The phenomenon ofFake News, aimed at voters in the Presidential Election of 2016 is examined from the standpoint of voting results, demographics ofvoters usage of social media as source of news, slanting statistics of FakeNews on social media platforms and dataon voters propensity or inclination tobe swayed by Fake News. A mathematical model is derived to estimate how voteswere thus swung to both Hillary Clinton and Donald Trump by idealogues andentrepeneurs (and possibly Russia). The model focuses initially on theaggregation of votes needed to swing theElectoral College votes from Michigan, Wisconsin and Pennsylvania but uses anationwide proportionality analysis for the eventual model. The end result isconclusive. Fake News led to the Electoral College Victory of DonaldTrump. A remarkable numerical resultfrom the model is that the percentage of swing votes lost toClinton (1.07 %), (based onthe reported values of 17% of social media readers having a propensity toswitch after reading Fake News), when added to her actual popular vote marginof 2.22%, equals a margin of 3.29%. This compares to the aggregateor average final poll prediction results of 3.3% for Clinton reported by RealClear Politics . So as not to rely on a single estimate of propensity toswitch, a parametric analysis isconducted varying this factor. Theanalysis shows that this factor can be as low as 2.4% and still be sufficient to switch 379,000votes nationally, the amount needed proportionally to swing the 39,000 votes inMichigan, Wisconsin and Pennsylvania that gave Trump the Electoral Collegevictory.