A model-free scheme for meme ranking in social media

作者:

Highlights:

• We develop a model-free scheme for meme ranking task.

• We have evaluated the proposed scheme on two large-scale real-world datasets.

• Our work presents fine-grained modeling of dynamic information and can be readily generalized to other domains.

摘要

The prevalence of social media has greatly catalyzed the dissemination and proliferation of online memes (e.g., ideas, topics, melodies, and tags). However, this information abundance is exceeding the capability of online users to consume it. Ranking memes based on their popularities could promote online advertisement and content distribution. Despite such importance, few existing work can solve this problem well. They are either daunted by unpractical assumptions or incapability of characterizing dynamic information. As such, in this paper, we elaborate a model-free scheme to rank online memes in the context of social media. This scheme is capable to characterize the nonlinear interactions of online users, which mark the process of meme diffusion. Empirical studies on two large-scale, real-world datasets (one in English and one in Chinese) demonstrate the effectiveness and robustness of the proposed scheme. In addition, due to its fine-grained modeling of user dynamics, this ranking scheme can also be utilized to explain meme popularity through the lens of social influence.

论文关键词:Meme ranking,Model-free scheme,Transfer entropy

论文评审过程:Received 6 March 2015, Revised 9 July 2015, Accepted 3 October 2015, Available online 13 October 2015, Version of Record 5 January 2016.

论文官网地址:https://doi.org/10.1016/j.dss.2015.10.002