Predicting Successful Memes using Network and Community Structure

TL;DR

This paper presents a model that predicts meme success based on early spreading patterns and community structure, outperforming existing methods and challenging the idea that early popularity predicts future success.

Contribution

It introduces a comprehensive feature set including community-based metrics and compares predictive frameworks, highlighting the importance of community structure in success prediction.

Findings

Community features are the strongest predictors of meme success.

Early popularity is not a reliable indicator of future success.

Proposed methods outperform existing approaches, especially for very popular or unpopular memes.

Abstract

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.