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Chasm in Hegemony: Explaining and Reproducing Disparities in Homophilous Networks

Published:04 June 2021Publication History
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Abstract

In networks with a minority and a majority community, it is well-studied that minorities are under-represented at the top of the social hierarchy. However, researchers are less clear about the representation of minorities from the lower levels of the hierarchy, where other disadvantages or vulnerabilities may exist. We offer a more complete picture of social disparities at each social level with empirical evidence that the minority representation exhibits two opposite phases: at the higher rungs of the social ladder, the representation of the minority community decreases; but, lower in the ladder, which is more populous, as you ascend, the representation of the minority community improves. We refer to this opposing phenomenon between the upper-level and lower-level as the chasm effect. Previous models of network growth with homophily fail to detect and explain the presence of this chasm effect. We analyze the interactions among a few well-observed network-growing mechanisms with a simple model to reveal the sufficient and necessary conditions for both phases in the chasm effect to occur. By generalizing the simple model naturally, we present a complete bi-affiliation bipartite network-growth model that could successfully capture disparities at all social levels and reproduce real social networks. Finally, we illustrate that addressing the chasm effect can create fairer systems with two applications in advertisement and fact-checks, thereby demonstrating the potential impact of the chasm effect on the future research of minority-majority disparities and fair algorithms.

