{"id":33670,"date":"2018-11-13T17:06:19","date_gmt":"2018-11-13T22:06:19","guid":{"rendered":"https:\/\/digital.hbs.edu\/platform-rctom\/submission\/a-use-case-for-machine-learning-how-facebook-uses-machine-learning-to-combat-fake-news\/"},"modified":"2018-11-13T17:06:19","modified_gmt":"2018-11-13T22:06:19","slug":"a-use-case-for-machine-learning-how-facebook-uses-machine-learning-to-combat-fake-news","status":"publish","type":"hck-submission","link":"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/a-use-case-for-machine-learning-how-facebook-uses-machine-learning-to-combat-fake-news\/","title":{"rendered":"A Use Case for Machine Learning: How Facebook Uses Machine Learning to Combat Fake News"},"content":{"rendered":"

Machine learning enables companies to organize and analyze an enormous scale and complexity of data. Facebook has 2.27 billion active users generating an incredible amount of data.[1]<\/a> Therefore, machine learning is particularly relevant to Facebook because its gives Facebook the tools to analyze the large volume of posts and determine how relevant they are to individual users. With that information, Facebook can rank relevancy and order of posts that show in a user\u2019s feed, thus creating the most valuable and engaging user experience. More recently, it has become an increasingly important platform for political campaigning. This paper will focus on Facebook\u2019s use of machine learning to manage political content on its site. Today, with platforms like Facebook, content is being generated by a wider range of sources, which has eroded the credibility of the political information on Facebook. Recently, we have seen this occur with the proliferation of \u201cfake news\u201d, specifically falsified political information. This development has significant implications for Facebook and risks alienating its user based which can impact its bottom line and user base. It is Facebook\u2019s mission to create a constructive community that brings people together to create positive experiences. False news is \u201charmful to [their] community\u201d and \u201cmakes the world less informed\u201d which inherently \u201cerodes trust\u201d with its users.[2]<\/a> In this context, using machine learning and other statistical tools to identify inaccurate and manipulated information is paramount to Facebook\u2019s efforts to combat the spread of such information.<\/p>\n

In the near term, Facebook has expanded its fact checking capabilities in order to identify fake news. Facebook uses a combination of machine learning and human labor in order to do this. There is limited information on how exactly Facebook structures its models, but its algorithms likely \u201canalyze the way a [post] is written, and tell you if it\u2019s similar to an article written with little to no biased words, strong adjectives, opinion, or colorful language\u201d.[3]<\/a> This is a complex task because it is very difficult to characterize what defines news as \u201cfake\u201d, as this process entails a high degree of subjectivity. Facebook uses previously<\/em> debunked stories to further inform its algorithms, and allows its users to report fake news as well.[4]<\/a> Once Facebook identifies a potential piece of fake news, it sends it to its network of fact checking partners, such as Schema.org, who then verify it.[5]<\/a> If flagged as fake, Facebook will show that post lower in its users\u2019 news feeds to limit its impact. Facebook will also use machine learning to identify the domains spreading that news and limit their distribution.[6]<\/a> In July of 2018, Facebook acquired Bloomsbury AI whose natural language processing capabilities, it is believed, will be put towards the challenges detailed above.[7]<\/a> In addition, Facebook is partnering with academic institutions to further improve their algorithms.[8]<\/a><\/p>\n

Politically charged content can come from many different sources. It can come from organic user content and from paid sources like true political campaigns. While the former is particularly difficult to manage, I think one area Facebook can improve in the near-to-medium term is increasing transparency among paid political content. In May of 2018, Facebook instituted a mandatory \u201cPaid For\u201d disclosure for any ad relating to politics.[9]<\/a> While this is a step in the right direction, it is clear that Facebook needs to improve their process for approval for these paid ads. Recently, media outlet Vice News conducted an experiment in which it successfully created and ran ads that falsely claimed to be \u201cPaid For\u201d by ISIS, Vice President Mike Pence, and Democratic National Committee Chairman Tom Perez.[10]<\/a> This is a clear indicator that the review and approval process of paid ads needs to be thoroughly audited and improved by Facebook.<\/p>\n

 <\/p>\n

Is the public sector ultimately responsible for regulating political messaging online?<\/p>\n

 <\/p>\n

 <\/p>\n

Word Count: 794<\/p>\n

[1]<\/a> Facebook Newsroom, [https:\/\/newsroom.fb.com\/company-info], accessed November 2018.<\/p>\n

