Using Social Network Analysis to predict online contributions: The impact of network diversity in cross-cultural collaboration

Mittelmeier, Jenna; Héliot, YingFei; Rienties, Bart and Whitelock, Denise (2016). Using Social Network Analysis to predict online contributions: The impact of network diversity in cross-cultural collaboration. In: WebSci '16 Proceedings of the 8th ACM Conference on Web Science, ACM, New York, pp. 269–273.

DOI: https://doi.org/10.1145/2908131.2908169

Abstract

Although collaborative web-based tools are often used in blended environments such as education, little research has analysed the predictive power of face-to-face social connections on measurable user behaviours in online collaboration, particularly in diverse settings. In this paper, we use Social Network Analysis to compare users’ pre-existing social networks with the quantity of their contributions to an online chat-based collaborative activity in a higher education classroom. In addition, we consider whether the amount of diversity present in one’s social network leads to more online contributions in an anonymous cross-cultural collaborative setting. Our findings indicate that pre-existing social connections can predict how much users contribute to online education-related collaborative activities with diverse group members, even more so than academic performance. Furthermore, our findings suggest that future Web Science research should consider how the more traditionally ‘qualitative’ socio-cultural influences affect user participation and use of online collaborative tools.

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