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

Astroturfing is a phenomenon in which sponsors of fake messages or reviews are masked because their intentions are not genuine.Astroturfing reviews are intentionally made to influence people to take decisions in favour of or against a target service or product or organization.The tourism sector being one of the sectors that is flourishing and witnessing unprecedented growth is affected by the activities of astroturfers.Astroturfing reviews can cause many problems to tourists who make decisions based on available online reviews.However,authentic and genuine reviews help people make informed decisions.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.