Towards a New Linguistic Model for Detecting Political Lies

The present study addresses the problem of how the two US presidential candidates Donald Trump and Hillary Clinton use statements judged to be false by the Politifact site while delivering their campaign speeches. Two corpora of Clinton’s and Trump’s alleged lies were compiled. Each corpus contained 16 statements judged to be false or ridiculously untrue (‘pants on fire’) by the Pulitzer Prize Winner site Politifact. Some statements were accompanied by the video recordings where they appeared; others had no video recordings affiliated because they are either tweets or their events had not been recorded on Youtube or elsewhere. The present research made use of CBCA (Criteria-based Content Analysis) but as a stepping stone for building a new model of detecting lies in political discourse to suit the characteristics of campaign discourse. This furnished the qualitative dimension of the research. As for the quantitative dimension, data were analyzed using software, namely LIWC (Linguistic Inquiry & Word Count), and also focused on the content analysis of the deception cues that can be matched with the results obtained from computerized findings. When VSA (Voice Stress Analysis) was required, Praat was used. Statistical analyses were occasionally applied to reach highly accurate results. The study concluded that the New Model (NM) is not context-sensitive, being a quantitative one, and is thus numerically oriented in its decisions. Moreover, when qualitative analysis intervenes, especially in examining Politifact rulings, context plays a crucial role in passing judgements on deceptive vs. non-deceptive discourse