What's buzzing in the blizzard of buzz? Automotive component isolation in social media postings

Alan S. Abrahams, Jian Jiao, Weiguo Fan, G. Alan Wang, Zhongju Zhang

Research output: Contribution to journalArticlepeer-review

91 Scopus citations


In the blizzard of social media postings, isolating what is important to a corporation is a huge challenge. In the consumer-related manufacturing industry, for instance, manufacturers and distributors are faced with an unrelenting, accumulating snow of millions of discussion forum postings. In this paper, we describe and evaluate text mining tools for categorizing this user-generated content and distilling valuable intelligence frozen in the mound of postings. Using the automotive industry as an example, we implement and tune the parameters of a text-mining model for component diagnostics from social media. Our model can automatically and accurately isolate the vehicle component that is the subject of a user discussion. The procedure described also rapidly identifies the most distinctive terms for each component category, which provides further marketing and competitive intelligence to manufacturers, distributors, service centers, and suppliers.

Original languageEnglish (US)
Pages (from-to)871-882
Number of pages12
JournalDecision Support Systems
Issue number4
StatePublished - Nov 2013
Externally publishedYes


  • Diagnostics
  • Social media analytics
  • Text mining
  • User-generated content (UGC)

ASJC Scopus subject areas

  • Management Information Systems
  • Information Systems
  • Developmental and Educational Psychology
  • Arts and Humanities (miscellaneous)
  • Information Systems and Management


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