Research Projects

  

 

Launching the TALENT Network to Promote the Training of Archival & Library Educators w. iNnovative Technologies
Principal Investigator(s): Richard Marciano
Funder: Institute of Museum and Library Services
Research Areas: Archival Science > Data Science, Analytics, and Visualization > Library and Information Science
The TALENT Network (Training of Archival & Library Educators with iNnovative Technologies) brings together experts from across the United States (including archivists, librarians, Library and Information Science educators, historians, learning scientists, cognitive scientists, computer scientists, and software engineers) in order to create a durable, diverse, and multidisciplinary national community focused on developing digital expertise and leadership skills among archival and library educators.
Libraries, Integration, and New Americans: Understanding immigrant acculturative stress
Principal Investigator(s): Ana Ndumu
Funder: Institute of Museum and Library Services
Research Areas: Information Justice, Human Rights, and Technology Ethics > Library and Information Science
Libraries, Integration, and New Americans,” or L.I.N.A., is a three-year research project directed by Dr. Ana Ndumu that will answer the following questions: What is the role of information in immigrant acculturative stress? How does information-related acculturative
stress impact library access? How can libraries help adult immigrants who are overwhelmed by information? Funding from IMLS under the Laura Bush 21st Century Early Career.
Machine Learning Strategies for FDR Presidential Library Collections (ML-FDR)
Principal Investigator(s): Richard Marciano
Funder: Unfunded Other Non-Federal
Research Areas: Archival Science > Data Science, Analytics, and Visualization > Machine Learning, AI, Computational Linguistics, and Information Retrieval
Demonstrate computational treatments of digital cultural assets using Artificial Intelligence (AI) and Machine Learning (ML) techniques that can help unlock hard-to-reach archival content related to WWII-era records housed at the FDR Presidential Library. This content is under-utilized by scholars examining American responses to the Holocaust.
Measuring the Impact of Urban Renewal
Principal Investigator(s): Richard Marciano
Funder: 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
Funder: 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.
Professor of the Practice
Principal Investigator(s): Jason R. Baron Douglas W. Oard
Funder: Unfunded Other Non-Federal
Research Areas: Archival Science > Machine Learning, AI, Computational Linguistics, and Information Retrieval
Memorandum of Understanding With MITRE Corporation. Collaborative research on methods of artificial intelligence used to improve administration of the Freedom of Information Act.
SaTC: CORE: Medium: Collaborative: BaitBuster 2.0: Keeping Users Away From Clickbait
Principal Investigator(s): Naeemul Hassan
Funder: 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
Funder: 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.

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