Paper: Group topic-author model for efficient discovery of latent social astroturfing groups in tourism domain

Mar 25, 2019 | Muhmmad A. Kazim, Noora H. Mohammed

Link:

https://link.springer.com/article/10.1186/s42400-019-0029-8

Publisher:

Cybersecurity

Abstract:

 In this paper a Latent Dirichlet Allocation (LDA) based Group Topic-Author model is proposed for efficient discovery of social astroturfing groups within the tourism domain. An algorithm named Astroturfing Group Topic Detection (AGTD) is defined for the implementation of the proposed model. The experimental results of this study revealed the utility of the proposed system for the discovery of social astroturfing groups within the tourism domain.