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Award Abstract #1248077

INSPIRE: Computer Learning of Dynamical Systems to Investigate Cognitive and Motivational Effects of Social Media Use on Political Participation

Divn Of Social and Economic Sciences
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Initial Amendment Date: September 25, 2012
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Latest Amendment Date: September 22, 2015
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Award Number: 1248077
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Award Instrument: Standard Grant
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Program Manager: Brian D. Humes
SES Divn Of Social and Economic Sciences
SBE Direct For Social, Behav & Economic Scie
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Start Date: September 15, 2012
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End Date: August 31, 2016 (Estimated)
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Awarded Amount to Date: $1,199,319.00
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Investigator(s): John Jost john.jost@nyu.edu (Principal Investigator)
Richard Bonneau (Co-Principal Investigator)
Jonathan Nagler (Co-Principal Investigator)
Joshua Tucker (Co-Principal Investigator)
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Sponsor: New York University
NEW YORK, NY 10012-1019 (212)998-2121
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Cyber-Human Systems (CHS),
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Program Reference Code(s): 0000, 8653, 9179, OTHR
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Program Element Code(s): 1332, 1371, 1640, 7367, 7953, 8078


This INSPIRE award is partially funded by Human-Centered Computing Program and by Social-Computational Systems Program both in the Division of Information and Intelligent Systems in the Directorate for Computer & Information Science & Engineering, and by the Social Psychology Program in the Division of Behavioral and Cognitive Sciences and the Political Science Program in the Division of Social and Economic Sciences in the Directorate for Social, Behavioral and Economic Sciences.

With regards to intellectual merit, the goal of this project is to forge an interdisciplinary collaboration that examines the impact of social media on political behavior. First, from social psychology and political science, fundamental hypotheses will be developed about how, why and when social media affects citizens' cognition and motivation with respect to political participation. Second, these questions will be expressed as testable hypotheses derived from behavioral models. And third, drawing from biology and computer science, the project adapts sophisticated computational methods of approximate inference and machine learning (adapting methods developed for the analysis of Systems Biology data) to evaluate the behavioral models using extremely large social media and social network datasets.

The scientific opportunities afforded by the use of social media are readily apparent when we consider the richness and precision of data on participation in elections, protests, riots, and other spontaneous political events. This project will construct a comprehensive data set of incoming and outgoing social media messages messages using systematically structure formats that are ideally suited to machine learning methods, and this information will be integrated with information on social network connectivity and a vast array of metadata on individuals and their social contacts. By developing new methods to harvest and combine these data sources effectively, it will be possible to transform the scientific study of social and political attitudes and behavior. Every time individuals use social media, they leave behind a digital footprint of what was communicated, when it was communicated, and, to whom it was communicated. Typically, such precise estimates of these variables are available only to laboratory investigators working in artificial settings. No previous study has successfully used fine-grained social influence data such as these to predict consequential behavioral outcomes, such as attendance at a given protest or rally. The structure of the data means that we will have panel data on respondents, many of potentially long duration. In addition, the investigators will conduct a panel survey, which is essential for drawing causal inferences about the cognitive and motivational processes whereby social media use facilitates political participation.

With regards to broader impacts, this research will enhance interdisciplinary training for graduate and undergraduate students. These include students in psychology, political science, computer science, and biology and also includes students from groups that are underrepresented in these sciences. In addition, opportunities will be provided for high school students to become involved in the research process. The research program will foster broad dissemination of scientific understanding by leveraging past experience of the principal investigators with disseminating large code-bases, data-bases, and data-sets to share work with other scientists (pre-publication). Finally, the researchers are committed to making their research available to the general public and have extensive experience doing so.


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Vaccari, C., Valeriani, A., Barberá, P., Bonneau, R., Jost, J.T., Nagler, J., & Tucker, J.A. (2015).. "Political expression and action on social media: Exploring the relationship between lower- and higher-threshold political activities among Twitter users in Italy.," Journal of Computer-Mediated Communication., v.20, 2015, p. 221.

?Cristian Vaccari, Augusto Valeriani, Pablo Barberá, Richard Bonneau, John T. Jost, and Jonathan Nagler, and Joshua Tucker.. "Social Media and Political Communication: A survey of Twitter users during the 2013 Italian general election.," Italian Political Science Review., v.XLIII, 2014, p. 381. 


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