Incorporate credibility into context for the best social media answers

Qi Su, Helen Kai Yun Chen, Chu-ren Huang

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

6 Citations (Scopus)


In this paper, we focus on the task of identifying the best answer for a usergenerated question in Collaborative Question Answering (CQA) services. Given that most existing research on CQA has focused on non-textual features such as click-through counts which are relatively difficult to access, we examine the effectiveness of diverse content-based features for the task. Specially, we propose to explore how the information of evidentiality can contribute to the task. By the comparison of diverse textual features and their combinations, the current study provides useful insight into the issues of detecting the best answer to a given question in CQA without user features or system specific link structures.
Original languageEnglish
Title of host publicationPACLIC 24 - Proceedings of the 24th Pacific Asia Conference on Language, Information and Computation
Number of pages7
Publication statusPublished - 1 Dec 2010
Event24th Pacific Asia Conference on Language, Information and Computation, PACLIC 24 - Sendai, Japan
Duration: 4 Nov 20107 Nov 2010


Conference24th Pacific Asia Conference on Language, Information and Computation, PACLIC 24


  • Answer assessment
  • Collaborative question answering
  • Credibility
  • Quality

ASJC Scopus subject areas

  • Language and Linguistics
  • Computer Science (miscellaneous)

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