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Title:Uncovering hidden behavioral patterns in the era of “we media”: Modeling spatio-temporal dynamics for twitter news
Author(s):Huang, Hong; Yu, Han; Andrews, James E.; Yoon, JungWon; Burgess, Kelsey L.
Subject(s):Twitter news
Genetic test
Gene patent
Twitter geolocations
Twitter timestamp
Abstract:This research presents a Bayesian statistical model to examine spatio-temporal effects for Twitter use when reporting important events or news. The proposed model tests the Twitter News data surrounding the United States Supreme Court’s Myriad Genetics, Inc. June 13, 2013 decision and its impact on direct-to-consumer genetic testing and gene patenting. The model demonstrates the sensitivity in distinguishing the behaviours of Twitter users’ followers with and without adjusting spatio-temporal effects. It was also found that media professionals’ tweets were coming thick and quick, and producing “waves” of engagement of followers. However, grassroots actively participate in tweeting and constantly engage more followers. The model maps tweets across the spatial heterogeneity and temporal evolution in the early and post recognition and discussion of events. These findings demonstrate the importance of spatio-temporal effects to influence professionals or non-professionals for tweeting. The model also guided researchers to detect sub-events with low latency.
Issue Date:2017
Citation Info:Huang, H., Yu, H., Andrews, J. E., Yoon, Y., & Burgess, K. L. (2017). Uncovering Hidden Behavioral Patterns in the Era of “We Media”: Modeling Spatio-Temporal Dynamics for Twitter News. In iConference 2017 Proceedings (pp. 704-707).
Series/Report:iConference 2017 Proceedings
Genre:Conference Poster
Rights Information:Copyright 2017 Hong Huang, Han Yu, James E. Andrews, JungWon Yoon, and Kelsey L. Burgess
Date Available in IDEALS:2017-07-27

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