Automated dialog analysis to predict blogger community response to newcomer inquiries

Nicolae Nistor, Mihai Dascalu, Yvonne Serafin, Stefan Trausan-Matu

Research output: Contribution to journalArticleAcademicpeer-review

5 Citations (Web of Science)


Informal learning in online knowledge building communities (OKBCs) often starts with online academic help seeking, and with visitor inquiries on specific topics. In such a context, learning presupposes adequate OKBC response. Employing a social learning analytics approach based on natural language processing and Bakhtin's theory of dialogism, this study aims to predict blogger OKBC response. Manipulating the blog topic (well-defined vs. ill-defined) and the visitor inquiry format (off-topic vs. on-topic), a field experiment with a 2 x 2 factorial design was conducted on a sample of N = 68 blogger communities with a total of 25,303 members. For the entire sample, the community response was influenced only by the inquiry format. In a separate examination of the experimental groups, however, this remained true only for the well-defined topic, whereas for the ill-defined topic the community response only depended on the previously established dialog quality. The findings suggest identification criteria for responsive communities, which can support newcomer integration in OKBCs and, from a larger perspective, the use of OKBCs as components of formal learning environments.

Original languageEnglish
Pages (from-to)349–354
Number of pages6
JournalComputers in Human Behavior
Issue number2018
Publication statusPublished - Dec 2018
Externally publishedYes


  • Academic help seeking
  • Dialog analysis
  • Newcomer integration
  • Online knowledge building communities
  • Social learning analytics


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