Abstract
Online social networking services like Facebook provides a popular way for users to participate in different communication groups and discuss relevant topics with each other. While users tend to have an impact on each other, it is important to better understand and analyze users behavior in specific online groups. For social networking sites it is of interest to know if a topic will be interesting for users or not. Therefore, this study examines the prediction of user participation in online social networks discussions, in which we argue that it is possible to predict user participation in a public group using common machine learning techniques. We are predicting user participation based on association rules built with respect to user activeness of current posts. In total, we have crawled and extracted 2,443 active users interacting on 610 posts with over 14,117 comments on Facebook. The results show that the proposed approach has a high level of accuracy and the systematic study clearly depicts the possibility to predict user participation in social networking sites.
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Acknowledgement
This work was partially supported by the European Union’s Seventh Framework Programme for research, technological development and demonstration under grant agreement no 316097 [ENGINE] and by The National Science Centre, the decision no. DEC-2013/09/B/ST6/02317.
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Erlandsson, F., Borg, A., Johnson, H., Bródka, P. (2016). Predicting User Participation in Social Media. In: Wierzbicki, A., Brandes, U., Schweitzer, F., Pedreschi, D. (eds) Advances in Network Science. NetSci-X 2016. Lecture Notes in Computer Science(), vol 9564. Springer, Cham. https://doi.org/10.1007/978-3-319-28361-6_10
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DOI: https://doi.org/10.1007/978-3-319-28361-6_10
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