Predicting Successful Memes Using Network and Community Structure


  • Lilian Weng Indiana University Bloomington
  • Filippo Menczer Indiana University Bloomington
  • Yong-Yeol Ahn Indiana University Bloomington



meme virality, meme popularity, prediction, social networks, social media, community, Twitter, social contagion


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.




How to Cite

Weng, L., Menczer, F., & Ahn, Y.-Y. (2014). Predicting Successful Memes Using Network and Community Structure. Proceedings of the International AAAI Conference on Web and Social Media, 8(1), 535-544.