Evaluating Audience Loyalty and Authenticity in Influencer Marketing via Multi-task Multi-relational Learning

Authors

  • Seungbae Kim Department of Computer Science, University of California, Los Angeles
  • Xiusi Chen Department of Computer Science, University of California, Los Angeles
  • Jyun-Yu Jiang Department of Computer Science, University of California, Los Angeles
  • Jinyoung Han Department of Applied Artificial Intelligence, Sungkyunkwan University
  • Wei Wang Department of Computer Science, University of California, Los Angeles

Keywords:

Ranking/relevance of social media content and users, Social network analysis; communities identification; expertise and authority discovery, Centrality/influence of social media publications and authors, Trust; reputation; recommendation systems

Abstract

Since influencer marketing has become an essential marketing method, influencer fraud behavior such as buying fake followers and engagements to manipulate the popularity is under the spotlight. To address this issue, we propose a multi-task audience evaluation model that can assess both the loyalty and authenticity of influencers’ audiences. More specifically, the proposed model takes engagement information of an influencer’s audience, including likes and comments on social media posts, and predicts (i) the retention rate of the audience of the influencer and (ii) how the influencer is associated with fake audiences (or engagement bots). To learn the social interaction between influencers and their audiences, we build multi-relational networks based on the diverse engagement behavior such as commenting. Our model further utilizes the contextualized information captured in user comments to learn distinct engagement behavior of genuine and fake users. Based on the predicted loyalty and authenticity scores, we rank influencers to find those who are followed by loyal and authentic audiences. By using a large-scale Instagram influencer-audience dataset which contains 14,221 influencers, 9,290,895 audiences, and 65,848,717 engagements, we evaluate ranking performance, and show that the proposed framework outperforms other baseline methods.

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Published

2021-05-22

How to Cite

Kim, S., Chen, X., Jiang, J.-Y., Han, J., & Wang, W. (2021). Evaluating Audience Loyalty and Authenticity in Influencer Marketing via Multi-task Multi-relational Learning. Proceedings of the International AAAI Conference on Web and Social Media, 15(1), 278-289. Retrieved from https://ojs.aaai.org/index.php/ICWSM/article/view/18060