
NSF Org: |
SES Division of Social and Economic Sciences |
Recipient: |
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Initial Amendment Date: | June 25, 2020 |
Latest Amendment Date: | June 25, 2020 |
Award Number: | 2031002 |
Award Instrument: | Standard Grant |
Program Manager: |
Melanie Hughes
SES Division of Social and Economic Sciences SBE Directorate for Social, Behavioral and Economic Sciences |
Start Date: | June 15, 2020 |
End Date: | November 30, 2022 (Estimated) |
Total Intended Award Amount: | $199,996.00 |
Total Awarded Amount to Date: | $199,996.00 |
Funds Obligated to Date: |
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History of Investigator: |
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Recipient Sponsored Research Office: |
201 SIKES HALL CLEMSON SC US 29634-0001 (864)656-2424 |
Sponsor Congressional District: |
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Primary Place of Performance: |
132 Brackett Hall Clemson SC US 29634-0001 |
Primary Place of
Performance Congressional District: |
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Unique Entity Identifier (UEI): |
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Parent UEI: |
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NSF Program(s): |
Sociology, Secure &Trustworthy Cyberspace, EPSCoR Co-Funding |
Primary Program Source: |
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Program Reference Code(s): |
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Program Element Code(s): |
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Award Agency Code: | 4900 |
Fund Agency Code: | 4900 |
Assistance Listing Number(s): | 47.075 |
ABSTRACT
In this project, the new wave of cyber-hostility occasioned by COVID-19 is investigated through exploration and analysis of data from prominent social media sites over a twelve-month period. COVID-19-related cyber-hostility targeting people based on race/ethnicity, age, social class, immigrant status, and political ideology has emerged on social media. Through this project, insights are provided into COVID-19-related cyber-hostility and, more broadly, light is shed on how people respond online to unexpected societal calamities. Findings from this project offer cues on how to communicate with the public during destabilizing times and how to ease tension during crises and stop further escalation of outbursts of cyber-hostility.
Data for this project are collected from prominent social media sites, including Twitter, Facebook, Instagram, Snapchat, and Reddit, over a 12-month period. These data allow exploration of how quickly and broadly cyber-hostility related to COVID-19 spreads, both within and between social networks. Machine learning methods are developed to provide a timely and necessary understanding of the sources of COVID-19-related cyber-hostility, how and where it circulates online, and individual and situational factors associated with COVID-19-related cyber-hostility. This analysis allows insight into whether COVID-19-related cyber-hostility develops into a persistent fixture in cyberspace, or becomes less prevalent as the severity of the pandemic diminishes. This project is jointly funded by Sociology, the Established Program to Stimulate Competitive Research (EPSCoR), and Secure and Trustworthy Cyberspace (SaTC).
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.
PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH
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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 overarching aim of this grant project was to study cyber-hostility related to COVID-19. Specifically, we wanted to know if we could effectively use machine learning techniques to detect instances of cyber-hostility related to the COVID-19 pandemic, and, if so, how prevalent this type of material is on social media. Moreover, we wanted to explore who engages with this type of online content and the potential effects it can have on those who encounter it. We collected a large dataset from Twitter and Reddit spanning several years to address these topics.
First, utilizing machine learning techniques, we found that cyber-hostility related to the COVID-19 pandemic was indeed present on social media. In came in various forms, with cyber-hostility targeting others based on age, political ideology, and social class, for instance Chiefly, though, cyber-hostility related to the pandemic targeted members of the Asian community and China. One outcome we observed was that rhetoric on Twitter regarding China, individuals of Asian descent, and the pandemic in general became increasingly hostile as the pandemic progressed. In addition, we found evidence that offline rhetoric, particularly by individuals in influential positions, affect the ebb and flow of online cyber-hostility. For instance, when prominent politicians publicly used phrases such as “China Virus” to describe COVID-19, cyber-hostility on Twitter spiked.
This project also sought to assess the role of the pandemic in affecting social media behavior, particularly regarding online radicalization. Online radicalization can occur when an online user becomes fixated on a topic, increasingly views it negatively, and adopts an identity pitting themself against others. In this case, we found evidence of individuals on both Twitter and Reddit who demonstrated a growing fixation with the terms China or Chinese. Over time, their use of these terms became more negative and hostile, indicating potential warning signs of radicalization.
Last Modified: 03/09/2023
Modified by: Matthew Costello
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