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A pair of recent papers highlights how today’s social networks not only reflect societal biases, but can actually amplify them.<\/p>\n\t\t\t\tA pair of recent papers highlights how today’s social networks not only reflect societal biases, but can actually amplify them.<\/p>\n
Ana-Andreea Stoica<\/a> et. al.’s Algorithmic glass ceiling in social networks: the effects of social recommendations on network diversity<\/a>\u00a0looks at the effect of “social recommendations” such as friend suggestions and people to follow, both at the theoretical level and empirically on Instagram.\u00a0\u00a0 The authors find that “prominent social recommendation algorithms can exacerbate the under-representation of certain demographic groups at the top of the social hierarchy.”\u00a0 More specifically:<\/p>\n 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 colour from attaining equal representation.<\/p>\n One would a priori<\/em> expect similarity metrics, usually the basis of recommender systems, to contribute to sustaining disparities among various groups. We show much more: using empirical evidence from newly collected data on Instagram and a rigorous analysis of mathematical models, we prove that prominent recommender algorithms reinforce the rate at which disparity grows<\/em>.<\/p><\/blockquote>\n The first couple of sections of the paper are a quick read, after which it gets into some heavy-duty math.\u00a0\u00a0 Fortunately, Kim Martineau’s How Social Networking Sites May Discriminate Against Women<\/a> on\u00a0Columbia News,<\/em>\u00a0is a good summary; and Adrian Collyer, on the ACM’s The morning paper<\/em><\/a>, walks through the paper in detail.<\/p>\n The underlying dynamic here of homophily<\/a> — <\/em>people’s tendency to prefer to interact with people similar to themselves — isn’t new.\u00a0 Neither is the idea of a “glass ceiling” in social media,*\u00a0 or realization that algorithmic recommendations reflect societal biases.**\u00a0\u00a0 What’s important about this paper is both the formal model and the experimental results showing bias amplification<\/em>.<\/p>\n Meanwhile,\u00a0Nikki Usher et al<\/span>‘s<\/span> Twitter Makes It Worse: Political Journalists, looks at “beltway journalists\u2019 peer-to-peer relationships on Twitter\u2014or how journalists use the platform to legitimate, amplify, and engage each other,” and similarly finds substantial evidence of gender bias.\u00a0 In particular:<\/a><\/p>\n Most alarming is that male journalists amplify and engage male peers almost exclusively, while female journalists tend to engage most with each other.\u00a0 The significant support for claims of gender asymmetry as well as evidence of gender silos are findings that not only underscore the importance of further research but also suggest overarching consequences for the structure of contemporary political communication. Hey wait a second, I’m noticing a pattern here!<\/p>\n <\/p>\n * see for example Susan Herring et. al.’s classic 2003 paper Women and children last: the discursive construction of Weblogs<\/a> and Shirin Nilizadeh et. al.’s 2016 Twitter’s Glass Ceiling: The Effect of Perceived Gender on Online Visibility<\/a>.<\/p>\n ** recent books like Dr. Safiya Umoja Noble’s Algorithms of Oppression: How Search Engines Reinforce Racism<\/a>\u00a0 and Virginia Eubanks’ Automating Inequality<\/a> have plenty of examples; here’s a 2011 post from me focusing on TechMeme<\/a>‘s recommendation algorithms<\/p>\n <\/p>\n \n<\/div>\n","protected":false},"excerpt":{"rendered":" \t\t\t\tToday’s social networks magnify societal biases.\t\t<\/p>\n","protected":false},"author":1,"featured_media":810,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[419,13],"tags":[443,325,446],"class_list":["post-780","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-diversity","category-social-computing","tag-research","tag-social-networks","tag-sof"],"_links":{"self":[{"href":"https:\/\/2024.thenexus.today\/index.php\/wp-json\/wp\/v2\/posts\/780","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/2024.thenexus.today\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/2024.thenexus.today\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/2024.thenexus.today\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/2024.thenexus.today\/index.php\/wp-json\/wp\/v2\/comments?post=780"}],"version-history":[{"count":0,"href":"https:\/\/2024.thenexus.today\/index.php\/wp-json\/wp\/v2\/posts\/780\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/2024.thenexus.today\/index.php\/wp-json\/"}],"wp:attachment":[{"href":"https:\/\/2024.thenexus.today\/index.php\/wp-json\/wp\/v2\/media?parent=780"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/2024.thenexus.today\/index.php\/wp-json\/wp\/v2\/categories?post=780"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/2024.thenexus.today\/index.php\/wp-json\/wp\/v2\/tags?post=780"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}
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