This year, Facebook acquired Bloomsbury AI, a London-based artificial intelligence startup specializing in \u201ctrawling\u201d documents for patterns and relationships\u2014a signal that it views AI and machine learning as a critical component in its fake news arsenal [3].<\/p>\n
In the near term, however, the scope of machine-learning\u2019s impact on the challenge of fake news has been and will continue to be limited by factors such as the immaturity of natural-language processing technologies and the breadth of contextual data required to evaluate credibility.<\/p>\n
The language problem is exacerbated by the widespread use of irony\u2014a universal sore thumb for machine learning technology\u2014and even \u201cadversarial\u201d writing, whereby fake news authors actively obscure the intended message of their articles to avoid detection by algorithm. In the style guide for Nazi publication The Daily Stormer, founder Andrew Anglin writes, \u201cThe unindoctrinated should not be able to tell if we are joking or not\u201d [4].<\/p>\n
On the credibility side, a 2018 Georgia Tech Research Institute study concluded that \u201cWhile modeling text content of articles is sufficient to identify bias, it is not capable of determining credibility.\u201d Unlike bias, which was reliably estimated by the presence of certain key words, credibility assessment was \u201chighly tailored to the specific conspiracy theories found in the training set\u201d [5].<\/p>\n
Given these limitations, it is not surprising that Facebook has continued to utilize human eyes by designing a platform that feeds suspicious stories\u2014many of which were flagged by algorithms\u2014to a dashboard viewed by human fact-checkers at third-party accredited publications and internally [6]. Also unsurprising is that it tends to hire content reviewers not for their technical skills, but for their language expertise [7].<\/p>\n
Based on the evaluations by fact-checkers, Facebook will display stories flagged as false lower on people\u2019s newsfeeds, rather than removing them altogether [8].<\/p>\n
Going forward<\/strong><\/p>\n With advances in natural language processing, it is likely that machine learning technologies can assume more of the burden of identifying fake news, but Facebook should take several more active steps to meet the challenge of a growing and evolving fake news universe.<\/p>\n Through initiatives like the Facebook Journalism Project and the News Integrity Initiative, Facebook has already begun collaboration with news organizations, technology companies, and academic institutions aimed at helping individuals make more informed decisions about news consumption and funding research, projects, and products related to news veracity [9]. While this is valuable work, Facebook should aim to explicitly address the challenge of identifying credibility through article content by leading a coordinated effort to consolidate credible news data as training data for machine learning algorithms. Whereas previously, articles could be flagged for their similarity to established conspiracies, this data set might allow articles to be flagged for their deviation from established facts.<\/p>\n Outside of machine learning, Facebook has supplemented its team of trained fact-checkers by facilitating user<\/em>\u2019s ability to flag posts as suspicious. A limitation to this approach is the \u201cecho-chamber\u201d effect, whereby users are more frequently shown posts with messages with which they are likely to agree, and thus less likely to report. A solution could be to provide alternative newsfeed views through which users can read content that has not already been filtered to their preferences. While these user flags are not a substitute for professional fact-checking, like machine learning, they could help narrow the funnel of articles that fact-checkers must evaluate.<\/p>\n Open question<\/strong><\/p>\n With so many advances in AI and machine learning still to come, it is difficult to predict how Facebook and other content hosts will fare in the battle against fake news. A concerning trend is the use of machine learning to create<\/em> fake news content. How might Facebook and machine learning address this phenomenon?<\/p>\n Word Count: 793<\/strong><\/p>\n 1<\/sup> Alexis C Madrigal, \u201cInside Facebook’s Fast-Growing Content-Moderation Effort<\/em>,\u201d The Atlantic,\u00a0 February 7, 2018, https:\/\/www.theatlantic.com\/technology\/archive\/2018\/02\/what-facebook-told-insiders-about-how-it-moderates-posts\/552632<\/a>, accessed November 2018.<\/p>\n 2<\/sup> Alexandra Stevenson<\/a>, \u201cSoldiers in Facebook\u2019s War on Fake News Are Feeling Overrun<\/em>,\u201d New York Times,\u00a0 October 9, 2018, https:\/\/www.nytimes.com\/2018\/10\/09\/business\/facebook-philippines-rappler-fake-news.html<\/a>, accessed November 2018.<\/p>\n 3<\/sup> Margi<\/a> Murphy, \u201cFacebook buys British artificial intelligence company Bloomsbury<\/em>,\u201d The Telegraph, July 2, 2018, https:\/\/www.telegraph.co.uk\/technology\/2018\/07\/02\/facebook-buys-british-artificial-intelligence-company-bloomsbury\/<\/a>, accessed November 2018.<\/p>\n 4<\/sup> Andrew Marantz, \u201cInside the Daily Stormer\u2019s Style Guide<\/em>,\u201d The New Yorker, January 15, 2018, https:\/\/www.newyorker.com\/magazine\/2018\/01\/15\/inside-the-daily-stormers-style-guide<\/a>, accessed November 2018.<\/p>\n 5<\/sup> James Fairbanks, Natalie Fitch, Nathan Knauf, and Erica Briscoe. 2018. Credibility<\/p>\n Assessment in the News: Do we need to read?. In Proceedings of<\/p>\n WSDM workshop on Misinformation and Misbehavior Mining on the Web<\/p>\n (MIS2). ACM, New York, NY, USA, 8 pages. https:\/\/doi.org\/10.475\/123_4<\/a><\/p>\n \u00a0<\/em><\/p>\n 6 <\/sup>Madrigal<\/p>\n 7 <\/sup>Ibid.<\/p>\n 8<\/sup> Mike Ananny, \u201cChecking in with the Facebook fact-checking partnership<\/em>,\u201d Columbia Journalism Review, April 4, 2018, https:\/\/www.cjr.org\/tow_center\/facebook-fact-checking-partnerships.php<\/a>, accessed November 2018.<\/p>\n 9<\/sup> Adam Mosseri, \u201cWorking to Stop Misinformation and False News<\/em>,\u201d <\/em>Facebook for Media, April 7, 2017, https:\/\/www.cjr.org\/tow_center\/facebook-fact-checking-partnerships.php<\/a>, accessed November 2018.<\/p>\n
![\"\"](\"https:\/\/d3.harvard.edu\/platform-rctom\/wp-content\/uploads\/sites\/4\/2018\/11\/enhanced-buzz-13362-1395069380-20.jpeg\")
<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"