Research Projects
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.
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
Funders: National Science Foundation
Research Areas: Data Privacy and Sociotechnical Cybersecurity Data Science, Analytics, and Visualization Smart Cities and Connected Communities
Improving public transit for lower-income individuals - who often endure complex, lengthy trips - by providing a methods, guidelines, and a toolkit to identify and characterize the challenges typical of such complex trips.
Principal Investigator(s): Vanessa Frias-Martinez
Funders: National Science Foundation
Research Areas: Data Privacy and Sociotechnical Cybersecurity Data Science, Analytics, and Visualization Smart Cities and Connected Communities
Improving public transit for lower-income individuals - who often endure complex, lengthy trips - by providing a methods, guidelines, and a toolkit to identify and characterize the challenges typical of such complex trips.
Semantic Foundations and Formal Methods for Evolutionary System-of-Systems
Principal Investigator(s): Jennifer Golbeck
Funders: DoD-Defense
Principal Investigator(s): Jennifer Golbeck
Funders: DoD-Defense
Systematically Documenting New Sociotechnical Foundations for Research Synthesis Infrastructures
Principal Investigator(s): Joel Chan
Funders: Alfred P. Sloan Foundation Other Non-Federal
Principal Investigator(s): Joel Chan
Funders: Alfred P. Sloan Foundation Other Non-Federal
TechBridge: Fostering Digital Literacy and Intergenerational Connection in Montgomery County
Principal Investigator(s): Galina Madjaroff Reitz
Funders: State of MD
Research Areas: Accessibility and Inclusive Design Human-Computer Interaction Youth Experience, Learning, and Digital Practices
Principal Investigator(s): Galina Madjaroff Reitz
Funders: State of MD
Research Areas: Accessibility and Inclusive Design Human-Computer Interaction Youth Experience, Learning, and Digital Practices
Testbed for the Redlining Archives of California’s Exclusionary Spaces (T-RACES)
Principal Investigator(s): Richard Marciano
Funders: Unfunded
Research Areas: Archival Science Data Science, Analytics, and Visualization Library and Information Science Machine Learning, AI, Computational Linguistics, and Information Retrieval
Making publicly accessible online documents relating to the practice of “redlining” neighborhoods in the 1930s and 1940s in eight California cities. “Redlining” refers to the practice of flagging minority neighborhoods as undesirable for home loans. The project creates a searchable database and interactive map interface.
Principal Investigator(s): Richard Marciano
Funders: Unfunded
Research Areas: Archival Science Data Science, Analytics, and Visualization Library and Information Science Machine Learning, AI, Computational Linguistics, and Information Retrieval
Making publicly accessible online documents relating to the practice of “redlining” neighborhoods in the 1930s and 1940s in eight California cities. “Redlining” refers to the practice of flagging minority neighborhoods as undesirable for home loans. The project creates a searchable database and interactive map interface.
Transforming Indigenous Archival Search: Evaluating Reparative Aggregation, Linked Data, and Cultural-Technical Infrastructures in the SNAC Platform
Principal Investigator(s): Diana E. Marsh
Funders: Andrew W. Mellon Foundation Other Non-Federal
Research Areas: Archival Science
Principal Investigator(s): Diana E. Marsh
Funders: Andrew W. Mellon Foundation Other Non-Federal
Research Areas: Archival Science
UMD INFO College Fellows Program at the National Agricultural Library
Principal Investigator(s): Katrina Fenlon
Funders: US Department of Agriculture
Research Areas: Archival Science Digital Humanities Library and Information Science Youth Experience, Learning, and Digital Practices
Principal Investigator(s): Katrina Fenlon
Funders: US Department of Agriculture
Research Areas: Archival Science Digital Humanities Library and Information Science Youth Experience, Learning, and Digital Practices
University of Maryland College of Information Studies Fellows Program at the National Agricultural Library
Principal Investigator(s): Katrina Fenlon
Funders: US Department of Agriculture
Principal Investigator(s): Katrina Fenlon
Funders: US Department of Agriculture