an online forum, seeking to identify users with high expertise.”
Combating Attacks and Abuse in Large Online Communities (2016, background): “Another directi on is modeling user interaction to design network-based ranking algorithms to identify experts [118, 134, 248].”