Gespeichert in:
| Hauptverfasser: | , , , , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2020
|
| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/2007.08916 |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866916109362397184 |
|---|---|
| author | Gleeson, James P. Onaga, Tomokatsu Fennell, Peter Cotter, James Burke, Raymond O'Sullivan, David J. P. |
| author_facet | Gleeson, James P. Onaga, Tomokatsu Fennell, Peter Cotter, James Burke, Raymond O'Sullivan, David J. P. |
| contents | A detailed analysis of Twitter-based information cascades is performed, and it is demonstrated that branching process hypotheses are approximately satisfied. Using a branching process framework, models of agent-to-agent transmission are compared to conclude that a limited attention model better reproduces the relevant characteristics of the data than the more common independent cascade model. Existing and new analytical results for branching processes are shown to match well to the important statistical characteristics of the empirical information cascades, thus demonstrating the power of branching process descriptions for understanding social information spreading. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2007_08916 |
| institution | arXiv |
| publishDate | 2020 |
| record_format | arxiv |
| spellingShingle | Branching process descriptions of information cascades on Twitter Gleeson, James P. Onaga, Tomokatsu Fennell, Peter Cotter, James Burke, Raymond O'Sullivan, David J. P. Physics and Society 60J85 A detailed analysis of Twitter-based information cascades is performed, and it is demonstrated that branching process hypotheses are approximately satisfied. Using a branching process framework, models of agent-to-agent transmission are compared to conclude that a limited attention model better reproduces the relevant characteristics of the data than the more common independent cascade model. Existing and new analytical results for branching processes are shown to match well to the important statistical characteristics of the empirical information cascades, thus demonstrating the power of branching process descriptions for understanding social information spreading. |
| title | Branching process descriptions of information cascades on Twitter |
| topic | Physics and Society 60J85 |
| url | https://arxiv.org/abs/2007.08916 |