2014/03/25 by Lilian Weng, Filippo Menczer, Weng, Lilian +3 · 1 citation
Computer Science · Physics and Astronomy · #Computers and Society (cs.CY) #Data Analysis #FOS: Computer and information sciences #FOS: Physical sciences #Physics and Society (physics.soc-ph) #Social and Information Networks (cs.SI) #Statistics and Probability (physics.data-an) #cs.CY #cs.SI #physics.data-an #physics.soc-ph
paper · pdf · doi:10.48550/arxiv.1403.6199
10 pages, 6 figures, 2 tables. Proceedings of 8th AAAI Intl. Conf. on Weblogs and social media (ICWSM 2014)
arxiv created 2014/05/30 · arxiv updated 2014/06/02
We investigate the predictability of successful memes using their early spreading patterns in the underlying social networks. We propose and analyze a comprehensive set of features and develop an accurate model to predict future popularity of a meme given its early spreading patterns. Our paper provides the first comprehensive comparison of existing predictive frameworks. We categorize our features into three groups: influence of early adopters, community concentration, and characteristics of adoption time series. We find that features based on community structure are the most powerful predictors of future success. We also find that early popularity of a meme is not a good predictor of its future popularity, contrary to common belief. Our methods outperform other approaches, particularly in the task of detecting very popular or unpopular memes.