Incorporating Participant Reputation in Community-Driven Question Answering Systems

Liu, Hong; Yang, Zaihan; Davison, Brian D.

doi:10.1109/cse.2009.28

Cited by 11 publications

(6 citation statements)

References 16 publications

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“…However, our focus is on internal scores. In this area, many graph-based approaches have been proposed based on PageRank [19,23], trust/distrust propagation [15,45], mutual reinforcement propagation between user reputation scores and content quality [4,33] and frequent patterns [14]. In addition, Agarwal et al [1] also proposed a graph-based algorithm to identify influential bloggers.…”

Section: Definition Of Reputationmentioning

confidence: 99%

User reputation in a comment rating environment

Chen

Guo

Tseng

et al. 2011

Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

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Reputable users are valuable assets of a web site. We focus on user reputation in a comment rating environment, where users make comments about content items and rate the comments of one another. Intuitively, a reputable user posts high quality comments and is highly rated by the user community. To our surprise, we find that the quality of a comment judged editorially is almost uncorrelated with the ratings that it receives, but can be predicted using standard text features, achieving accuracy as high as the agreement between two editors! However, extracting a pure reputation signal from ratings is difficult because of data sparseness and several confounding factors in users' voting behavior. To address these issues, we propose a novel bias-smoothed tensor model and empirically show that our model significantly outperforms a number of alternatives based on Yahoo! News, Yahoo! Buzz and Epinions datasets.

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Section: Definition Of Reputationmentioning

confidence: 99%

User reputation in a comment rating environment

Chen

Guo

Tseng

et al. 2011

Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

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“…Predicting user-voted best answers: There is a large body of work on building models to predict user-voted best answers (e.g., [1,6,15,3,8,17,10]). The assumption behind this line of work is that quality judgments of answers are expensive to obtain and it seems to be reasonable to use the readily available user-voted best answers as the target to predict.…”

Section: Related Workmentioning

confidence: 99%

“…On the other hand, many studies [1,6,15,3,8,17,10] have treated the user-voted best answers as the ground truth source of high quality answers and have developed models to predict whether an answer would be voted as the best answer based on features extracted from the answer, past activities of the answerer, etc. As a counter-point, other studies, e.g., [14,9], report best answers not to be entirely high quality ones.…”

Section: Introductionmentioning

confidence: 99%

Vote calibration in community question-answering systems

Chen

Dasgupta

Wang

et al. 2012

Proceedings of the 35th International ACM SIGIR Conference on Research and Development in Information Retrieval

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User votes are important signals in community question-answering (CQA) systems. Many features of typical CQA systems, e.g. the best answer to a question, status of a user, are dependent on ratings or votes cast by the community. In a popular CQA site, Yahoo! Answers, users vote for the best answers to their questions and can also thumb up or down each individual answer. Prior work has shown that these votes provide useful predictors for content quality and user expertise, where each vote is usually assumed to carry the same weight as others. In this paper, we analyze a set of possible factors that indicate bias in user voting behavior -these factors encompass different gaming behavior, as well as other eccentricities, e.g., votes to show appreciation of answerers. These observations suggest that votes need to be calibrated before being used to identify good answers or experts. To address this problem, we propose a general machine learning framework to calibrate such votes. Through extensive experiments based on an editorially judged CQA dataset, we show that our supervised learning method of content-agnostic vote calibration can significantly improve the performance of answer ranking and expert ranking.

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“…Their results outperformed in finding user reputation and were successful in filtering spam experts. A comparative study of PageRank and HITS like schemes in modeling user reputation was carried out by Hong et al [16] on different topics in Yahoo! Answers.…”

Section: Related Workmentioning

confidence: 99%

Confluence of social network, social question and answering community, and user reputation model for information seeking and experts generation

Alam

Khusro

Ullah

et al. 2016

Journal of Information Science

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Social question and answering (Q&A) is one of the most effective approaches to knowledge acquisition using information seeking and collaboration. Most modern social Q&A systems use a static points-based user reputation model, which has the effect of diminishing the value of experts. In order to overcome this issue, we have developed a dynamic points-based user reputation model that takes user rating and social network analysis as input. The impact weight of each relation and user ratings are not static but are dependent on the current level of asker and answerer and on the difficulty level of the question. We propose a novel social Q&A platform that is the confluence of different features of social network, social Q&A, and the dynamic points-based user reputation model. The beta version of the system was evaluated by conducting a clinical study for 4 months in different academic environments. The results show that the proposed social Q&A outperforms the available static points-based social Q&A systems in representing the actual user reputation with an increased user satisfaction.

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Incorporating Participant Reputation in Community-Driven Question Answering Systems

Abstract: Abstract-Community-driven

Cited by 11 publications

References 16 publications

User reputation in a comment rating environment

User reputation in a comment rating environment

Vote calibration in community question-answering systems

Confluence of social network, social question and answering community, and user reputation model for information seeking and experts generation

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