Transcription of Machine Learning for Detection of Fake News
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Machine Learning for Detection of Fake News
Machine Learning for Detection ofFake NewsbyNicole O BrienSubmitted to the Department of Electrical Engineering andComputer Sciencein partial fulfillment of the requirements for the degree ofMaster of Engineering in Electrical Engineering andComputer Scienceat theMassachusetts Institute of TechnologyJune 2018c Massachusetts Institute of Technology 2018. All rights author hereby grants to permission to reproduce and to distributepublicly paper and electronic copies of this thesis document in whole and in partin any medium now known or hereafter :Department of Electrical Engineering and Computer ScienceMay, 17, 2018 Certified by:Tomaso PoggioEugene McDermott Professor, BCS and CSAILT hesis SupervisorAccepted by:Katrina LaCurtsChairman, Masters of Engineering Thesis CommitteeMachine Learning for Detection of Fake NewsbyNicole O BrienSubmitted to the Department of Electrical Engineering andComputer Science on May 1y, 2018, in partial fulfillment of therequirements for the degree of Masters of Engineering in ElectricalEngineering and Computer ScienceAbstractRecent political events have lead to an increase in the popularity and spread offake news .
news and still classi es correctly, despite removal of the pattern which caused the Cleaning Step 2 model from Figure 9.4 to fail. . . . . . .52 9.5 This demonstrates an interesting correctly classi ed Fake News Ar-ticles. For real news trigrams, the model picks up a time reference, \past week\, and mathematical/technical phrases such as \analyze
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