Abstract
A common approach, adopted by most current research, represents users of a social media platform as nodes in a network, connected by various types of links indicating the different kinds of inter-user relationships and interactions. However, social media dynamics and the observed behavioral phenomena do not conform to this user-node-centric view, partly because it ignores the behavioral impact of connected user collectives. GitHub is unique in the social media setting in this respect: it is organized into “repositories”, which along with the users who contribute to them, form highly-interactive task-oriented “social collectives”. In this paper, we recast our understanding of all social media as a landscape of collectives, or “convos”: sets of users connected by a common interest in an (possibly evolving) information artifact, such as a repository in GitHub, a subreddit in Reddit or a group of hashtags in Twitter. We describe a computational approach to classifying convos at different stages of their “lifespan” into distinct collective behavioral classes. We then train a Multi-layer Perceptron (MLP) to learn transition probabilities between behavioral classes to predict, with high-degree of accuracy, future behavior and activity levels of these convos.
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Acknowledgments
This work was supported by the Defense Advanced Research Projects Agency (DARPA) under Contract No FA8650-18-C-7824. All statements of fact, opinion or conclusions contained herein are those of the authors and should not be construed as representing the official views or policies of AFRL, DARPA, or the U.S. Government.
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Katsios, G., Sa, N., Strzalkowski, T. (2020). Social Convos: A New Approach to Modeling Information Diffusion in Social Media. In: Ahram, T. (eds) Advances in Artificial Intelligence, Software and Systems Engineering. AHFE 2019. Advances in Intelligent Systems and Computing, vol 965. Springer, Cham. https://doi.org/10.1007/978-3-030-20454-9_3
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DOI: https://doi.org/10.1007/978-3-030-20454-9_3
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