We all have seen them: 鈥渘ews鈥�, ads, chain messages. They are scattered with content supposedly selected for us: unreliable news, rumors, malicious texts try to divide communities, cities, nations and the world. Fake news is content created maliciously with the […]
A exploration of the arms race to use Machine Learning by both those creating and stopping fake news.
The proliferation of misinformation, or fake news, on social media platforms has become a serious problem. Facebook – largely considered to be the main culprit in this controversy – is leaning heavily on machine learning to fight off this issue.
This paper will focus on Facebook鈥檚 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 鈥渇ake news鈥�, 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鈥檚 mission to create a constructive community that brings people together to create positive experiences. False news is 鈥渉armful to [their] community鈥� and 鈥渕akes the world less informed鈥� which inherently 鈥渆rodes trust鈥� with its users. In this context, using machine learning and other statistical tools to identify inaccurate and manipulated information is paramount to Facebook鈥檚 efforts to combat the spread of such information.
