A data-driven study of temporal adverbials as discourse segmentation markers

Abstract

The increased chance of deception in computer-mediated communica- tion and the potential risk of taking action based on deceptive information calls for automatic detection of deception. To achieve the ultimate goal of automatic predic- tion of deception, we selected four common classification methods and empirically compared their performance in predicting deception. The deception and truth data were collected during two experimental studies. The results suggest that all of the four methods were promising for predicting deception with cues to deception. Among them, neural networks exhibited consistent performance and were robust across test settings. The comparisons also highlighted the importance of selecting important in- put variables and removing noise in an attempt to enhance the performance of classi- fication methods. The selected cues offer both methodological and theoretical contributions to the body of deception and information systems research