National Science Foundation (NSF)
Visualizing and Enlarging the Statistics of Publication Information under Dynamics (VESPID)
Funding: $507,492 (2020-2022)
The project, through the National Science Foundation's National Center for Science and Engineering Statistics (NCSES) studies trends in research and publishing, integrating data from the NSF's Survey of Doctorate Recipients, patents, and leading publication indicators with publication and bibliometrics analyses. The broader aim of the project is to inform data-driven policies on scientific processes and funding, with specific focus on underrepresented PhD recipients. The work is led by Quantitative Scientific Solutions, LLC.
Office of Naval Research (ONR)
Sociolinguistic Information Filtering Tool (SIFT)
Funding: $150,000 (2020-2021)
This work is developing tools to provide users with the information and the backend support they need to reclaim control of their experience on social media. The Sociolinguistic Information Filtering Tool (SIFT) will consist of a desktop extension and associated mobile app initially based on machine learning, natural language processing and network theory techniques. Motivated by recent work understanding human information processing in the context of warning mechanisms, the project is building these tools iteratively, with humans-in-the-loop via in-depth user studies. We will evaluate and refine algorithmic approaches for account filtering and user-initiated bulk action toward automated, malicious, or otherwise unwanted accounts. The work is led by Quantitative Scientific Solutions, LLC.
National Science Foundation (NSF)
RAPID: Social Un-distancing: Understanding self-privacy violations in online communities during the Coronavirus pandemic
Funding: $200,000 (2020-2021)
Perceptions of privacy evolve over time as concerns about security and privacy are tied to the day’s events, and the longer arc of shifting norms. Little is known more specifically about the unique evolution of privacy attitudes during crises, but understanding this is consequential. We posit that self-privacy violations during acute events can leave individuals susceptible to deviant actors at times when, critically, these individuals are most vulnerable. We know that victims of Hurricane Harvey sought assistance through social media, in some cases revealing their full names and addresses online. But what we are witnessing in the case of the Coronavirus pandemic is inherently distinct from previous crises in important ways. COVID-19 is a global, relatively protracted acute threat. Unlike natural disasters or military engagements, the pandemic has left communications infrastructure intact. Digital outlets have become lifelines. We posit that self-privacy violations during the Coronavirus pandemic can help individuals feel more socially connected during a time of anxiety and physical distance. Yet, this benefit may leave them susceptible to risk when, critically, these individuals are most vulnerable. In this project, we propose to study online self-disclosure during the Coronavirus epidemic. This research will help us detect acts of self-disclosure in online settings and learn how oversharing is expedited or even encouraged during the coronavirus crisis.
Defense Advanced Research Projects Agency (DARPA)
Synthetic Prediction Markets with Algorithm Traders for Determining Experimental Reproducibility
Funding: $2,930,995 (2019-2022)
The project is developing artificial prediction markets to evaluate the reproducibility of published research claims in the social and behavioral science literatures. Markets are populated by artificial agents, trained and updated within human-expert prediction markets, but deployable offline. Artificial agents represent atomic properties of relevant signals, including full text of scientific papers, metadata for specific papers, and metadata about the community and the field. Agents (“trader bots”) learn trading patterns from subject matter experts engaged in prediction markets, but unlike their human counterparts, have comprehensive, unbiased view on the existing literature and metadata. The project is funded through DARPA’s Systematizing Confidence in Open Research and Evidence (SCORE) program. The project team includes collaborators at Penn State ARL, Smeal College of Business, Texas A&M, Old Dominion University, and Microsoft Research.