Award Abstract # 2031002
RAPID: Cyber-Hostility and COVID-19

NSF Org: SES
Division of Social and Economic Sciences
Recipient: CLEMSON UNIVERSITY
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: FY 2020 = $199,996.00
History of Investigator:
  • Matthew Costello (Principal Investigator)
    mjcoste@clemson.edu
  • Feng Luo (Co-Principal Investigator)
  • Yin Yang (Co-Principal Investigator)
  • Long Cheng (Co-Principal Investigator)
  • Hongxin Hu (Co-Principal Investigator)
Recipient Sponsored Research Office: Clemson University
201 SIKES HALL
CLEMSON
SC  US  29634-0001
(864)656-2424
Sponsor Congressional District: 03
Primary Place of Performance: Clemson University
132 Brackett Hall
Clemson
SC  US  29634-0001
Primary Place of Performance
Congressional District:
03
Unique Entity Identifier (UEI): H2BMNX7DSKU8
Parent UEI:
NSF Program(s): Sociology,
Secure &Trustworthy Cyberspace,
EPSCoR Co-Funding
Primary Program Source: 01002021DB NSF RESEARCH & RELATED ACTIVIT
Program Reference Code(s): 025Z, 065Z, 075Z, 096Z, 7434, 7914, 9150, 9178, 9179
Program Element Code(s): 133100, 806000, 915000
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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(Showing: 1 - 10 of 13)
Anderson, John and Huang, Qiqing and Cheng, Long and Hu, Hongxin "BYOZ: Protecting BYOD Through Zero Trust Network Security" Proceedings of the 16th IEEE International Conference on Networking, Architecture, and Storage , 2022 https://doi.org/978-1-6654-5408 Citation Details
Cheng, Long and Wilson, Christin and Liao, Song and Young, Jeffrey and Dong, Daniel and Hu, Hongxin "International Conference on Machine Learning and Applications" Conference on Computer and Communications Security , 2020 Citation Details
Costello, Matthew and Cheng, Long and Luo, Feng and Hu, Hongxin and Liao, Song and Vishwamitra, Nishant and Li, Mingqi and Okpala, Ebuka "COVID-19: A Pandemic of Anti-Asian Cyberhate" Journal of Hate Studies , v.17 , 2021 https://doi.org/10.33972/jhs.198 Citation Details
Cuo, Keyan and Zhao, Wentai and Mu Jaden and Vishwamitra, Vishant and Zhao, Ziming and Hu, Hongxin "Understanding the Generalizability of Hateful Memes Detection Models Against COVID-19-related Hateful Memes" International Conference on Machine Learning and Applications , 2022 Citation Details
Ding, Wenbo and Hu, Hongxin and Cheng, Long "IOTSAFE: Enforcing Safety and Security Policy withReal IoT Physical Interaction Discovery" Network and Distributed System Security Symposium , 2021 Citation Details
Liao, SOng and Wilson, Christin and Cheng, Long and Hu, Hongxin and Deng, Huixing "Measuring the Effectiveness of Privacy Policies for Voice Assistant Applications" Annual Computer Security Applications Conference , 2020 Citation Details
Mingqi, Li and Ding, Fei and Zhang, Dan and Cheng, Long and Hu, Hongxin and Luo, Feng "Multi-level Distillation of Semantic Knowledge for Pre-training Multilingual Language Model" Emperical Methods in Natural Language Processing , 2022 Citation Details
Mingqi, Li and Liao, Song and Okpala, Ebuka and Tong, Max and Costello, Matthew and Cheng, Long and Hu, Hongxin and Luo, Feng "COVID-HateBERT: a Pre-trained Language Modelfor COVID-19 related Hate Speech Detection" International Conference on Machine Learning and Applications , 2021 Citation Details
Okpala, Ebuka and Cheng, Long and Mbwambo, Nicodemus and Luo, Feng "AAEBERT: Debiasing BERT-based Hate SpeechDetection Models via Adversarial Learning" International Conference on Machine Learning and Applications , 2022 Citation Details
Vishwamitra, Nishant and Hu, Hongxin and Luo and Cheng, Long "Towards Understanding and Detecting Cyberbullyingin Real-world Images" Network and Distributed System Security Symposium , 2021 Citation Details
Vishwamitra, Nishant and Hu, Ruijia Roger and Luo, Feng and Cheng, Long and Costello, Matthew and Yang, Yin "On Analyzing COVID-19-related Hate Speech Using BERT Attention" IEEE International Conference on Machine Learning and Applications , 2020 https://doi.org/10.1109/ICMLA51294.2020.00111 Citation Details
(Showing: 1 - 10 of 13)

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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