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The University of Maryland’s Social Data Science Center in conjunction with the Coleridge Initiative and the Kentucky Center for Statistics are delighted to announce this Executive Certificate in Data Literacy and Evidence Building that will be offered Fall 2024. The program draws on over a decade of successful experience training government agency staff and researchers in how to work with real world data to develop projects that could go from products to practice. Class participants will also learn how to examine the impact of COVID-19 on the labor market in a way that can be transferred to a wide range of policy contexts. This training program will be most useful for federal, state, and local government agency staff, graduate students in public policy, and practitioners who seek a high value credential. Much of the content for this course was generated by the original NYU-Accenture-KYStats-UMunich-UMD-OSU-Coleridge team. The lead individuals from each institution included Julia Lane, Xiangyu Ren, and Rafael Ladislau Alves (NYU), Jared Dubin (Accenture), Angie Tombari (KYStats), Frauke Kreuter (UMDand U Munich), Caro Haensch (NYU and UMunich), Jody Derezinski Williams (UMD), Tian Lou (OSU), and Jessica Cunningham (Coleridge). Webinar Hosts and Lead Instructors: Frauke Kreuter, Ph.D. Professor, Joint Program in Survey Methodology, Co-Director, Social Data Science Center(SoDa), University of Maryland Chair of Statistics and Data Science in Social Sciences and Humanities, Ludwig-Maximilian, University of Munich Angie Tombari, Ph.D. Senior research Analyst & Chief Methodologist, Kentucky Center for Statistics Nathan Barrett, Ph.D. Vice President of Product and Development, Coleridge Initiative
Data Literacy & Evidence Building Executive Certificate info webinar 05 22 2024
SoDa Symposium Seed Grant Series: Do County Demographics Shape Exposure to Cyber Harm? Exploring Socio-Economic Factors Influencing Integrated Attack Surface Size and Vulnerability SoDa Seed Grant Awarded Project: Correlating Population Demographics with Maryland's County-Level Critical Infrastructure Vulnerabilities Abstract: Local government-controlled critical infrastructure relies on computer systems and is increasingly vulnerable to cyber-attacks that can disrupt its operation impacting citizens’ daily lives. While some research has looked at user characteristics and their susceptibility to compromise, to date there is limited research examining how vulnerabilities in government infrastructures supporting several critical services are potentially distributed in different communities.  This project aims to bridge this gap by examining the relationship between exposed, and potentially vulnerable, county government services and the underlying characteristics of the communities they serve. To do so we aggregate and group publically available internet scans into geographic locations for the communities it serves, with publically available from the U.S. Census to conduct a series of exploratory analyses to investigate the relationships between community characteristics and the levels of potential vulnerability of county government services. We examine counties across 5 representative states across the country and find evidence of linkages between population, rural/urban divide, race, and income with changes in the size and exposure to vulnerability of county attack surfaces. Our findings suggest the need by policy makers to utilize more nuanced methods to allocate scarce resources across communities to address systemic cyber risk. Presenters: Charles Harry (PI) Associate Research Professor School of Public Policy and College of Information Studies University of Maryland charry@umd.edu William Lucyshyn (Co-PI) Research Professor and Director of Research Center for Governance of Technology and Systems (GoTech) School of Public Policy University of Maryland lucyshyn@umd.edu Commentator / Industry Expert: Netta Squires Director of Local Cybersecurity State of Maryland netta.squires@maryland.gov Moderator: Wayne Lutters, PhD Professor and Associate Dean for Strategic Initiatives Co-Director, Social Data Science Center (SoDa) College of Information Studies University of Maryland The SoDa Center at UMD SoDa Symposia highlight the diverse challenges and opportunities in the emerging area of Social Data Science.   Combining insights of SoDa researchers and partners from UMD and around the world, these regular virtual events showcase research and expert commentary about advances and open problems in the use of surveys, administrative, and trace data to understand and shape the social world we live in. Ranging from technical challenges of gathering high-quality data, ethical management of social data at scale, or examples of the power of social data science in education, business, government, or civic life, SoDa Symposia provide an opportunity for a broad audience of researchers, students, and practitioners to learn more about the potential of social data science to change the world.
SoDa Symposium Seed Grant Series: Do County Demographics Shape Exposure to Cyber Harm?
SoDa Seed Grant Awarded Project: Effective Few-Shot Learning for Constructs in Psychological and Social Science Abstract: The idea of a construct is central in the psychological and social sciences: constructs are abstract categories like empathy, misinformation, or benefits of social interaction that are operationalized in order to make them measurable. Social scientists spend an enormous amount of time and care in thinking about constructs; they are the vocabulary over which theories are defined. This stands in striking contrast to a great deal of current computational research in AI and NLP, where theory tends to be secondary and most work utilizes directly observable behaviors, simplistic operational definitions, or intuitive but poorly-defined proxy relationships. The gap between social science needs and computational practice is deeply problematic: computer scientists often blast forward with highly scalable methods without establishing careful connections to real-world problems and research questions, while social scientists often expend effort on traditional manual analysis out of a distrust for automated solutions or, conversely, they use the output of computational systems uncritically as if what they produce is known to be correct. This research project is bridging that gap by advancing a recent AI approach called few-shot learning, bringing together cutting-edge machine learning with human expertise in order to identify and work with constructs in a way that is both efficient and trustworthy. Paired with that methodological goal is the use of these methods to support substantive research in computational political science, focused on the measurement of politicized topics, and in mental health, focused on measurement of constructs related to suicidal risk. Presenters: Philip Resnik, Ph.D. (PI) MPower Professor Institute for Advanced Computer Studies and Department of Linguistics University of Maryland Alexander Hoyle PhD Candidate Computational Linguistics and Information Processing Lab University of Maryland Industry Expert / Commentator: Andrew Stavisky, