Name
Abstract | ||
|---|---|---|
In this paper, we address the task of cross-cultural deception detection. Using crowdsourcing, we collect three deception datasets, two in English (one originating from United States and one from India), and one in Spanish obtained from speakers from Mexico. We run comparative experiments to evaluate the accuracies of deception classifiers built for each culture, and also to analyze classification differences within and across cultures. Our results show that we can leverage cross-cultural information, either through translation or equivalent semantic categories, and build deception classifiers with a performance ranging between 60-70%. |
Year | Venue | Field |
|---|---|---|
2014 | PROCEEDINGS OF THE 52ND ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, VOL 2 | Deception,Computer science,Crowdsourcing,Cross-cultural,Artificial intelligence,Natural language processing,Machine learning |
DocType | Volume | Citations |
Conference | P14-2 | 9 |
PageRank | References | Authors |
0.58 | 10 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Verónica Pérez-Rosas | 1 | 40 | 5.02 |
| Rada Mihalcea | 2 | 6460 | 445.54 |