Name
Abstract | ||
|---|---|---|
The occurrence of new events in a system is typically driven by external causes and by previous events taking place inside the system. This is a general statement, applying to a range of situations including, more recently, to the activity of users in online social networks (OSNs). Here we develop a method for extracting from a series of posting times the relative contributions that are exogenous, e.g., news media, and endogenous, e.g., information cascade. The method is based on the fitting of a generalized linear model (GLM) equipped with a self-excitation mechanism. We test the method with synthetic data generated by a nonlinear Hawkes process, and apply it to a real time series of tweets with a given hashtag. In the empirical dataset, the estimated contributions of exogenous and endogenous volumes are close to the amounts of original tweets and retweets respectively. We conclude by discussing the possible applications of the method, for instance in online marketing. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1103/PhysRevE.98.052304 | PHYSICAL REVIEW E |
Field | DocType | Volume |
Social media,Nonlinear system,Social network,Information cascade,News media,Online advertising,Synthetic data,Generalized linear model,Artificial intelligence,Classical mechanics,Machine learning,Physics | Journal | 98 |
Issue | ISSN | Citations |
5 | 2470-0045 | 0 |
PageRank | References | Authors |
0.34 | 12 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kazuki Fujita | 1 | 0 | 0.34 |
| Alexey N. Medvedev | 2 | 0 | 0.34 |
| Shinsuke Koyama | 3 | 94 | 8.84 |
| Renaud Lambiotte | 4 | 920 | 64.98 |
| Shigeru Shinomoto | 5 | 343 | 37.75 |