# Can Cascades be Predicted?

arXiv: https://arxiv.org/abs/1403.4608 · HTML: https://mlsanity.com/paper/1403.4608/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Mar 2014.

## Built on by (most cited first, 30 of 31)

- [TwHIN-BERT: A Socially-Enriched Pre-trained Language Model for Multilingual Tweet Representations at Twitter](https://mlsanity.com/paper/2209.07562/) (2022)
- [Proactively Reducing the Hate Intensity of Online Posts via Hate Speech Normalization](https://mlsanity.com/paper/2206.04007/) (2022)
- [CCGL: Contrastive Cascade Graph Learning](https://mlsanity.com/paper/2107.12576/) (2021)
- [HotComment: A Benchmark for Evaluating Popularity of Online Comments](https://mlsanity.com/paper/2604.25614/) (2026)
- [CasCIFF: A Cross-Domain Information Fusion Framework Tailored for Cascade Prediction in Social Networks](https://mlsanity.com/paper/2308.04961/) (2023)
- [Cascade-based Randomization for Inferring Causal Effects under Diffusion Interference](https://mlsanity.com/paper/2405.12340/) (2024)
- [From Leakage to Fidelity: Reliable Benchmarking for Temporal Cascade Prediction](https://mlsanity.com/paper/2510.25348/) (2025)
- [CCasGNN: Collaborative Cascade Prediction Based on Graph Neural Networks](https://mlsanity.com/paper/2112.03644/) (2021)
- [Revisiting Information Cascades in Online Social Networks](https://mlsanity.com/paper/2208.00904/) (2022)
- [Explicit Time Embedding Based Cascade Attention Network for Information Popularity Prediction](https://mlsanity.com/paper/2308.09976/) (2023)
- [The Role of Network and Identity in the Diffusion of Hashtags](https://mlsanity.com/paper/2407.12771/) (2024)
- [AutoCas: Autoregressive Cascade Predictor in Social Networks via Large Language Models](https://mlsanity.com/paper/2502.18040/) (2025)
- [Estimating Online Influence Needs Causal Modeling! Counterfactual Analysis of Social Media Engagement](https://mlsanity.com/paper/2505.19355/) (2025)
- [OmniTrend: Content-Context Modeling for Scalable Social Popularity Prediction](https://mlsanity.com/paper/2604.26252/) (2026)
- [Independent Asymmetric Embedding for Information Diffusion Prediction on Social Networks](https://mlsanity.com/paper/2105.08291/) (2021)
- [Incomplete Gamma Integrals for Deep Cascade Prediction using Content, Network, and Exogenous Signals](https://mlsanity.com/paper/2106.07012/) (2021)
- [Temporal Scale Estimation for Oversampled Network Cascades: Theory, Algorithms, and Experiment](https://mlsanity.com/paper/2109.10937/) (2021)
- [HierCas: Hierarchical Temporal Graph Attention Networks for Popularity Prediction in Information Cascades](https://mlsanity.com/paper/2310.13219/) (2023)
- [Predicting Viral Rumors and Vulnerable Users for Infodemic Surveillance](https://mlsanity.com/paper/2401.09724/) (2024)
- [Information Cascade Prediction under Public Emergencies: A Survey](https://mlsanity.com/paper/2404.01319/) (2024)
- [CasFT: Future Trend Modeling for Information Popularity Prediction with Dynamic Cues-Driven Diffusion Models](https://mlsanity.com/paper/2409.16619/) (2024)
- [On Your Mark, Get Set, Predict! Modeling Continuous-Time Dynamics of Cascades for Information Popularity Prediction](https://mlsanity.com/paper/2409.16623/) (2024)
- [Before the Outrage: Challenges and Advances in Predicting Online Antisocial Behavior](https://mlsanity.com/paper/2507.20614/) (2025)
- [Deep learning framework for predicting stochastic take-off and die-out of early spreading](https://mlsanity.com/paper/2510.04574/) (2025)
- [Linking Heterogeneous Data with Coordinated Agent Flows for Social Media Analysis](https://mlsanity.com/paper/2510.26172/) (2025)
- [Benchmark Datasets for Lead-Lag Forecasting on Social Platforms](https://mlsanity.com/paper/2511.03877/) (2025)
- [Predicting Post Virality with Temporal Cross-Attention over Trend Signals](https://mlsanity.com/paper/2605.02358/) (2026)
- [Cyberbullying Governance on Social Media: A Unified Framework from Content Identification to Intervention](https://mlsanity.com/paper/2605.27584/) (2026)
- [Boundary Degree as a Node-level Feature for Epidemic Scenario Identification in Agent-based Cascade Simulations](https://mlsanity.com/paper/2606.29596/) (2026)
- [Memetic Trojans: Social Contagions as Carriers of Adversarial Payloads in Agent Networks](https://mlsanity.com/paper/2610.00430/) (2026)

## Abstract (arXiv)

On many social networking web sites such as Facebook and Twitter, resharing or reposting functionality allows users to share others' content with their own friends or followers. As content is reshared from user to user, large cascades of reshares can form. While a growing body of research has focused on analyzing and characterizing such cascades, a recent, parallel line of work has argued that the future trajectory of a cascade may be inherently unpredictable. In this work, we develop a framework for addressing cascade prediction problems. On a large sample of photo reshare cascades on Facebook, we find strong performance in predicting whether a cascade will continue to grow in the future. We find that the relative growth of a cascade becomes more predictable as we observe more of its reshares, that temporal and structural features are key predictors of cascade size, and that initially, breadth, rather than depth in a cascade is a better indicator of larger cascades. This prediction performance is robust in the sense that multiple distinct classes of features all achieve similar performance. We also discover that temporal features are predictive of a cascade's eventual shape. Observing independent cascades of the same content, we find that while these cascades differ greatly in size, we are still able to predict which ends up the largest.