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Research Spending & Results

Award Detail

Awardee:NEW YORK UNIVERSITY
Doing Business As Name:New York University
PD/PI:
  • John T Jost
  • (212) 998-2121
  • jj54@nyu.edu
Co-PD(s)/co-PI(s):
  • Richard Bonneau
  • Jonathan Nagler
  • Joshua Tucker
Award Date:09/25/2012
Estimated Total Award Amount: $ 999,997
Funds Obligated to Date: $ 1,199,319
  • FY 2012=$999,997
  • FY 2015=$199,322
Start Date:09/15/2012
End Date:08/31/2017
Transaction Type:Grant
Agency:NSF
Awarding Agency Code:4900
Funding Agency Code:4900
CFDA Number:47.075
Primary Program Source:040100 NSF RESEARCH & RELATED ACTIVIT
Award Title or Description:INSPIRE: Computer Learning of Dynamical Systems to Investigate Cognitive and Motivational Effects of Social Media Use on Political Participation
Federal Award ID Number:1248077
DUNS ID:041968306
Parent DUNS ID:041968306
Program:Political Science
Program Officer:
  • Brian Humes
  • (703) 292-7284
  • bhumes@nsf.gov

Awardee Location

Street:70 WASHINGTON SQUARE S
City:NEW YORK
State:NY
ZIP:10012-1019
County:New York
Country:US
Awardee Cong. District:10

Primary Place of Performance

Organization Name:New York University
Street:6 Washington Place
City:New York
State:NY
ZIP:10003-6603
County:New York
Country:US
Cong. District:12

Abstract at Time of Award

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.

Publications Produced as a Result of this Research

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Brady, W.J., Wills, J., Jost, J.T., Tucker, J., & Van Bavel, J.J. "Emotion shapes the diffusion of moralized content in social networks." Proceedings of the National Academy of Sciences., v.114, 2017, p.7313. doi:10.1073/pnas.1618923114 

Barberá, P., Wang, N., Bonneau, R. Jost, J.T., Nagler, J., Tucker, J.A., & González-Bailón, S. "The critical periphery in the growth of social protests." PLoS ONE, v.10, 2015, p.e0143611. doi:10.1371/journal.pone.0143611 

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.

Vaccari, C., Valeriani, A., Barberá, P., Jost, J.T., Nagler, J., & Tucker, J.A. "Of echo chambers and contrarian clubs: Exposure to political disagreement among German and Italian users of Twitter." Social Media and Society, v., 2016, p.http://jo. doi:10.1177/2056305116664221 

Vaccari, C., Valeriani, A., Barberá, P., Bonneau, R., Jost, J.T., Nagler, J., & Tucker, J.A. "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. doi:10.1111/jcc4.12108 

Barberá, P., Wang, N., Bonneau, R. Jost, J.T., Nagler, J., Tucker, J.A., & González-Bailón, S. "The critical periphery in the growth of social protests." PLoS ONE., v.10, 2015, p.e0143611. doi:10.1371/journal.pone.0143611 

? Barberá, P., Jost, J.T., Nagler, J., Tucker, J.A., & Bonneau, R. (2015). "Tweeting from left to right: Is online political communication more than an echo chamber?" Psychological Science., v.26, 2015, p.1531.

Barberá, Pablo "Birds of the Same Feather Tweet Together: Bayesian Ideal Point Estimation Using Twitter Data." Political Analysis, v.23, 2015, p.76.

Vaccari, C., Valeriani, A., Barberá, P., Bonneau, R., Jost, J.T., Nagler, J., & Tucker, J. "Social media and political communication: A survey of Twitter users during the 2013 Italian general election." Rivista Italiana di Scienza Politica/Italian Political Science Review, v.XLIII, 2013, p.381. doi:10.1426/75245 

?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. doi:10.1426/75245 

Metzger, M., Tucker, J.A., Nagler, J. & Bonneau, R. "Tweeting Identity? Ukrainian, Russian and #EuroMaidan.?" The Journal of Comparative Economics., v.44, 2016, p.16. doi:10.1016/j.jce.2015.12.004 

Vaccari, C., Valeriani, A., Barberá, P., Bonneau, R., Jost, J.T., Nagler, J., & Tucker, J.A. "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.

