Name
Abstract | ||
|---|---|---|
Characterizing information diffusion on social platforms like Twitter enables us to understand the properties of underlying media and model communication patterns. As Twitter gains in popularity, it has also become a venue to broadcast rumors and misinformation. We use epidemiological models to characterize information cascades in twitter resulting from both news and rumors. Specifically, we use the SEIZ enhanced epidemic model that explicitly recognizes skeptics to characterize eight events across the world and spanning a range of event types. We demonstrate that our approach is accurate at capturing diffusion in these events. Our approach can be fruitfully combined with other strategies that use content modeling and graph theoretic features to detect (and possibly disrupt) rumors. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1145/2501025.2501027 | SNAKDD |
Keywords | Field | DocType |
event type,model communication pattern,content modeling,epidemic model,characterizing information diffusion,epidemiological model,information cascade,graph theoretic feature,twitter gain,social platform,epidemiological modeling | Broadcasting,Graph,Internet privacy,Epidemic model,Computer science,Popularity,Information cascade,Misinformation | Conference |
Citations | PageRank | References |
68 | 3.44 | 14 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Fang Jin | 1 | 302 | 26.61 |
| Edward Dougherty | 2 | 68 | 3.44 |
| Parang Saraf | 3 | 155 | 11.98 |
| Yang Cao | 4 | 74 | 6.09 |
| Naren Ramakrishnan | 5 | 1913 | 176.25 |