Research Projects
Filtered by: Data Science, Analytics, and Visualization
Measuring the Impact of Urban Renewal
Principal Investigator(s): Richard Marciano
Funders: Unfunded Other Non-Federal
Research Areas: Archival Science Data Science, Analytics, and Visualization
This is a case study focusing on the legacy of urban renewal in Asheville, North Carolina between 1965 and 1980, when housing policies were enacted that ultimately displaced and erased African American businesses and communities with traumatic and lasting effects. The study focuses on designing new access interfaces to tell human stories. Ongoing results were presented to the Racial Reparations Commission of the City of Asheville on May 20, 2023.
Principal Investigator(s): Richard Marciano
Funders: Unfunded Other Non-Federal
Research Areas: Archival Science Data Science, Analytics, and Visualization
This is a case study focusing on the legacy of urban renewal in Asheville, North Carolina between 1965 and 1980, when housing policies were enacted that ultimately displaced and erased African American businesses and communities with traumatic and lasting effects. The study focuses on designing new access interfaces to tell human stories. Ongoing results were presented to the Racial Reparations Commission of the City of Asheville on May 20, 2023.
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.
Reducing the gender gap in AfD discussions: an evidence scoring approach
Principal Investigator(s): Giovanni Luca Ciampaglia
Funders: Wikimedia Foundation Unfunded
Research Areas: Data Science, Analytics, and Visualization Social Networks, Online Communities, and Social Media
Principal Investigator(s): Giovanni Luca Ciampaglia
Funders: Wikimedia Foundation Unfunded
Research Areas: Data Science, Analytics, and Visualization Social Networks, Online Communities, and Social Media
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.
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.
Testbed for the Redlining Archives of California’s Exclusionary Spaces (T-RACES)
Principal Investigator(s): Richard Marciano
Funders: Unfunded Other Non-Federal
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 Other Non-Federal
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.