Discovering authorities in question answer communities by using link analysis

By Paweł Jurczyk, Eugene Agichtein. 2007

413 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Science Applications, Information Systems

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Citations in the corpus

From amateurs to connoisseurs: modeling the evolution of user expertise through online reviews (2013, background): “We also brieﬂy mention the topic of expertise identiﬁcation [1, 6, 16, 32].”
Community-Based Question Answering via Heterogeneous Social Network Learning (2016, background): “The benefits of cQA have been proven in (Jurczyk and Agichtein 2007; Li, Lyu, and King 2012) and cQA has attracted lots of attention in the fields of information retrieval and natural language processing research (Bilotti et al. 2010).”
SemEval-2019 Task 8: Fact Checking in Community Question Answering Forums (2019, background): “Jurczyk and Agichtein (2007) modelled author authority using link analysis.”
Community-Based Question Answering via Asymmetric Multi-Faceted Ranking Network Learning (2017, background): “The benefits of CQA have been well-recognized today (Jurczyk and Agichtein 2007).”
Identifying topical authorities in microblogs (2011, background): “[12] identified authorities in Q&A communities using link analysis by considering the induced graph from interactions between users.”
Finding high-quality content in social media (2008, methodology): “The HITS algorithm was applied over the same graph [8, 19 ] and it was shown to produce good results in finding experts and/or good answers.”
A Comprehensive Survey and Classification of Approaches for Community Question Answering (2016, methodology): “Jurczyk and Agichtein [2007] created ground truth automatically from user features, such as best answer ratio. Unfortunately, such ground truth is obtained according to a certain heuristic method that itself can be considered as an approach to estimate user expertise [J. Liu et al. 2011]. Zhang et a”
Early detection of potential experts in question answering communities (2011, background): “[3] performed link analysis over the question-answer interconnections among users of Yahoo Answers.”
Learning to recognize reliable users and content in social media with coupled mutual reinforcement (2009, methodology): “Jurczyk and Agichtein [ 10 ] show an application of the HITS algorithm to a CQA portal, especially the user interactions graph, and show a positive correlation between authority calculated with the HITS algorithm and answer quality.”
Discovering value from community activity on focused question answering sites: a case study of stack overflow (2012, background): “informed the design of network-based ranking algorithms fo r identifying users with high expertise [10, 17, 25, 26].”
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].”
Finding the right facts in the crowd: factoid question answering over social media (2008, methodology): “Jurczyk and Agichtein [14] show an application of HITS algorithm to a QA portal.”
Modeling and Predicting the Helpfulness of Online Reviews (2008, neutral): “Motivated by this idea, various algorithms have been proposed to discover the authorities or leaders in the Web domain [9, 17, 13].”
IntroAssist: A Tool to Support Writing Introductory Help Requests (2018, background): “Even if an algorithm recommends an ideal match based on target expertise, willingness to help, or similar interests [8,31,34,38,40,43,50,69], people reaching out might still fail to send a help request that resonates with the receiver enough for them to respond.”