Sentiment Analysis to Enhance Detection of Latent Astroturfing Groups in Online Social Networks

  • Noora AlallaqEmail author
  • Muhmmad Al-khiza’ayEmail author
  • Mohammed Iqbal Dohan
  • Xin Han
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 950)


Astroturfing is a sponsored and organized campaign for making a favor to the sponsor by the act of expressing an opinion via reviews on a product or service. Such reviews are misleading and cause potential risk to the public in general as there has been increasing trend of making decisions based on the online reviews. In this paper, we proposed an enhanced LDA to have a novel model known as Latent Group Detective Model (LGDM) which is meant for solving the problem of astroturfing group detection. This model is based on the fact that there is a difference between a generative process of normal review and a potential astroturfing review. LDGM accommodate a label for finding sentiment associated with reviews to have the better analysis of AGs. Four cases are considered for AG analysis. Case 1 observes the groups as they are discovered. Case 2 performs opposite sentiment analysis. Case 3 focuses on high sentiment reviews while case 4 deals with change of sentiment of potential AGs across products or services.


Online reviews Astroturfing Astroturfing group LGDM Sentiment analysis 


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Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.School of Information TechnologyDeakin UniversityBurwoodAustralia
  2. 2.Department of Multimedia, College of Computer Science and Information TechnologyUniversity of Al-QadisiyahDiwaniyahIraq
  3. 3.College of Computer ScienceXi’an Shiyou UniversityXi’anChina

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