Ana-Andreea Stoica
ABSTRACT
As social recommendations such as friend suggestions and people to follow become increasingly popular and influential on the growth of social media, we find that prominent social recommendation algorithms can exacerbate the under-representation of certain demographic groups at the top of the social hierarchy. To study this imbalance in online equal opportunities, we leverage new Instagram data and offer for the first time an analysis that studies the effect of gender, homophily and growth dynamics under social recommendations. Our mathematical analysis demonstrates the existence of an algorithmic glass ceiling that exhibits all the properties of the metaphorical social barrier that hinders groups like women or people of color from attaining equal representation. What raises concern is that our proof shows that under fixed minority and homophily parameters the algorithmic effect is systematically larger than the glass ceiling generated by the spontaneous growth of social networks. We discuss ways to address this concern in future design.
- C Avin, B Keller, Z Lotker, C Mathieu, and David Peleg. 2015. Homophily and the glass ceiling effect in social networks. ITCS (2015). Google Scholar
Digital Library
- S Barocas and A D Selbst. 2016. Big data's disparate impact. California Law Review (2016).Google Scholar
- Tolga Bolukbasi, Kai-Wei Chang, James Y Zou, Venkatesh Saligrama, and Adam T Kalai. 2016. Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings. Advances in Neural Information Processing Systems (NIPS) (2016), 4349--4357. Google ScholarDigital Library
- Alexandra Chouldechova. 2017. Fair Prediction with Disparate Impact: A Study of Bias in Recidivism Prediction Instruments. Big data 5, 2 (2017), 153--163.Google Scholar
- Y Dong, R A Johnson, J Xu, and N V Chawla. 2017. Structural Diversity and Homophily: A Study Across More Than One Hundred Big Networks. In KDD '17. ACM Press, New York, New York, USA, 807--816. Google ScholarDigital Library
- Maeve Duggan. 2015. Mobile Messaging and Social Media 2015. Pew Research Center (2015).Google Scholar
- Benjamin G Edelman, Michael Luca, and Dan Svirsky. 2017. Racial Discrimination in the Sharing Economy: Evidence from a Field Experiment. American Economic Journal: Applied Economics 9, 2 (2017), 1--22.Google ScholarCross Ref
- A Hannak, C Wagner, D Garcia, Alan Mislove, Markus Strohmaier, and Christo Wilson. 2017. Bias in Online Freelance Marketplaces: Evidence from TaskRabbit and Fiverr. Proceedings of CSCW (2017). Google ScholarDigital Library
- Ravi Kumar, Prabhakar Raghavan, S Rajagopalan, D Sivakumar, Andrew Tomkins, and E Upfal. 2000. Stochastic models for the Web graph. IEEE FOCS (2000), 57--65. Google ScholarDigital Library
- Michael Ley. 2009. DBLP: some lessons learned. Proceedings of the VLDB Endowment 2, 2 (2009), 1493--1500. Google ScholarDigital Library
- David Liben-Nowell and Jon Kleinberg. 2007. The link-prediction problem for social networks. journal of the Association for Information Science and Technology 58, 7 (2007), 1019--1031. Google ScholarDigital Library
- Miller McPherson, Lynn Smith-Lovin, and James M Cook. 2001. Birds of a Feather: Homophily in Social Networks. Annual review of sociology 27 (2001), 415--444.Google Scholar
- Alan Mislove, S Lehmann, Y Y Ahn, and J-P Onnela. 2011. Understanding the Demographics of Twitter Users. In ICWSM.Google Scholar
- Shirin Nilizadeh, Anne Groggel, Peter Lista, Srijita Das, Yong-Yeol Ahn, Apu Kapadia, and Fabio Rojas. 2016. Twitter's Glass Ceiling: The Effect of Perceived Gender on Online Visibility.. In ICWSM. 289--298.Google Scholar
- Flávio Souza, Diego de Las Casas, Vinícius Flores, SunBum Youn, Meeyoung Cha, Daniele Quercia, and Virgílio Almeida. 2015. Dawn of the selfie era: The whos, wheres, and hows of selfies on Instagram. In Proceedings of the 2015 ACM on conference on online social networks. ACM, 221--231. Google ScholarDigital Library
- J Su, A Sharma, and S Goel. 2016. The Effect of Recommendations on Network Structure. WWW '18: Proceeding of the 25th International Conference on World Wide Web (2016). Google ScholarDigital Library
- Ke Yang and Julia Stoyanovich. 2016. Measuring Fairness in Ranked Outputs. Proceedings of Workshop FATML (2016).Google Scholar
- Muhammad Bilal Zafar, Isabel Valera, Manuel Gomez Rodriguez, and Krishna Gummadi. 2017. Fairness Beyond Disparate Treatment & Disparate Impact: Learning Classification without Disparate Mistreatment. In WWW '17 Proceedings of the 26th International Conference on World Wide Web. Google ScholarDigital Library
Index Terms
- Algorithmic Glass Ceiling in Social Networks: The effects of social recommendations on network diversity
Recommendations
Investigating Homophily in Online Social Networks
WI-IAT '10: Proceedings of the 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 01Similarity breeds connections, the principle of homophily, has been well studied in existing sociology literature. %Several studies have observed this phenomena by conducting surveys on human subjects. These studies have concluded that new ties are ...
Hegemony in Social Media and the effect of recommendations
WWW '19: Companion Proceedings of The 2019 World Wide Web ConferenceAs today’s media landscape is carved by social media endorsements and built on automated recommendations, both of these are often criticized for inducing vicious dynamics, such as the filter bubble effect, echo chamber, or polarization. We introduce a ...
Racial Homophily and Its Persistence in Newcomers' Social Networks
This study examined the formation and persistence of homophilous, or same-race, friendship ties among racial minorities and whites in a "newcomer" setting. Homophilous ties provide valuable sources of mutual support but may limit racial minorities' ...
Comments