Measuring Perceived Causal Relationships Between Narrative Events with a Crowdsourcing Application on Mturk


The computational study of narrative is important to multiple academic disciplines. However, prior research has been limited by the inability to quantify each subject’s comprehension of the causal structure. With the aid of big data technology and crowdsourcing tools, we aim to design a new approach to analyze the content of narratives in a data-driven manner, while also making these analyses scientifically replicable. The goal of this research is therefore to develop a method that can be used to measure people’s understanding of the causal relationships within a piece of text.

Lee D., Lin YR., Osgood N., Thomson R. (eds) Social, Cultural, and Behavioral Modeling. SBP-BRiMS 2017. Lecture Notes in Computer Science, vol 10354. Springer, Cham

Dian Hu
Ph.D. Candidate

Dian Hu is a Ph.D. candidate in the Systems Engineering program at the George Washington University’s School of Engineering and Applied Science. Dian earned his Bachelor of Science degree in Systems Engineering from the GW with honors. In his first job before graduate study, Dian worked on several projects involving data visualization, data migration and web-based application development. Dian’s academic interests are Big Data and Health, Rumor Spreading Mechanisms, Influenza, Risky Decision Models, Machine Learning, and Natural Language Processing.