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MEASURING INFLUENCE ON INSTAGRAM: A NETWORK-OBLIVIOUS APPROACH 4 linear combination of values in the training set and while this result in a better ranker, the predicted value more often overshoots. Due to resource and time constraints we ran the PageR- ank algorithm a subset of 10% of the users, resulting in an rs score of 0.673. These results, only better then the followers baseline, are to be expected given Instagram’s flow of information, as discussed in Section 2. TABLE 1 R2 and rs statistics for regression models [8] [9] [10] [11] [12] [13] [14] [15] [16] [17] [18] [19] [20] [21] [22] [23] [24] [25] [26] [27] [28] [29] [30] [31] [32] D. Brown and S. Fiorella, Influence marketing: How to create, manage, and measure brand influencers in social media marketing. Que Publishing, 2013. N. Zietek, “Influencer marketing: the characteristics and compo- nents of fashion influencer marketing,” 2016. A. Ha, “An experiment: Instagram marketing techniques and their effectiveness,” 2015. Y. Bijen, “# ad: The effects of an influencer, comments and prod- uct combination on brand image.” Master’s thesis, University of Twente, 2017. N. Lisichkova and Z. Othman, “The impact of influencers on online purchase intent,” 2017. A. Richardson, O. Ganz, and D. Vallone, “The cigar ambassador: how snoop dogg uses instagram to promote tobacco use,” Tobacco control, pp. tobaccocontrol–2013, 2013. V. P. Cornet and N. K. Hall, “Instagram power users and their effect on social movements,” 2016. Y. Chen, “The rise of ’micro-influencers’ on instagram,” 2016. N. Barbieri, F. Bonchi, and G. Manco, “Influence-based network- oblivious community detection,” in Data Mining (ICDM), 2013 IEEE 13th International Conference on. IEEE, 2013, pp. 955–960. S. Budalakoti and R. Bekkerman, “Bimodal invitation-navigation fair bets model for authority identification in a social network,” in Proceedings of the 21st International Conference on World Wide Web, ser. WWW ’12, 2012, pp. 709–718. C.-J. Hsieh, M. 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Available: ”http://sparxoo.com/2009/10/01/ differing- approaches- to- social- influence” L.Lu ̈,D.Chen,X.-L.Ren,Q.-M.Zhang,Y.-C.Zhang,andT.Zhou, “Vital nodes identification in complex networks,” Physics Reports, vol. 650, pp. 1–63, 2016. M.Cha,H.Haddadi,F.Benevenuto,andP.K.Gummadi,“Measur- ing user influence in twitter: The million follower fallacy.” Icwsm, vol. 10, no. 10-17, p. 30, 2010. D. Chen, L. Lu ̈, M.-S. Shang, Y.-C. Zhang, and T. Zhou, “Identi- fying influential nodes in complex networks,” Physica a: Statistical mechanics and its applications, vol. 391, no. 4, pp. 1777–1787, 2012. I.AngerandC.Kittl,“Measuringinfluenceontwitter,”inProceed- ings of the 11th International Conference on Knowledge Management and Knowledge Technologies. ACM, 2011, p. 31. N. Agarwal, H. Liu, L. Tang, and P. S. Yu, “Identifying the influential bloggers in a community,” in Proceedings of the 2008 international conference on web search and data mining. ACM, 2008, pp. 207–218. M. Bianchini, M. Gori, and F. Scarselli, “Inside pagerank,” Trans- actions on Internet Technology (TOIT), vol. 5, no. 1, pp. 92–128, Feb. 2005. S. Brin and L. Page, “The anatomy of a large-scale hypertextual web search engine,” in Proceedings of the Seventh International Conference on World Wide Web 7, ser. WWW7, 1998, pp. 107–117. J. Weng, E.-P. Lim, J. Jiang, and Q. He, “Twitterrank: finding topic- sensitive influential twitterers,” in Proceedings of the third ACM international conference on Web search and data mining. ACM, 2010, pp. 261–270. A. Khrabrov and G. Cybenko, “Discovering influence in com- munication networks using dynamic graph analysis,” in Social Computing (SocialCom), 2010 IEEE Second International Conference on. IEEE, 2010, pp. 288–294. S. Budalakoti and R. Bekkerman, “Bimodal invitation-navigation fair bets model for authority identification in a social network,” Regression Multi-Regression R2 rs R2 rs full Ridge Regression full Random Forest minimal Ridge Regression minimal Random Forest Followers Baseline Likes Baseline 0.725 0.626 0.723 0.616 0.211 0.666 0.848 0.727 0.869 0.621 0.818 0.727 0.864 0.611 0.757 0.204 0.859 0.654 0.821 0.861 0.818 0.859 0.725 0.853 7 CONCLUSIONS AND FUTURE WORK This work focused on measuring influence and influencer ranking on Instagram, a content sharing OSN. Our def- inition of influence (Def. 3.1) and the features extracted from public information allowed us to use out-of-the-box regression models to create what is, to our knowledge, the first influence ranking algorithm based on an intuitive score derived from network-oblivious statistics. We have shown general truths regarding Instagram such that the commonly sought out audience size is a poor metric for influence. In our work, we did not consider the temporal nature of influence, i.e., the influence of a user is likely to change over time. The rate of change may even depend on the influence itself, as per the rich get richer phenomenon [38]. Lastly, only simple user and posts statistics were used in this work. We believe the use of more complex features would result in stronger models and a better ranking algo- rithm. These features can be post specific, from the simple ”day of the week” to complex ”contains faces” [39], [40], user specific, e.g. the user’s age or common content type [41], [42], or features relating to a user’s audience, such as audience location or age [43], [44]. REFERENCES [1] R. M. Morgan and S. D. Hunt, “The commitment-trust theory of relationship marketing,” The journal of marketing, pp. 20–38, 1994. [2] R. Scoble and S. Israel, Naked conversations: how blogs are changing the way businesses talk with customers. John Wiley & Sons, 2005. [3] A. Al-Bahrani and D. Patel, “Incorporating twitter, instagram, and facebook in economics classrooms,” The Journal of Economic Education, vol. 46, no. 1, pp. 56–67, 2015. [4] T. Gegenhuber and L. Dobusch, “Making an impression through openness: how open strategy-making practices change in the evolution of new ventures,” Long Range Planning, vol. 50, no. 3, pp. 337–354, 2017. [5] A. B. KURULTAY, “Dynamics of viral advertising,” The Turkish Online Journal of Design Art and Communication, vol. 2, no. 2, 2012. [6] S. Wilde, Viral marketing within social networking sites: the creation of an effective viral marketing campaign. Diplomica Verlag, 2013. [7] M. Rakic ́ and B. Rakic ́, “Viral marketing.” Ekonomika, vol. 60, no. 4, 2014.PDF Image | MEASURING INFLUENCE ON INSTAGRAM: A NETWORK-OBLIVIOUS APPROACH
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