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Precise Answering in Internet Groups Forum

  • Unique Paper ID: 154883
  • Volume: 8
  • Issue: 12
  • PageNo: 666-673
  • Abstract:
  • We focus on detecting potential topical experts in community question answering platforms early on in their lifecycle. We use a semi-supervised machine learning approach. We extract three types of feature: (i) textual, (ii) behavioral, and (iii) time-aware, which we use to predict whether a user will become an expert in the long term. We compare our method to a machine learning method based on a state-of-the-art method in expertise retrieval. Results on data from Stack Overflow demonstrate the utility of adding behavioral and time-aware features to the baseline method with a net improvement in accuracy of 26% for very early detection of expertise. In this paper we provide measures of labeling difficult questions and use the number of difficult questions responded by a user combined with other user interaction parameters in identifying potential topical experts. Using a random forest classifier with the proposed feature set on StackOverflow data, we obtain an improvement in accuracy of 5 - 16% over existing techniques, in detecting topical experts. The popularity of community question answer (CQA) forums like StackOverflow, Yahoo Answers and Quora is increasing tremendously with thousands of questions being posted each day and about thrice the number of responses being provided. With such query explosion, users participating in these forums receive a huge number of postings that adversely affects their responsiveness and also the quality of the responses. Hence, identifying topical experts is necessary to improve the efficacy of these systems in terms of both response time and quality.
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Cite This Article

  • ISSN: 2349-6002
  • Volume: 8
  • Issue: 12
  • PageNo: 666-673

Precise Answering in Internet Groups Forum

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Impact Factor
8.01 (Year 2024)
UGC Approved
Journal no 47859

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