Abstract
Influencer fraud, which can significantly damage authentic influencers and companies, has become one of the most important problems that can adversely affect the influencer marketing industry. Fraudulent influencers obtain fake engagements on their posts by purchasing engagement bots that automatically generate likes and comments. To identify bots that make fake engagements to influencers, we perform an in-depth analysis on the social network of influencer engagements, which consists of 14,221 influencers, 9,290,895 users, and 65,848,717 engagements. We find that bots tend to have low local clustering coefficients and write short comments which are similar to each other. Based on the analysis results of the unique engagement behavior of bots, we propose a neural network-based model that learns text, behavior, and graph representations of social media users to detect the engagement bots from audiences of influencers. The experimental results show that the proposed model outperforms well-known baseline methods by achieving 80% accuracy.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Arenas-Marquez, F.J., Martínez-Torres, M.R., Toral, S.: Electronic word-of-mouth communities from the perspective of social network analysis. Technol. Anal. Strateg. Manag. 26(8), 927–942 (2014)
Bahdanau, D., Cho, K., Bengio, Y.: Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473 (2014)
Chawla, N.V., Bowyer, K.W., Hall, L.O., Kegelmeyer, W.P.: Smote: synthetic minority over-sampling technique. J. Artif. Intell. Res. 16, 321–357 (2002)
De Veirman, M., Cauberghe, V., Hudders, L.: Marketing through instagram influencers: the impact of number of followers and product divergence on brand attitude. Int. J. Advert. 36(5), 798–828 (2017)
Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: Bert: pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018)
Ferrara, E., Varol, O., Davis, C., Menczer, F., Flammini, A.: The rise of social bots. Commun. ACM 59(7), 96–104 (2016)
Hoffman, D.L., Fodor, M.: Can you measure the ROI of your social media marketing? MIT Sloan Manag. Rev. 52(1), 41 (2010)
Kim, S., Han, J., Yoo, S., Gerla, M.: How are social influencers connected in instagram? In: Ciampaglia, G.L., Mashhadi, A., Yasseri, T. (eds.) SocInfo 2017. LNCS, vol. 10540, pp. 257–264. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-67256-4_20
Kim, S., Jiang, J.Y., Nakada, M., Han, J., Wang, W.: Multimodal post attentive profiling for influencer marketing. In: Proceedings of The Web Conference 2020, pp. 2878–2884 (2020)
Kudugunta, S., Ferrara, E.: Deep neural networks for bot detection. Inf. Sci. 467, 312–322 (2018)
Marciano, J.: The real economic losses from influencer fraud. Marketing Technology Insights (2019)
Perozzi, B., Al-Rfou, R., Skiena, S.: Deepwalk: online learning of social representations. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 701–710 (2014)
Rios, S.A., Aguilera, F., Nuñez-Gonzalez, J.D., Graña, M.: Semantically enhanced network analysis for influencer identification in online social networks. Neurocomputing 326, 71–81 (2019)
Schuchard, R., Crooks, A., Stefanidis, A., Croitoru, A.: Bots in nets: empirical comparative analysis of bot evidence in social networks. In: Aiello, L.M., Cherifi, C., Cherifi, H., Lambiotte, R., Lió, P., Rocha, L.M. (eds.) COMPLEX NETWORKS 2018. SCI, vol. 813, pp. 424–436. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-05414-4_34
Varol, O., Ferrara, E., Davis, C.A., Menczer, F., Flammini, A.: Online human-bot interactions: detection, estimation, and characterization. In: Eleventh International AAAI Conference on Web and Social Media (2017)
Wang, G., Konolige, T., Wilson, C., Wang, X., Zheng, H., Zhao, B.Y.: You are how you click: clickstream analysis for sybil detection. In: Presented as part of the 22nd \(\{\)USENIX\(\}\) Security Symposium (\(\{\)USENIX\(\}\) Security 2013), pp. 241–256 (2013)
Yang, X., Kim, S., Sun, Y.: How do influencers mention brands in social media? Sponsorship prediction of instagram posts. In: Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 101–104 (2019)
Acknowledgements
This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2018R1D1A1A02085647).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Kim, S., Han, J. (2020). Detecting Engagement Bots on Social Influencer Marketing. In: Aref, S., et al. Social Informatics. SocInfo 2020. Lecture Notes in Computer Science(), vol 12467. Springer, Cham. https://doi.org/10.1007/978-3-030-60975-7_10
Download citation
DOI: https://doi.org/10.1007/978-3-030-60975-7_10
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-60974-0
Online ISBN: 978-3-030-60975-7
eBook Packages: Computer ScienceComputer Science (R0)