Ranking Online Memes in Emergency Events Based on Transfer Entropy

The rapid proliferation of online social networks has greatly boosted the dissemination and evolution of online memes, which can be free text, trending catchphrase, or micro media. However, this information abundance is exceeding the capability of the public to consume it, especially in unusual situations such as emergency management, intelligence acquisition, and crime analysis. Thus, there calls for a reliable approach to rank meme appropriately according to its influence, which will let the public focus on the most important memes without sinking into the information flood. However, studying meme in any detail on a large scale proves to be challenging. Previous bottom-up approaches are often highly complex, while the more recent top-down network analysis approaches lack detailed modeling for meme dynamics. In this paper, we first present a formal definition for meme ranking task, and then introduce a scheme for meme ranking in the context of online social networks (OSN). To the best of our knowledge, this is the first time that memes have been ranked in a model-free manner. Empirical results on two emergency events indicate that our scheme outperforms several benchmark approaches. This scheme is also robust by insensitive to sample rate. In light of the scheme´s fine-grain modeling on meme dynamics, we also reveal two key factors affecting meme influence.