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Automatic fake news detection models are ostensibly based on logic, where thetruth of a claim made in a headline can be determined by supporting or refutingevidence found in a resulting web query. These models are believed to bereasoning in some way; however, it has been shown that these same results, orbetter, can be achieved without considering the claim at all -- only theevidence. This implies that other signals are contained within the examinedevidence, and could be based on manipulable factors such as emotion, sentiment,or part-of-speech (POS) frequencies, which are vulnerable to adversarialinputs. We neutralize some of these signals through multiple forms of bothneural and non-neural pre-processing and style transfer, and find that thisflattening of extraneous indicators can induce the models to actually requireboth claims and evidence to perform well. We conclude with the construction ofa model using emotion vectors built off a lexicon and passed through an"emotional attention" mechanism to appropriately weight certain emotions. Weprovide quantifiable results that prove our hypothesis that manipulablefeatures are being used for fact-checking.

Automatic Fake News Detection: Are current models "fact-checking" or "gut-checking"?