Natural Language Processing Seminar|
Dialogue-based Deception Detection
Master Student - “Brain and Cognitive Science” Program
University of Amsterdam
Wednesday, June 20, 2018|
11:00am - 12:00pm
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About the Event
Previous research has shown that humans are not better than random guessing at detecting lies. However, recent research has shown, that automatic methods can increase deception detection accuracies. This is partly due to machine learning techniques and increasing amounts of behavioral data availability. Multimodal deception detection is an approach that combines various data sources, such as language, speech or visual cues (facial expression, gestures etc.) in an attempt to merge the individual advances in deception detection in the corresponding fields. This approach was able to show, that with the joint use of multiple modalities, automatic deception detection accuracies can be improved. Everyday deception often takes place within dialogues, but this setting stills remains largely unexplored. In this talk, I will describe my current research on expanding previous multimodal deception detection techniques onto a dialogue setting. This project attempts to incorporate the dimension of the dialogue interaction into the current multimodal deception detection techniques. While the project is ongoing, the presentation will cover methodological aspects related to data collection, annotation, and preprocessing as well as a first glimpse into classification performances on deceit detection in dialog settings.
Felix Soldner is a Master Student at the University of Amsterdam in the “Brain and Cognitive Science” program. He is currently doing an internship at the University of Michigan with the Language and Information Technologies group. His background lies in the intersection of psychology and biomimetics. His research interests are in deception detection and crime science. He has interned previously at the “lie-lab” in the psychology department in Amsterdam. During that time, he worked on a project that examined automated methods of detecting fake online reviews. He compared different classification methods and the impact of data collecting procedures on their performances. Felix enjoys working on interdisciplinary topics, especially examining psychological phenomena with methods from computer science.
Faculty Sponsor: Rada Mihalcea
Open to: Public