Can Cascades be Predicted?
| Autor: | Jon Kleinberg, P. Alex Dow, Justin Cheng, Lada A. Adamic, Jure Leskovec |
|---|---|
| Rok vydání: | 2014 |
| Předmět: |
Social and Information Networks (cs.SI)
FOS: Computer and information sciences Physics - Physics and Society Social network business.industry Computer science FOS: Physical sciences Computer Science - Social and Information Networks Machine Learning (stat.ML) Physics and Society (physics.soc-ph) Data science Statistics - Machine Learning Key (cryptography) H.2.8 business Information cascade |
| Zdroj: | WWW |
| DOI: | 10.48550/arxiv.1403.4608 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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