Summary of What Drives Online Popularity: Author, Content or Sharers? Estimating Spread Dynamics with Bayesian Mixture Hawkes, by Pio Calderon et al.
What Drives Online Popularity: Author, Content or Sharers? Estimating Spread Dynamics with Bayesian Mixture Hawkes
by Pio Calderon, Marian-Andrei Rizoiu
First submitted to arxiv on: 5 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Social and Information Networks (cs.SI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed Bayesian Mixture Hawkes (BMH) model jointly learns the influence of source, content, and spread in shaping the popularity of online items. The BMH model is formulated as a hierarchical mixture model of separable Hawkes processes, accommodating different classes of Hawkes dynamics and feature sets’ influence on these classes. This approach outperforms state-of-the-art models and predictive baselines on two real-world retweet cascade datasets referencing articles from controversial and traditional media publishers. The BMH model also demonstrates better utilization of cascade- and item-level information compared to alternatives. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper studies how online content spreads on social media, considering three factors: the source, the content itself, and the pathways of content spread. It proposes a new model called Bayesian Mixture Hawkes (BMH) that takes into account all these factors to predict how widely and rapidly online items will be shared. The model is tested on two real-world datasets and performs better than other models and baselines. Additionally, the paper shows that the effectiveness of headline writing styles varies across different types of publishers. |
Keywords
» Artificial intelligence » Mixture model
