Automated dialog analysis to predict blogger community response to newcomer inquiries. (December 2018) - Nistor, Nicolae; Dascalu, Mihai; Serafin, Yvonne; Trausan-Matu, Stefan
Abstract: 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 × 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. Highlights: We examined collaborative dialog in online knowledge building communities. Community response is an essential prerequisite of informal learning. Automated dialog analysis based on Bakhtin's dialogism was employed. For all and well-defined topics, only inquiry format influenced community response. For ill-defined topics only dialog quality influenced community response.