Ensuring Integrity of COVID-19 Data and News
Large amounts of epidemiological data are being generated and collected from a variety of sources to understand the impact and propagation of COVID-19. Huge amounts of news articles are generated and disseminated about the pandemic to keep the population informed. The appropriateness of the actions taken by individuals, corporations, and governments are often based on the quality of data and news. Thus, ensuring the quality of data and news is important. However, malicious actors can alter the attributes of data records, insert spurious records, or suppress records causing any analysis to be inadequate and misinformation to be propagated. This project addresses the critical problem of defining and identifying spurious data and news concerning COVID-19 and tracking the source of misinformation.
Project’s Latest News
For Your Eyes Only: Data Privacy in a Pandemic
Listen to this episode from NSF PREPARE: Science Before the Storm on Spotify. Welcome to our first researcher match! These scientists have never collaborated before but we thought it would be fun to eavesdrop on their “what if?” conversation. Podcast features Professor Indrakshi Ray (CSU Computer Science Department), Assistant Professor Sameer Patil (IU Luddy School of Informatics, Computing, and Engineering), Postdoctoral Research Associate John Seberger (MSU ComArtSci). Listen on Spotify…
CSU COVID-19 research grant
Congressman Joe Neguse announces $199,748 grant for Colorado State University COVID-19 research. “I am thrilled to see this public recognition of the important research being done at Colorado State University,” said Congressman Neguse. Read more…
NSF Award
NSF awarded RAPID: Ensuring Integrity of COVID-19 Data and News across Regions. 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. Read more…
Falsified COVID-19 Records Detection
Colorado State University to deploy machine-learning tool that can detect falsified COVID-19 medical records. The National Science Foundation nearly $200,000 to Colorado State University and University of Colorado researchers to build machine-learning tools that can verify the integrity of COVID-19 patient medical records. Read more…
Publications & Software
- Noushin Salek Faramarzi, Fateme Hashemi Chaleshtori, Hossein Shirazi, Indrakshi Ray, Ritwik Banerjee: “Claim Extraction and Dynamic Stance Detection in COVID-19 Tweets”. In Companion Proceedings of the ACM Web Conference (WWW), Austin, Texas, April 23. Paper | Presentation
- Chaoyuan Zuo, Hossein Shirazi, Fateme Hashemi, Ritwik Banerjee, and Indrakshi Ray, “Seeing Should Probably not be Believing: The Role of Deceptive Support in COVID-19 Misinformation on Twitter,” Journal of Data and Information Quality, 15(1): 9:1—9:26, March 23,. Paper | Software
- Joaquin Cuomo, Hajar Homayouni, Indrakshi Ray, and Sudipto Ghosh, “Detecting Temporal Dependencies in Data”, In Proceedings of the British International Conference on Databases (BICOD), London, U.K., March 2022. Paper | Presentation
- Ritwik Banerjee and Indrakshi Ray, “Prevention, Detection, and Cure for Misinformation”, In Proceedings of the IEEE International Conference on Cognitive Machine Intelligence (CoGMI), Atlanta, Georgia, December 2021. Paper | Presentation
- Sina Mahdipour Saravani, Indrajit Ray, and Indrakshi Ray. “Automated Identification of Social Media Bots Using Deepfake Text Detection.” In Proceedings of International Conference on Information Systems Security (ICISS), Patna, India, December 2021. Paper | Presentation | Software
- Sina Mahdipour Saravani, Ritwik Banerjee and Indrakshi Ray, “An Investigation into the Contribution of Locally Aggregated Descriptors to Figurative Language Identification”, In Proceedings of Workshop on Insights from Negative Results in NLP (Insights@ACL), Punta Cana, Dominican Republic, November 2021. Paper | Presentation | Software
- Hajar Homayouni, Indrakshi Ray, Sudipto Ghosh, Shlok Gondalia, and Michael Kahn, “Anomaly Detection in COVID-19 Time-Series Data”, Springer Nature Computer Science, 2(4):279, July 2021. Paper | Software
