TSDL: A Framework for Tip Spam Detection in Location Based Social Network.
| dc.contributor.author | Adewole, K.S. | |
| dc.contributor.author | Isiaka, R.M. | |
| dc.contributor.author | Jimoh, R.G. | |
| dc.contributor.author | Ajiboye, A.R. | |
| dc.date.accessioned | 2023-05-12T14:26:35Z | |
| dc.date.available | 2023-05-12T14:26:35Z | |
| dc.date.issued | 2017 | |
| dc.description.abstract | In Web 2.0 systems, Location Based Social Network (LBSN) has become increasingly popular because of its ability for locating places, such as restaurants, hotels and nearby facilities that can render services to the users. Among such LBSN is Apontador, a popular social network introduced in Brazil a couple of years ago. However, despite the numerous opportunities offered by Apontador LBSN, it has become attractive social network for tips spam distribution. In this work, an intelligent machine learning framework is proposed to detect tips spam in Apontador LBSN. The proposed framework explored three classification algorithms: Random Forest, Multilayer Perceptron, and Decorate. Bio-inspired feature identification was studied using Evolutionary algorithm (EA) and Particle Swamp Optimization (PSO) to identify the discriminating features for tip spam detection in LBSN. Three experiments were conducted based on sixty (60), twenty-two (22), and ten (10) features to ascertain the performance of the proposed framework for tip spam detection. Based on the various experiments conducted, Random Forest classification algorithm produced accuracy and F-measure of 90.5% and ROC of 96.7% using 60 features extracted from Apontador LBSN. The algorithm also outperformed the two other classifiers during EA evaluation. However, Decorate classifier produced the best results during the PSO evaluation, achieving F-measure and ROC of 86.3% and 92.9% respectively. The experimental results show that the proposed framework improved in performance for tips spam detection in Apontador LBSN. | en_US |
| dc.identifier.uri | https://uilspace.unilorin.edu.ng/handle/20.500.12484/10205 | |
| dc.language.iso | en | en_US |
| dc.publisher | Nigeria Computer Society | en_US |
| dc.relation.ispartofseries | Journal of Information Security, Privacy and Digital Forensic;1(1); 1-9 | |
| dc.subject | Location based social network; tip spam; evolutionary computation; machine learning. | en_US |
| dc.title | TSDL: A Framework for Tip Spam Detection in Location Based Social Network. | en_US |