Metzger, M., Bonneau, R., Nagler, J., & Tucker, J.A. "Tweeting identity? Ukrainian, Russian, and #EuroMaidan" Journal of Comparative Economics, v.44, 2016, p.16. doi:10.1016/j.jce.2015.12.004 

Barberá, P., Jost, J.T., Nagler, J., Tucker, J.A., & Bonneau, R. "Tweeting from left to right: Is online political communication more than an echo chamber?" Psychological Science., v.26, 2015, p.1531. doi:10.1177/0956797615594620 


Project Outcomes Report

Disclaimer

This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.

The goal of this project was to forge an interdisciplinary collaboration to examine the impact of social media on political behavior. We created a laboratory at New York University composed of faculty and students from psychology, political science, computer science, and systems biology. Working together, we tested fundamental hypotheses about how, why, and when social media affects citizens' cognitions and motivations with respect to political participation. We adapted sophisticated computational methods of approximate inference and machine learning to evaluate behavioral models using extremely large social media and social network datasets. 

In one project, we analyzed three datasets tracking protest communication on Twitter in different languages and political contexts and employed a network decomposition technique to examine their hierarchical structure. We found that peripheral members of the network are critical in increasing the reach of protest messages and generating online content at levels that are comparable to core participants. An analysis of two other datasets unrelated to mass protests revealed that core-periphery dynamics play a distinctive and powerful role in collective action.

In another study, we analyzed the contents of 600,000 messages sent by 8,000 Twitter users during the lead-up to an Occupy Wall Street demonstration and observed that social identification and political ideology were robust predictors of protest participation, whereas expressions of self-interest and anger were either negatively related or unrelated to participation. This work illustrates the promise of using automated methods to analyze new data sources for studying protest activity and its motivational underpinnings.

To investigate the role of emotion in the social transmission of moral ideas, we analyzed 500,000 Twitter messages concerning three polarizing moral/political issues and observed that the presence of moral-emotional words in messages increased their diffusion by a factor of 20% for each additional word. Furthermore, we found that moral contagion was bounded by group membership; moral-emotional language increased diffusion more strongly within liberal and conservative networks, and less between them.

In another project, we estimated ideological preferences of 3.8 million Twitter users and, using a dataset of 150 million tweets concerning 12 political and non-political issues, we observed that information was exchanged primarily among individuals with similar ideological preferences for political issues (e.g., presidential election, government shutdown) but not for many other current events (e.g., Boston marathon bombing, Super Bowl). Discussion of the Newtown shootings in 2012 reflected a dynamic process, beginning as a “national conversation” before being transformed into a polarized exchange. We conclude that previous work may have overestimated the degree of ideological segregation in social media usage.

We also examined the effects of online social networks on protest participation following the Charlie Hebdo attack in France. Using the geolocation feature included in Twitter metadata, we identified 750 people who were present for the protest and compared them with a comparison group of people who were also in Paris and tweeted using Charlie Hebdo related hashtags but were not present at the protest. For both groups, we created “two-hop” networks (people followed by our protestors/non-protestors, and people followed by those people); this resulted in a network with 130 million Twitter users. We obtained strong evidence that protesters were more likely to be connected to other protesters than members of the comparison group were to each other.

This research enhanced interdisciplinary training for 7 postdoctoral researchers, 25 doctoral students, 4 masters’ students, 15 undergraduate students, and 1 high school student (https://wp.nyu.edu/smapp/people/). Nearly all of our publications were co-authored with graduate students, postdocs, and/or international collaborators. We developed a number of machine learning tools for analyzing social media data that have been shared publicly with the research community, including: data base architecture for storing and managing Twitter data; an original “Tweet coder” for manual annotation of tweet content that interacts with the data base for storing Twitter data; R “libraries” for the analysis of Twitter data; and a tool for scraping Facebook data from public pages. Our code has been made public on github (https://github.com/SMAPPNYU), and we continue to develop a suite of Python tools (smappPY, pysmap, smappdragon, and the smapp-toolkit) to facilitate the collection and analysis of social media data; collectively, these packages have been downloaded over 150,000 times. An R library (streamR) developed by one of the lab’s graduate students (Pablo Barberá, now an Assistant Professor at the London School of Economics) has been downloaded 100,000 times.

The results of our research have been disseminated widely not only in presentations to universities, academic conferences, policy institutes, public venues, and think tanks (including several international keynote addresses), but also in interviews, blog posts, and articles published in the Washington Post, the Monkey Cage, and numerous other magazines (including a special report on technology and politics in The Economist), newspapers, and websites. 

 


Last Modified: 12/11/2017
Modified by: John T Jost

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