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An Analysis of Deceptive Text Using Techniques of Machine Learning, Corpus Generation, and Online Crowdsourcing

Creative Commons 'BY-NC-ND' version 4.0 license

This research demonstrates how to use deep learning techniques alongside corpus generation and online crowdsourcing in order to better understand deceptive text. In this dissertation, I use state-of-the-art classifiers to examine the structure of deceptive text and determine what parts mark it as deceptive. I also expand knowledge of deception into new areas by adding to the knowledge base of deceptive text with large amounts of curated, realistic data. It also offers a more complete understanding by examining deceptive text through multiple lenses. My research accomplishes this through three interrelated projects: (I) The construction of a new state-of-the-art classifier, and modifying the input to the classifier to examine what the classifier considers most informative in a classification, (II) the creation of a new corpus of deceptive text, the Motivated Deception Corpus, which uses gameifying techniques to improve the quality of deceptive text samples by making them more realistic through competition, and (III) a human subject study on Amazon Mechanical Turk, where I observe what samples humans consider deceptive or truthful and use a Cultural Consensus Theory model to identify what prompts a subject to decide one way or the other.

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