Research Projects
Filtered by: Social Networks, Online Communities, and Social Media
CAREER: Advancing Remote Collaboration: Inclusive Design for People with Dementia
Principal Investigator(s): Amanda Lazar
Funders: National Science Foundation
Research Areas: Health Informatics Human-Computer Interaction Social Networks, Online Communities, and Social Media
Technology increasingly provides opportunities to interact remotely with others. People with cognitive impairment can be excluded from these opportunities when technology is not designed with their needs, preferences, and abilities in mind.
Principal Investigator(s): Amanda Lazar
Funders: National Science Foundation
Research Areas: Health Informatics Human-Computer Interaction Social Networks, Online Communities, and Social Media
Technology increasingly provides opportunities to interact remotely with others. People with cognitive impairment can be excluded from these opportunities when technology is not designed with their needs, preferences, and abilities in mind.
CAREER: Socio-Algorithmic Foundations of Trustworthy Recommendations
Principal Investigator(s): Giovanni Luca Ciampaglia
Funders: National Science Foundation
Research Areas: Data Science, Analytics, and Visualization Human-Computer Interaction Social Networks, Online Communities, and Social Media
Principal Investigator(s): Giovanni Luca Ciampaglia
Funders: National Science Foundation
Research Areas: Data Science, Analytics, and Visualization Human-Computer Interaction Social Networks, Online Communities, and Social Media
Collaborative Research: ER2: Developing Educational Resources for the Ethical Use of Pervasive Data
Principal Investigator(s): Jessica Vitak
Funders: National Science Foundation
Research Areas: Data Privacy and Sociotechnical Cybersecurity Human-Computer Interaction Information Justice, Human Rights, and Technology Ethics Machine Learning, AI, Computational Linguistics, and Information Retrieval Social Networks, Online Communities, and Social Media
This project develops educational resources and training to promote ethical practices in the collection, storage, and analysis of pervasive data from digital platforms. By creating case studies, interactive modules, and “train the trainer” programs, it aims to enhance responsible research practices among computing students and early-career researchers.
Principal Investigator(s): Jessica Vitak
Funders: National Science Foundation
Research Areas: Data Privacy and Sociotechnical Cybersecurity Human-Computer Interaction Information Justice, Human Rights, and Technology Ethics Machine Learning, AI, Computational Linguistics, and Information Retrieval Social Networks, Online Communities, and Social Media
This project develops educational resources and training to promote ethical practices in the collection, storage, and analysis of pervasive data from digital platforms. By creating case studies, interactive modules, and “train the trainer” programs, it aims to enhance responsible research practices among computing students and early-career researchers.
Crowdsourced Data: Accuracy, Accessibility, Authority (CDAAA)
Principal Investigator(s): Victoria Van Hyning
Funders: 8/1/2022 - 4/9/2025 Institute of Museum and Library Services
Research Areas: Accessibility and Inclusive Design Archival Science Digital Humanities Information Justice, Human Rights, and Technology Ethics Library and Information Science Social Networks, Online Communities, and Social Media
CDAAA explores the sociotechnical barriers libraries, archives, and museums face in integrating crowdsourced transcriptions to discovery systems.
Principal Investigator(s): Victoria Van Hyning
Funders: 8/1/2022 - 4/9/2025 Institute of Museum and Library Services
Research Areas: Accessibility and Inclusive Design Archival Science Digital Humanities Information Justice, Human Rights, and Technology Ethics Library and Information Science Social Networks, Online Communities, and Social Media
CDAAA explores the sociotechnical barriers libraries, archives, and museums face in integrating crowdsourced transcriptions to discovery systems.
Detecting and Mapping War-induced Damage to Agricultural Fields in Ukraine using Multi-Modal Remote Sensing Data
Principal Investigator(s): Sergii Skakun
Funders: NASA - Proposal Only Other Federal
Research Areas: Data Science, Analytics, and Visualization Human-Computer Interaction Machine Learning, AI, Computational Linguistics, and Information Retrieval Smart Cities and Connected Communities Social Networks, Online Communities, and Social Media
This project advances remote sensing methods to map war-induced damage to Ukraine’s agricultural fields using infrared and visible spectrum satellite data. By developing deep-learning and data fusion techniques, the research will detect artillery craters, burned areas, and abandoned fields to assess the war’s impact on agriculture at scale.
Principal Investigator(s): Sergii Skakun
Funders: NASA - Proposal Only Other Federal
Research Areas: Data Science, Analytics, and Visualization Human-Computer Interaction Machine Learning, AI, Computational Linguistics, and Information Retrieval Smart Cities and Connected Communities Social Networks, Online Communities, and Social Media
This project advances remote sensing methods to map war-induced damage to Ukraine’s agricultural fields using infrared and visible spectrum satellite data. By developing deep-learning and data fusion techniques, the research will detect artillery craters, burned areas, and abandoned fields to assess the war’s impact on agriculture at scale.
Developing and Investigating Data Science Interventions Connected to University Athletics to Address Systemic Racism in Undergraduate STEM Education (better known as DataGOAT)
Principal Investigator(s): Tamara Clegg
Funders: National Science Foundation
Research Areas: Data Science, Analytics, and Visualization Future of Work Health Informatics Human-Computer Interaction Information Justice, Human Rights, and Technology Ethics Social Networks, Online Communities, and Social Media Youth Experience, Learning, and Digital Practices
This project, DataGOAT, engages Black male collegiate athletes in data science by connecting their sports performance and health data to STEM learning. It aims to overcome racialized stereotypes, foster STEM identities, and create educational pathways through courses, internships, and data analysis tools, benefiting both participants and the broader educational community.
Principal Investigator(s): Tamara Clegg
Funders: National Science Foundation
Research Areas: Data Science, Analytics, and Visualization Future of Work Health Informatics Human-Computer Interaction Information Justice, Human Rights, and Technology Ethics Social Networks, Online Communities, and Social Media Youth Experience, Learning, and Digital Practices
This project, DataGOAT, engages Black male collegiate athletes in data science by connecting their sports performance and health data to STEM learning. It aims to overcome racialized stereotypes, foster STEM identities, and create educational pathways through courses, internships, and data analysis tools, benefiting both participants and the broader educational community.
HCC: Small: The Incel Phenomenon: Assessing Radicalization and Deradicalization Online
Principal Investigator(s): Jennifer Golbeck
Funders: National Science Foundation
Research Areas: Human-Computer Interaction Information Justice, Human Rights, and Technology Ethics Social Networks, Online Communities, and Social Media Youth Experience, Learning, and Digital Practices
This project, led by Jennifer Golbeck at UMD’s College of Information, studies how radicalization and deradicalization occur within online incel communities.
Principal Investigator(s): Jennifer Golbeck
Funders: National Science Foundation
Research Areas: Human-Computer Interaction Information Justice, Human Rights, and Technology Ethics Social Networks, Online Communities, and Social Media Youth Experience, Learning, and Digital Practices
This project, led by Jennifer Golbeck at UMD’s College of Information, studies how radicalization and deradicalization occur within online incel communities.
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 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
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.