Popularity Prediction for Social Media over Arbitrary Time Horizons
Summary: Self-excited Hawkes point process with a growth-rate predictor for popularity across arbitrary horizons, fusing static features and observed growth. A scalable, horizon-agnostic model that outperforms horizon-specific baselines on Facebook page data with billions of views. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Daniel Haimovich (Meta)
- 2. Dima Karamshuk (Meta)
- 3. Thomas J. Leeper (Meta)
- 4. Evgeniy Riabenko (Meta)
- 5. Milan Vojnovic (London School of Economics)
BibTeX Citation
@article{haimovich_vldb22,
title = {{Popularity Prediction for Social Media over Arbitrary Time Horizons}},
author = {Haimovich, Daniel and Karamshuk, Dima and Leeper, Thomas J. and Riabenko, Evgeniy and Vojnovic, Milan},
journal = {PVLDB},
series = {{VLDB} '22},
volume = {15},
number = {4},
pages = {841--849},
doi = {10.14778/3503585.3503593},
url = {https://doi.org/10.14778/3503585.3503593},
year = {2022}
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| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 6,214 | CHASSIS: Conformity Meets Online Information Diffusion | 2020 | SIGMOD | 5.9425753e-05 |
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