# Predicting Successful Memes using Network and Community Structure

arXiv: https://arxiv.org/abs/1403.6199 · HTML: https://mlsanity.com/paper/1403.6199/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Mar 2014.

## Built on by (most cited first, 11 of 11)

- [On the Evolution of (Hateful) Memes by Means of Multimodal Contrastive Learning](https://mlsanity.com/paper/2212.06573/) (2022)
- [Contextualizing Internet Memes Across Social Media Platforms](https://mlsanity.com/paper/2311.11157/) (2023)
- [Using Semantic Similarity and Text Embedding to Measure the Social Media Echo of Strategic Communications](https://mlsanity.com/paper/2303.16694/) (2023)
- [Meme Similarity and Emotion Detection using Multimodal Analysis](https://mlsanity.com/paper/2503.17493/) (2025)
- [Clustering Internet Memes Through Template Matching and Multi-Dimensional Similarity](https://mlsanity.com/paper/2505.00056/) (2025)
- [AI-Gram: When Visual Agents Interact in a Social Network](https://mlsanity.com/paper/2604.21446/) (2026)
- [Incomplete Gamma Integrals for Deep Cascade Prediction using Content, Network, and Exogenous Signals](https://mlsanity.com/paper/2106.07012/) (2021)
- [Cluster-based Deep Ensemble Learning for Emotion Classification in Internet Memes](https://mlsanity.com/paper/2302.08343/) (2023)
- [Predicting Viral Rumors and Vulnerable Users for Infodemic Surveillance](https://mlsanity.com/paper/2401.09724/) (2024)
- [Limits to Predicting Online Speech Using Large Language Models](https://mlsanity.com/paper/2407.12850/) (2024)
- [When or What? Understanding Consumer Engagement on Digital Platforms](https://mlsanity.com/paper/2510.10474/) (2025)

## Abstract (arXiv)

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.