References

  1. Lada A Adamic, Bernardo A Huberman, AL Barabási, R Albert, H Jeong, and G Bianconi. 2000. Power-law distribution of the world wide web. science, Vol. 287, 5461 (2000), 2115--2115.Google ScholarGoogle Scholar
  2. Noga Alon and Joel H Spencer. 2004. The probabilistic method .John Wiley & Sons.Google ScholarGoogle ScholarCross RefCross Ref
  3. Chen Avin, Barbara Keller, Zvi Lotker, Claire Mathieu, David Peleg, and Yvonne-Anne Pignolet. 2015. Homophily and the glass ceiling effect in social networks. In Proceedings of the 2015 conference on innovations in theoretical computer science. 41--50.Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. Mahmoudreza Babaei, Abhijnan Chakraborty, Juhi Kulshrestha, Elissa M Redmiles, Meeyoung Cha, and Krishna P Gummadi. 2019. Analyzing biases in perception of truth in news stories and their implications for fact checking. In Proceedings of the Conference on Fairness, Accountability, and Transparency. 139--139.Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. Albert-Laszlo Barabâsi, Hawoong Jeong, Zoltan Néda, Erzsebet Ravasz, Andras Schubert, and Tamas Vicsek. 2002. Evolution of the social network of scientific collaborations. Physica A: Statistical mechanics and its applications, Vol. 311, 3--4 (2002), 590--614.Google ScholarGoogle Scholar
  6. Rashmi Pankajai Bomiriya. 2014. Topics in exponential random graph modeling. (2014).Google ScholarGoogle Scholar
  7. Stephen P Borgatti and Martin G Everett. 1997. Network analysis of 2-mode data. Social networks, Vol. 19, 3 (1997), 243--270.Google ScholarGoogle Scholar
  8. Stéphane Boucheron, Gábor Lugosi, and Pascal Massart. 2013. Concentration inequalities: A nonasymptotic theory of independence .Oxford university press.Google ScholarGoogle Scholar
  9. Carlos Castillo, Marcelo Mendoza, and Barbara Poblete. 2011. Information credibility on twitter. In Proceedings of the 20th international conference on World wide web. 675--684.Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. Fan Chung, Fan RK Chung, Fan Chung Graham, Linyuan Lu, Kian Fan Chung, et al. 2006. Complex graphs and networks. Number 107. American Mathematical Soc.Google ScholarGoogle Scholar
  11. David A Cotter, Joan M Hermsen, Seth Ovadia, and Reeve Vanneman. 2001. The glass ceiling effect. Social forces, Vol. 80, 2 (2001), 655--681.Google ScholarGoogle Scholar
  12. Vanessa Wei Feng and Graeme Hirst. 2013. Detecting deceptive opinions with profile compatibility. In Proceedings of the Sixth International Joint Conference on Natural Language Processing. 338--346.Google ScholarGoogle Scholar
  13. Kiran Garimella and Dean Eckles. 2020. Images and Misinformation in Political Groups: Evidence from WhatsApp in India. arXiv preprint arXiv:2005.09784 (2020).Google ScholarGoogle Scholar
  14. Kiran Garimella and Gareth Tyson. 2018. WhatsApp, doc? A first look at WhatsApp public group data. arXiv preprint arXiv:1804.01473 (2018).Google ScholarGoogle Scholar
  15. Minyoung Huh, Andrew Liu, Andrew Owens, and Alexei A Efros. 2018. Fighting fake news: Image splice detection via learned self-consistency. In Proceedings of the European Conference on Computer Vision (ECCV). 101--117.Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. Matthieu Latapy, Clémence Magnien, and Nathalie Del Vecchio. 2008. Basic notions for the analysis of large two-mode networks. Social networks, Vol. 30, 1 (2008), 31--48.Google ScholarGoogle Scholar
  17. Yang Liu and Yi-Fang Brook Wu. 2018. Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In Thirty-Second AAAI Conference on Artificial Intelligence .Google ScholarGoogle ScholarCross RefCross Ref
  18. Miller McPherson, Lynn Smith-Lovin, and James M Cook. 2001. Birds of a feather: Homophily in social networks. Annual review of sociology, Vol. 27, 1 (2001), 415--444.Google ScholarGoogle Scholar
  19. Laurie A Morgan. 1998. Glass-ceiling effect or cohort effect? A longitudinal study of the gender earnings gap for engineers, 1982 to 1989. American sociological review (1998), 479--493.Google ScholarGoogle Scholar
  20. Vahed Qazvinian, Emily Rosengren, Dragomir Radev, and Qiaozhu Mei. 2011. Rumor has it: Identifying misinformation in microblogs. In Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing. 1589--1599.Google ScholarGoogle Scholar
  21. Zhaopeng Qu and Zhong Zhao. 2017. Glass ceiling effect in urban China: Wage inequality of rural-urban migrants during 2002--2007. China Economic Review, Vol. 42 (2017), 118--144.Google ScholarGoogle ScholarCross RefCross Ref
  22. Herbert Robbins and David Siegmund. 1971. A convergence theorem for non negative almost supermartingales and some applications. In Optimizing methods in statistics. Elsevier, 233--257.Google ScholarGoogle Scholar
  23. Kai Shu, Amy Sliva, Suhang Wang, Jiliang Tang, and Huan Liu. 2017. Fake news detection on social media: A data mining perspective. ACM SIGKDD explorations newsletter, Vol. 19, 1 (2017), 22--36.Google ScholarGoogle ScholarDigital LibraryDigital Library
  24. Ana-Andreea Stoica, Jessy Xinyi Han, and Augustin Chaintreau. 2020. Seeding Network Influence in Biased Networks and the Benefits of Diversity. In Proceedings of The Web Conference 2020. 2089--2098.Google ScholarGoogle ScholarDigital LibraryDigital Library
  25. Ana-Andreea Stoica, Christopher Riederer, and Augustin Chaintreau. 2018. Algorithmic Glass Ceiling in Social Networks: The effects of social recommendations on network diversity. In Proceedings of the 2018 World Wide Web Conference. 923--932.Google ScholarGoogle ScholarDigital LibraryDigital Library
  26. Zhi-Qiang You, Xiao-Pu Han, Linyuan Lü, and Chi Ho Yeung. 2015. Empirical studies on the network of social groups: the case of Tencent QQ. PLoS One, Vol. 10, 7 (2015), e0130538.Google ScholarGoogle ScholarCross RefCross Ref

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  1. Chasm in Hegemony: Explaining and Reproducing Disparities in Homophilous Networks

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