Abstract
Nowadays, many social media platforms are centered around content creators (CC). On these platforms, the tie formation process depends on two factors: (a) the exposure of users to CCs (decided by, e.g., a recommender system), and (b) the following decision-making process of users. Recent research studies underlined the importance of content quality by showing that under exploratory recommendation strategies, the network eventually converges to a state where the higher the quality of the CC, the higher their expected number of followers. In this paper, we extend prior work by (a) looking beyond averages to assess the fairness of the process and (b) investigating the importance of exploratory recommendations for achieving fair outcomes. Using an analytical approach, we show that non-exploratory recommendations converge fast but usually lead to unfair outcomes. Moreover, even with exploration, we are only guaranteed fair outcomes for the highest (and lowest) quality CCs.
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Notes
- 1.
Although, in principle, on influencer-centered platforms, e.g., YouTube, any user could create content, reports show that only a small number of users do so [25]. This also means that in practice CCs can also follow other CCs. However, since they are relatively few compared to non-CCs, we can opt for simplicity and ignore such edges. Our results can be generalised though when allowing for such edges and thus not restricting the network to a bipartite structure. A more detailed discussion on this issue can be found in [4].
- 2.
Throughout the paper we use \(\overline{k}\) to denote the set of non-zero natural numbers that are at most equal to k, i.e. \(\overline{k} := \{1, 2, \dots k\}\).
- 3.
Note that under full generality each user receives a list of recommendations. However, allowing for a single recommendation per user makes our model comparable with [4].
- 4.
Note that this is a weak version of the fairness definition. Alternatively, we can say a network A is fair if \(a_{., 1}> a_{., 2}> \dots > a_{., n}\). However, as we will see later in the results section, as the number of users goes to infinity the chance of achieving equality goes to zero. So, in the limit, the two definitions are equivalent.
- 5.
Note that the two preliminary results are independent on the RS.
- 6.
This can be easily shown for \(c= 4n/(n-1)\) by using Lemma 1 to prove that \(p_{B^*, S\cup S^*}\ge 1/2n\) (although better bounds can be obtained).
- 7.
“Although, on average, quality is positively related to success, songs of any given quality can experience a wide range of outcomes” (from [7], p. 855).
References
Bakshy, E., Rosenn, I., Marlow, C., Adamic, L.: The role of social networks in information diffusion. In: Proceedings of the 21st international conference on World Wide Web, pp. 519–528 (2012)
Hall, W., Tinati, Ramine, Jennings, W.: From Brexit to Trump: social media’s role in democracy. Computer 51(1), 18–27 (2018)
Stacy Adams, J.: Towards an understanding of inequity. J Abnormal Social Psychol. 67(5), 422 (1963)
Pagan, N., Mei, W., Li, C., Dörfler, F.: A meritocratic network formation model for the rise of social media influencers. Nature Commun. 12(1), 1–12 (2021)
Zipf, G.K.: Human behavior and the principle of least effort: an introduction to human ecology. Ravenio Books (2016)
Vany, A.D.: Hollywood economics: how extreme uncertainty shapes the film industry. Routledge (2003)
Salganik, M.J., Dodds, P.S., Watts, D.J.: Experimental study of inequality and unpredictability in an artificial cultural market. Science 311(5762), 854–856 (2006)
McNee, S.M., Riedl, J., Konstan, J.A.: Being accurate is not enough: how accuracy metrics have hurt recommender systems. In: CHI’06 Extended Abstracts on Human Factors in Computing Systems, pp. 1097–1101 (2006)
Kunaver, M., Požrl, T.: Diversity in recommender systems-a survey. Knowl. Syst. 123, 154–162 (2017)
Helberger, N., Karppinen, Kari, D’Acunto, L.: Exposure diversity as a design principle for recommender systems. Inf. Commun. Soc. 21(2), 191–207 (2018)
Gravino, P., Monechi, B., Loreto, V.: Towards novelty-driven recommender systems. Comptes Rendus Physique 20(4), 371–379 (2019)
Guo, W., Krauth, K.,Jordan, M., Garg, N.: The stereotyping problem in collaboratively filtered recommender systems. In: Equity and Access in Algorithms, Mechanisms, and Optimization, pp. 1–10. ACM (2021)
Myerson, R.B.: Utilitarianism, egalitarianism, and the timing effect in social choice problems. Econometrica J. Econ. Soc. 883–897 (1981)
Chaney, A.G.B., Stewart, B.M., Engelhardt, B.E.: How algorithmic confounding in recommendation systems increases homogeneity and decreases utility. In: Proceedings of the 12th ACM Conference on Recommender Systems, pp. 224–232 (2018)
Erdös, P., Rényi, Alfréd: On random graphs, i. Publicationes Mathematicae (Debrecen) 6, 290–297 (1959)
Watts, D.J., Strogatz, S.H.: Collective dynamics of “small-world” networks. Nature 393(6684), 440 (1998)
Barabási, A., Albert, R.: Emergence of scaling in random networks. Science 286(5439), 509–512 (1999)
Snijders, T.A.B.: Stochastic actor-oriented models for network change. J. Math. Sociol. 21(1–2), 149–172 (1996)
Jackson, M.O.: Social and Economic Networks. Princeton University Press (2010)
Verma, S., Rubin, J.: Fairness definitions explained. In: 2018 IEEE/ACM International Workshop on Software Fairness (Fairware), pp. 1–7. IEEE (2018)
Garg, P., Villasenor, J., Foggo, V.: Fairness metrics: a comparative analysis. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 3662–3666. IEEE (2020)
Mitchell, S., Potash, E., Barocas, S., D’Amour, A., Lum, K.: Algorithmic fairness: choices, assumptions, and definitions. Annual Rev. Stat. Appl. 8, 141–163 (2021)
Binns, R.: On the apparent conflict between individual and group fairness. In: Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency, pp. 514–524 (2020)
Lucherini, E., Sun, M., Winecoff, A., Narayanan, A.: T-recs: a simulation tool to study the societal impact of recommender systems (2021). arXiv:2107.08959
Nielsen, J.: Participation inequality: lurkers vs. contributors in internet communities. Jakob Nielsen’s Alertbox 107, 108 (2006)
Acknowledgements
The authors gratefully acknowledge financial support from the University of Zürich and the SNSF under the NCCR Automation grant (Grant Number 180545). We also thank the anonymous reviewers for their valuable feedback.
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Ionescu, S., Pagan, N., Hannák, A. (2023). Individual Fairness for Social Media Influencers. In: Cherifi, H., Mantegna, R.N., Rocha, L.M., Cherifi, C., Miccichè, S. (eds) Complex Networks and Their Applications XI. COMPLEX NETWORKS 2016 2022. Studies in Computational Intelligence, vol 1077. Springer, Cham. https://doi.org/10.1007/978-3-031-21127-0_14
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