Research Projects
Mitigating online COVID misinformation costs: From individual to field interventions
Principal Investigator(s): Giovanni Luca Ciampaglia
Funders: Social Science Research Council Other Non-Federal
Research Areas: Data Science, Analytics, and Visualization Social Networks, Online Communities, and Social Media
This project will conduct one of the most systematic tests to date of the welfare effects of altering information environments by decreasing exposure to untrustworthy sources. Researchers will encourage social media users to change the composition of the accounts they follow and measure the effect of this intervention on real-world behavior. This design will provide a building block for future research on the effects of online information exposure on offline behavior.
Principal Investigator(s): Giovanni Luca Ciampaglia
Funders: Social Science Research Council Other Non-Federal
Research Areas: Data Science, Analytics, and Visualization Social Networks, Online Communities, and Social Media
This project will conduct one of the most systematic tests to date of the welfare effects of altering information environments by decreasing exposure to untrustworthy sources. Researchers will encourage social media users to change the composition of the accounts they follow and measure the effect of this intervention on real-world behavior. This design will provide a building block for future research on the effects of online information exposure on offline behavior.
NSF Convergence Accelerator Track J: NourishNet – A Food Recovery Toolbox
Principal Investigator(s): Vanessa Frias-Martinez
Funders: National Science Foundation
Principal Investigator(s): Vanessa Frias-Martinez
Funders: National Science Foundation
Open Micropublishing of Evidence Knowledge Graphs for Evidence-Informed Deliberation
Principal Investigator(s): Joel Chan
Funders: Metagov Other Non-Federal
Principal Investigator(s): Joel Chan
Funders: Metagov Other Non-Federal
Quantum Choreobotics: Democratizing Quantum Computing Through Interactive Dance/ Theater Performance, With On-Body Robots
Principal Investigator(s): Bill Kules
Funders: UMD Funded
Research Areas: Data Science, Analytics, and Visualization Health Informatics Human-Computer Interaction
Principal Investigator(s): Bill Kules
Funders: UMD Funded
Research Areas: Data Science, Analytics, and Visualization Health Informatics Human-Computer Interaction
Read, Watch, Play: Development of a Pedagogical Approach and Technical Infrastructure to Build Gamification, Game-Based Learning, and Other Hands-On Projects into Courses across Campus
Principal Investigator(s): Caro Williams-Pierce Andrew Fellows Katherine Izsak Pamela Duffy
Funders: State of MD
Research Areas: Youth Experience, Learning, and Digital Practices
Principal Investigator(s): Caro Williams-Pierce Andrew Fellows Katherine Izsak Pamela Duffy
Funders: State of MD
Research Areas: Youth Experience, Learning, and Digital Practices
Ready Now: Supporting Youth and Families During Crisis
Principal Investigator(s): Mega Subramaniam Nitzan Koren
Funders: Institute of Museum and Library Services
Research Areas: Library and Information Science
Principal Investigator(s): Mega Subramaniam Nitzan Koren
Funders: Institute of Museum and Library Services
Research Areas: Library and Information Science
Reducing the gender gap in AfD discussions: an evidence scoring approach
Principal Investigator(s): Giovanni Luca Ciampaglia
Funders: Wikimedia Foundation Other Non-Federal
Research Areas: Data Science, Analytics, and Visualization Social Networks, Online Communities, and Social Media
Principal Investigator(s): Giovanni Luca Ciampaglia
Funders: Wikimedia Foundation Other Non-Federal
Research Areas: Data Science, Analytics, and Visualization Social Networks, Online Communities, and Social Media
REU Supplement: SCC-IRB Track 1: Inclusive Public Transit Toolkit to Assess Quality of Service Across Socioeconomic Status in Baltimore City
Principal Investigator(s): Vanessa Frias-Martinez Jessica Vitak Christopher Antoun
Funders: National Science Foundation
Principal Investigator(s): Vanessa Frias-Martinez Jessica Vitak Christopher Antoun
Funders: National Science Foundation
REU Supplement: SCC-IRG Track 1: Inclusive Public Transit Toolkit to Assess Quality of Service Across Socioeconomic Status in Baltimore City
Principal Investigator(s): Vanessa Frias-Martinez Jessica Vitak Christopher Antoun
Funders: National Science Foundation
Research Areas: Accessibility and Inclusive Design Future of Work Human-Computer Interaction Smart Cities and Connected Communities
Principal Investigator(s): Vanessa Frias-Martinez Jessica Vitak Christopher Antoun
Funders: National Science Foundation
Research Areas: Accessibility and Inclusive Design Future of Work Human-Computer Interaction Smart Cities and Connected Communities
Revolutionizing Space-Based ISR through Decentralized Systems & In-Orbit ML Computing for Near-Real-Time Intelligence
Principal Investigator(s): Sergii Skakun
Funders: Little Place Labs Other Non-Federal
Principal Investigator(s): Sergii Skakun
Funders: Little Place Labs Other Non-Federal
SaTC: CORE: Medium: Collaborative: BaitBuster 2.0: Keeping Users Away From Clickbait
Principal Investigator(s): Naeemul Hassan
Funders: National Science Foundation
Research Areas: Machine Learning, AI, Computational Linguistics, and Information Retrieval Data Privacy and Sociotechnical Cybersecurity Data Science, Analytics, and Visualization Social Networks, Online Communities, and Social Media
Developing novel techniques - through the application of state-of-the-art machine learning - to detect various forms of clickbait, especially video-based clickbait, and study user behavior on social media to design effective warning systems.
Principal Investigator(s): Naeemul Hassan
Funders: National Science Foundation
Research Areas: Machine Learning, AI, Computational Linguistics, and Information Retrieval Data Privacy and Sociotechnical Cybersecurity Data Science, Analytics, and Visualization Social Networks, Online Communities, and Social Media
Developing novel techniques - through the application of state-of-the-art machine learning - to detect various forms of clickbait, especially video-based clickbait, and study user behavior on social media to design effective warning systems.
SaTC: CORE: Medium: Learning Code(s): Community-Centered Design of Automated Content Moderation
Principal Investigator(s): Katie Shilton
Funders: National Science Foundation
Research Areas: Machine Learning, AI, Computational Linguistics, and Information Retrieval Social Networks, Online Communities, and Social Media
This research project aims to improve online community moderation by using machine learning and natural language processing. It focuses on learning from existing community decisions, supporting moderators, and creating adaptable tools. The goal is healthier online spaces and better working conditions for moderators.
Principal Investigator(s): Katie Shilton
Funders: National Science Foundation
Research Areas: Machine Learning, AI, Computational Linguistics, and Information Retrieval Social Networks, Online Communities, and Social Media
This research project aims to improve online community moderation by using machine learning and natural language processing. It focuses on learning from existing community decisions, supporting moderators, and creating adaptable tools. The goal is healthier online spaces and better working conditions for moderators.