Abstract
Microblogging is a modern communication paradigm in which users post bits of information, or “memes” as we call them, that are brief text updates or micromedia such as photos, video or audio clips. Once a user post a meme, it become visible to the user community. When a user finds a meme of another user interesting, she can eventually repost it, thus allowing memes to propagate virally trough the social network. In this paper we introduce the meme ranking problem, as the problem of selecting which k memes (among the ones posted by their contacts) to show to users when they log into the system. The objective is to maximize the overall activity of the network, that is, the total number of reposts that occur. We deeply characterize the problem showing that not only exact solutions are unfeasible, but also approximated solutions are prohibitive to be adopted in an on-line setting. Therefore we devise a set of heuristics and we compare them trough an extensive simulation based on the real-world Yahoo! Meme social graph, using parameters learnt from real logs of meme propagations. Our experimentation demonstrates the effectiveness and feasibility of these methods.
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Animations of several meme propagations are available at http://barcelona.research.yahoo.net/memerank.
This problem is in a sense the converse of the Influence Maximization problem, defined in Kempe et al. (2003) in the context of Viral Marketing. In their problem it is given a single piece of information and the problem is that of identifying k users from which to start the propagations so to maximize the expected spread. Oppositely in our problem we are given a single user and we want to select k memes to propagate.
The probability of a possible world X is given by
$$Pr[X] = \prod\limits_{e \in E_X} p(e) \prod\limits_{e \in E \setminus E_X} (1 - p(e))~,$$where \(E_X \subseteq E\) denotes the set of arcs for which the coin flip has given a positive outcome in X.
Yahoo! Meme slogan is CREATE-FOLLOW-REPOST.
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Acknowledgements
The authors wish to acknowledge the Yahoo! Meme team for their help, Ulf Brefeld and Aris Gionis for their suggestions. This research is partially supported by the Spanish Centre for the Development of Industrial Technology under the CENIT program, project CEN-20101037, “Social Media” (www.cenitsocialmedia.es).
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Bonchi, F., Castillo, C. & Ienco, D. Meme ranking to maximize posts virality in microblogging platforms. J Intell Inf Syst 40, 211–239 (2013). https://doi.org/10.1007/s10844-011-0181-4
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DOI: https://doi.org/10.1007/s10844-011-0181-4