A knowledge adoption model based framework for finding helpful user-generated contents in online communities

G. Alan Wang, Xiaomo Liu, Weiguo Fan

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Many online communities allow their members to provide information helpfulness judgments that can be used to guide other users to useful contents quickly. However, it is a serious challenge to solicit enough user participation in providing feedbacks in online communities. Existing studies on assessing the helpfulness of user-generated contents are mainly based on heuristics and lack of a unifying theoretical framework. In this article we propose a text classification framework for finding helpful user-generated contents in online knowledge-sharing communities. The objective of our framework is to help a knowledge seeker find helpful information that can be potentially adopted. The framework is built on the Knowledge Adoption Model that considers both content-based argument quality and information source credibility. We identify 6 argument quality dimensions and 3 source credibility dimensions based on information quality and psychological theories. Using data extracted from a popular online community, our empirical evaluations show that all the dimensions improve the performance over a traditional text classification technique that considers word-based lexical features only.

Original languageEnglish (US)
Title of host publicationInternational Conference on Information Systems 2011, ICIS 2011
Pages2951-2961
Number of pages11
StatePublished - Dec 1 2011
Externally publishedYes
Event32nd International Conference on Information System 2011, ICIS 2011 - Shanghai, China
Duration: Dec 4 2011Dec 7 2011

Publication series

NameInternational Conference on Information Systems 2011, ICIS 2011
Volume4

Other

Other32nd International Conference on Information System 2011, ICIS 2011
CountryChina
CityShanghai
Period12/4/1112/7/11

Keywords

  • Information helpfulness
  • Knowledge adoption
  • Online community
  • Text classification
  • User-generated content

ASJC Scopus subject areas

  • Information Systems

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