Name
Abstract | ||
|---|---|---|
Customers represent their satisfactions of consuming products by sharing their experiences through the utilization of online reviews. Several machine learning-based approaches can automatically detect deceptive and fake reviews. Recently, there have been studies reporting the performance of ensemble learning-based approaches in comparison to conventional machine learning techniques. Motivated by the recent trends in ensemble learning, this paper evaluates the performance of ensemble learning-based approaches to identify bogus online information. The application of a number of ensemble learning-based approaches to a collection of fake restaurant reviews that we developed show that these ensemble learning-based approaches detect deceptive information better than conventional machine learning algorithms. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/COMPSAC48688.2020.00-73 | 2020 IEEE 44TH ANNUAL COMPUTERS, SOFTWARE, AND APPLICATIONS CONFERENCE (COMPSAC 2020) |
Keywords | DocType | ISSN |
Ensemble learning, deception detection | Conference | 0730-3157 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Luis Gutierrez-Espinoza | 1 | 0 | 0.34 |
| Faranak Abri | 2 | 0 | 1.69 |
| Akbar Siami Namin | 3 | 481 | 37.30 |
| Keith S. Jones | 4 | 6 | 5.85 |
| David Sears | 5 | 1 | 2.80 |