{"id":24353,"date":"2017-11-15T17:31:55","date_gmt":"2017-11-15T22:31:55","guid":{"rendered":"https:\/\/digital.hbs.edu\/platform-rctom\/submission\/black-box-personalization-and-fragmentation-of-the-facebook-news-supply\/"},"modified":"2017-11-15T17:31:55","modified_gmt":"2017-11-15T22:31:55","slug":"black-box-personalization-and-fragmentation-of-the-facebook-news-supply","status":"publish","type":"hck-submission","link":"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/black-box-personalization-and-fragmentation-of-the-facebook-news-supply\/","title":{"rendered":"Black Box Personalization and Fragmentation of the Facebook News Supply"},"content":{"rendered":"

As digitalization of news has become more pervasive across society, Facebook has become the de facto public forum for sharing of media content.\u00a0 As of 2017, 67% of adults get their news from social media[1]<\/a>.\u00a0 From a supply chain lens, one could consider media content Facebook\u2019s \u201ccustomer offering\u201d, and their recommendation algorithms to be the supply chain.\u00a0 Rapid advances in machine learning have made this supply chain more efficient via complex algorithms based on massive data sets, improving Facebook\u2019s ability to connect users with media they choose to engage with.\u00a0 If user engagement is the desired heuristic, these algorithms have unquestionably improved the Facebook user experience.\u00a0 However, there are two negatives to this approach: the unpredictability that comes from \u201cBlack Box\u201d systems, and the creation of \u201cEcho Chambers\u201d as users\u2019 feedback loops silo them into, \u201cSpheres of information where we’re rarely subjected to views that differ from our own.\u201d[2]<\/a><\/p>\n

\"\"<\/a>
http:\/\/graphics.wsj.com\/blue-feed-red-feed\/<\/figcaption><\/figure>\n

 <\/p>\n

Black Box models are those where only the outputs are visible.\u00a0 Facebook\u2019s proprietary news recommendation algorithm is an example.\u00a0 These models trade high performance for a complete lack of explanatory power and oversight: \u201cUsers have no control over what algorithms decide to show us, and little understanding of why they may learn to show you one piece of content over another.\u201d[3]<\/a>\u00a0 In fact, even Facebook employees can\u2019t definitively say what factors led to news being displayed a certain way for a certain person, or why tweaking inputs slightly will change result sets massively.\u00a0 The combination of black box algorithms with the general user\u2019s \u201cHuman tendency to select information that confirms their existing beliefs\u201d[4]<\/a> has a scary result.\u00a0 In addition, research has shown that Facebook exacerbates this problem by restricting \u201ccross-cutting\u201d content, or content from outside one\u2019s political sphere.\u00a0 \u201cAfter ranking [news content], there is on average slightly less crosscutting content\u201d[5]<\/a>. \u00a0Facebook is enabling formation of these echo chambers of like-minded users, without knowledge of how these echo chambers will affect the future profitability of Facebook or human society as a whole.<\/p>\n

There are three actors in this equation: content producers, Facebook, and content consumers.\u00a0 Facebook must address this concern by answering these two questions.\u00a0 How does Facebook interact with the content producers to select which content to show, broadly?\u00a0 How do users interact with Facebook to consume content, specifically?\u00a0 Currently, there is a lack of clarity about Facebook\u2019s role, intentions, and algorithms.\u00a0 To solve this problem going forward, Facebook must provide transparency in all duties they play.<\/p>\n

As \u201cFake News\u201d has entered the collective vocabulary, Facebook\u2019s role in filtering content has been called into question: are they an impartial aggregator that is truly indifferent to content, or an editor, emphasizing \u201cReal News\u201d from trustworthy sources?\u00a0 In the short term, Facebook can build a system to flag and filter fake news articles written by trolls, or any other spam-esque content.\u00a0 In both the short and long, Facebook must define its position to maintain the trust of content generators and consumers.<\/p>\n

The second question Facebook must answer is about its intentions and objectives.\u00a0 As Hosangar wrote, \u201cWhy should Facebook be obligated to show content with which we don’t engage?\u201d[6]<\/a>.\u00a0 Is the echo chamber actually even Facebook\u2019s responsibility to fix?\u00a0 If Facebook does come to the conclusion that this echo chamber is something worth fixing, they can tweak their content delivery system to deliver media content popular in other circles, perhaps communities that have contradictory views.<\/p>\n

The last question Facebook must face is how transparent they should be with the nuts and bolts of their actual recommendation algorithms. \u00a0To this point, the details of the algorithm have been kept under wraps: \u201cIt\u2019s the company\u2019s unique intellectual property, a competitive advantage it can’t expose\u201d[7]<\/a>.\u00a0 However, this is not the only solution.\u00a0 Facebook could use an open-source recommendation algorithm to allow others to peek into the logic behind what they\u2019re seeing.\u00a0 They could give the user more agency by building tools to allow more cross-cutting content to be displayed; potentially only a few users would opt into a more diversified Newsfeed, but it would at least force the user to make a conscious decision.\u00a0 Finally, Facebook could build and share reporting on what people consume, both at the individual (anonymized) and collective level.\u00a0 By taking these steps to ensure transparency in all steps of the content recommendation process, Facebook can ensure all parties have the trust and resources to consume news as they intend, while facilitating the conversation about the place of echo chambers in society.<\/p>\n

 <\/p>\n

(758 Words)<\/p>\n

[1]<\/a> Elisa Sheahrer and Jeffery Gottfried, \u201cNews Use Across Social Media Platforms 2017\u201d, Pew Research Center<\/em>, September 7, 2017, http:\/\/www.journalism.org\/2017\/09\/07\/news-use-across-social-media-platforms-2017\/<\/a>, accessed November 2017<\/p>\n

[2]<\/a> Patrick Kulp, \u201cEscape the echo chamber: How to fix your Facebook News Feed\u201d Mashable<\/em>, November 18, 2017, http:\/\/mashable.com\/2016\/11\/18\/facebook-hacking-newsfeed-well-rounded\/#fex3kZADGqq2<\/a>, accessed November 2017<\/p>\n

[3]<\/a> Christina Bonnington, \u201cIt\u2019s Time for More Transparency in A.I.\u201d Slate<\/em>, October 24, 2017, http:\/\/www.slate.com\/articles\/technology\/technology\/2017\/10\/silicon_valley_needs_to_start_embracing_transparency_in_artificial_intelligence.html<\/a>, accessed November 2017<\/p>\n

[4]<\/a> Alessandro Bessi, \u201cPersonality Traits and Echo Chambers on Facebook\u201d, June 16, 2016, IUSS Institute for Advanced Study<\/em>, https:\/\/arxiv.org\/pdf\/1606.04721.pdf<\/a>, Accessed November 2017<\/p>\n

[5]<\/a> Eytan Bakshy, Solomon Messing, Lata Adamic, \u201cExposure to ideologically diverse news and opinion on Facebook\u201d, Science Magazine<\/em>, Vol 348 Issue 6239 p. 1130, June 5, 2015<\/p>\n

[6]<\/a> Kartik Hosangar, \u201cBlame the Echo Chamber on Facebook.\u00a0 But Blame Yourself, Too\u201d Wired<\/em>, November 11, 2016, https:\/\/www.wired.com\/2016\/11\/facebook-echo-chamber\/<\/a> accessed November 2017<\/p>\n

[7]<\/a> Hosangar<\/p>\n","protected":false},"excerpt":{"rendered":"

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