RAPID: Countering COVID-19 Misinformation via Situation-Aware Visually Informed Treatment
- Funded by National Science Foundation (NSF)
- Total publications:0 publications
Grant number: 2027713
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Key facts
Disease
COVID-19Start & end year
20202021Known Financial Commitments (USD)
$104,491Funder
National Science Foundation (NSF)Principal Investigator
Yu-Ru LinResearch Location
United States of AmericaLead Research Institution
University of PittsburghResearch Priority Alignment
N/A
Research Category
Policies for public health, disease control & community resilience
Research Subcategory
Communication
Special Interest Tags
N/A
Study Type
Non-Clinical
Clinical Trial Details
N/A
Broad Policy Alignment
Pending
Age Group
Not Applicable
Vulnerable Population
Not applicable
Occupations of Interest
Not applicable
Abstract
Computer and Information Science and Engineering - As the COVID-19 pandemic spreads, countries and cities around the globe have taken stringent measures including quarantine and regional lockdown. The increasing isolation, along with the panic and anxiety, creates challenges for countering misinformation--people are increasingly tapping into online information sources already familiar to them with declining chances of accessing alternative stories. This project will develop mechanisms based on text and image analysis, social psychology, and crowd-sourcing that can be used in a timely manner to counter misinformation during the ongoing COVID-19 crisis and beyond. One of the novel features of the approach is to deal with a specific instance of misinformation by crowd-sourcing authentic images that counter this misinformation. This research will contribute to the scientific understanding of misinformation and of persuasive narrative construction, to the assessment of risk for the spread of misinformation, and to the development of mechanisms to counter misinformation.
The technical aims of this project are divided into three thrusts. The first thrust will investigate what information content and which specific part of a multimodal social media post (e.g, a piece of text, text with an image, image with an embedded slogan) will receive stronger responses and hence increase the likelihood of the post being shared. The second thrust will create metrics to assess the likelihood of the spread of misinformation based on predictors learned from the content to which users are exposed. The third thrust will focus on the development of a system to counter misinformation based on citizen journalists? inputs of field investigations and on machine learning techniques. Finally, the system will be evaluated by survey studies and interviews to examine the system?s usability, usefulness, and effectiveness in reducing the spreading and impact of misinformation.
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.
The technical aims of this project are divided into three thrusts. The first thrust will investigate what information content and which specific part of a multimodal social media post (e.g, a piece of text, text with an image, image with an embedded slogan) will receive stronger responses and hence increase the likelihood of the post being shared. The second thrust will create metrics to assess the likelihood of the spread of misinformation based on predictors learned from the content to which users are exposed. The third thrust will focus on the development of a system to counter misinformation based on citizen journalists? inputs of field investigations and on machine learning techniques. Finally, the system will be evaluated by survey studies and interviews to examine the system?s usability, usefulness, and effectiveness in reducing the spreading and impact of misinformation.
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.