[2]<\/a> Adam Mosseri, \u201cWorking To Stop Misinformation and False News,\u201d Facebook for Media<\/em> (blog), Facebook, April 7, 2017, [https:\/\/www.facebook.com\/facebookmedia\/blog\/working-to-stop-misinformation-and-false-news], accessed November 2018.<\/p>\n

[3]<\/a> Aaron Edell, \u201cI trained fake news detection AI with >95% accuracy, and I almost went crazy,\u201d Towards Data Science<\/em>, January 11, 2018, [https:\/\/towardsdatascience.com\/i-trained-fake-news-detection-ai-with-95-accuracy-and-almost-went-crazy-d10589aa57c], accessed November 2018.<\/p>\n

[4]<\/a> Tessa Lyons, \u201cIncreasing Our Efforts to Fight False News\u201d, Facebook Newsroom <\/em>(blog), June 21, 2018, [https:\/\/newsroom.fb.com\/news\/2018\/06\/increasing-our-efforts-to-fight-false-news\/], accessed November 2018.<\/p>\n

[5]<\/a> ibid<\/p>\n

[6]<\/a> ibid<\/p>\n

[7]<\/a> Patrick Kulp, \u201cFacebook Hires Team Behind AI Startup in Battle Against Fake News\u201d, Adweek, <\/em>July 3, 2018, [https:\/\/www.adweek.com\/digital\/facebook-hires-team-behind-ai-startup-in-battle-against-fake-news\/], accessed November 2018.<\/p>\n

[8]<\/a> Tessa Lyons, \u201cIncreasing Our Efforts to Fight False News\u201d, Facebook Newsroom <\/em>(blog), June 21, 2018, [https:\/\/newsroom.fb.com\/news\/2018\/06\/increasing-our-efforts-to-fight-false-news\/], accessed November 2018.<\/p>\n

[9]<\/a> Sean Wolfe, \u201cFacebook approved 100 fake ad disclosures that were allegedly \u2018paid for\u2019 by every United States senator,\u201d Business Insider<\/em>, October 30, 2018, [https:\/\/www.businessinsider.com\/facebok-fake-ads-election-senators-2018-10], accessed November 2018.<\/p>\n

[10]<\/a> Sean Wolfe, \u201cFacebook approved fake political ads that claimed to be paid for by ISIS and Mike Pence,\u201d Business Insider<\/em>, October 26, 2018, [https:\/\/www.businessinsider.com\/facebook-approved-fake-political-ads-isis-mike-pence-report-2018-10], accessed November 2018.<\/p>\n","protected":false},"excerpt":{"rendered":"

This paper will focus on Facebook\u2019s use of machine learning to manage political content on its site. Today, with platforms like Facebook, content is being generated by a wider range of sources, which has eroded the credibility of the political information on Facebook. Recently, we have seen this occur with the proliferation of \u201cfake news\u201d, specifically falsified political information. This development has significant implications for Facebook and risks alienating its user based which can impact its bottom line and user base. It is Facebook\u2019s mission to create a constructive community that brings people together to create positive experiences. False news is \u201charmful to [their] community\u201d and \u201cmakes the world less informed\u201d which inherently \u201cerodes trust\u201d with its users. In this context, using machine learning and other statistical tools to identify inaccurate and manipulated information is paramount to Facebook\u2019s efforts to combat the spread of such information.<\/p>\n","protected":false},"author":11191,"featured_media":33671,"comment_status":"open","ping_status":"closed","template":"","categories":[298,766,4673,346],"class_list":["post-33670","hck-submission","type-hck-submission","status-publish","has-post-thumbnail","hentry","category-big-data","category-facebook","category-fake-news","category-machine-learning","hck-taxonomy-organization-facebook","hck-taxonomy-industry-technology","hck-taxonomy-country-united-states"],"connected_submission_link":"https:\/\/d3.harvard.edu\/platform-rctom\/assignment\/rc-tom-challenge-2018\/","yoast_head":"\nA Use Case for Machine Learning: How Facebook Uses Machine Learning to Combat Fake News - Technology and Operations Management<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/a-use-case-for-machine-learning-how-facebook-uses-machine-learning-to-combat-fake-news\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"A Use Case for Machine Learning: How Facebook Uses Machine Learning to Combat Fake News - Technology and Operations Management\" \/>\n<meta property=\"og:description\" content=\"This paper will focus on Facebook\u2019s use of machine learning to manage political content on its site. 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