A graph-based approach to ememes identification and tracking in Social Media streams

Abstract A meme, as defined by Richard Dawkins, is a unit of information, a concept or an idea that spreads from person to person within a culture. Examples of memes can be a musical melody, a catchy phrase, trending news, behavioral patterns, etc. In this article the task of identifying potential memes in a stream of texts is addressed: in particular, the content generated by users of Social Media is considered as a rich source of information offering an updated window on the world happenings and on opinions of people. A textual electronic meme, a.k.a. ememe, is here considered as a frequently replicated set of related words that propagates through the Web over time. In this article an approach is proposed that aims to identify ememes in Social Media streams represented as graph of words. Furthermore, a set of measures is defined to track the change of information in time.

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