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Award Detail

Doing Business As Name:Indiana University
  • Byungkyu Lee
  • (812) 855-3430
Award Date:07/22/2021
Estimated Total Award Amount: $ 150,007
Funds Obligated to Date: $ 150,007
  • FY 2021=$150,007
Start Date:09/01/2021
End Date:08/31/2024
Transaction Type:Grant
Awarding Agency Code:4900
Funding Agency Code:4900
CFDA Number:47.075
Primary Program Source:040100 NSF RESEARCH & RELATED ACTIVIT
Award Title or Description:Collaborative Research: How online foci shape conversation
Federal Award ID Number:2116936
DUNS ID:006046700
Parent DUNS ID:006046700
Program Officer:
  • Joseph Whitmeyer
  • (703) 292-7808

Awardee Location

Street:509 E 3RD ST
Awardee Cong. District:09

Primary Place of Performance

Organization Name:Trustees of Indiana University
Street:770 Ballantine, 1020 E. Kirkwood Ave
Cong. District:09

Abstract at Time of Award

This project investigates the characteristics of online communities that shape the quality of their conversations. Social media sites are not homogeneous; they are made up of online “foci” of interaction -- online communities like groups, blogs, and discussion forums characterized by their stability and bounded nature. In certain foci, conversations are friendly and constructive, in others, hostile and unproductive. The goal of this study is to understand how the different characteristics of online foci — their cultures, moderation policies, attitudes towards outsiders, and social relationships — influence the quality of their conversations. Findings will advance scientific knowledge to improve a democratic society and help scholars and practitioners imagine and design a more inclusive and democratic online public sphere. This study leverages a large-scale multilevel dataset that covers all public conversations that unfolded during a major campaign across 1,058 topical forums on a popular platform. The data consist of 1.2 billion comments, 8.2 billion reactions to posts, and 2.6 billion reactions to comments among three hundred million users across 7.5 million posts. In addition, the project collects labeled comments to train machine learning models that measure and predict whether comments are deliberative according to two core dimensions: the civility of conversations and meaningful cross-ideological interactions. These data, in conjunction with recent methodological advances in the analysis of quasi-experimental designs, social network analysis, natural language processing, and multilevel modeling strategies, are used to identify the characteristics of online foci that foster cross-ideological interaction and civil discourse and to understand the role online foci play in moderating the impact of divisive, exogenous social events. This project is supported jointly by the Sociology, Methodology, Measurement, and Statistics, Human Networks and Data Science-Research, and Secure and Trustworthy Cyberspace Programs. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

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