Name
Abstract | ||
|---|---|---|
•An empirical Bayesian approach for making probabilistic predictions of events such as social unrest from online social network data.•A framework for predictive modelling from social networks by combining machine learning tools.•Analysis of datasets relating to social unrest in Australia during 2017/18, including a dataset used to classify protest-relevant tweets included with the paper. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1016/j.ipm.2019.102147 | Information Processing & Management |
Keywords | Field | DocType |
Bayesian statistics,Social unrest,Machine learning,Prediction | Data science,Open data,Data mining,Social media,Computer science,Unrest,Bayesian probability | Journal |
Volume | Issue | ISSN |
57 | 2 | 0306-4573 |
Citations | PageRank | References |
1 | 0.36 | 0 |
Authors | ||
7 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| jonathan tuke | 1 | 4 | 2.49 |
| Andrew Nguyen | 2 | 1 | 1.37 |
| Mehwish Nasim | 3 | 14 | 3.65 |
| Drew Mellor | 4 | 1 | 0.36 |
| Asanga Wickramasinghe | 5 | 1 | 0.36 |
| nigel g bean | 6 | 47 | 10.77 |
| Lewis Mitchell | 7 | 155 | 17.70 |