Name
Abstract | ||
|---|---|---|
Modern social media platforms facilitate the rapid spread of information online. Modelling phenomena such as social contagion and information diffusion are contingent upon a detailed understanding of the information-sharing processes. In Twitter, an important aspect of this occurs with retweets, where users rebroadcast the tweets of other users. To improve our understanding of how these distributions arise, we analyse the distribution of retweet times. We show that a power law with exponential cutoff provides a better fit than the power laws previously suggested. We explain this fit through the burstiness of human behaviour and the priorities individuals place on different tasks. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1145/3041021.3053903 | WWW (Companion Volume) |
DocType | Volume | Citations |
Conference | abs/1703.05545 | 1 |
PageRank | References | Authors |
0.39 | 6 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Peter Mathews | 1 | 1 | 0.39 |
| Lewis Mitchell | 2 | 155 | 17.70 |
| Giang Nguyen | 3 | 4 | 2.16 |
| nigel g bean | 4 | 47 | 10.77 |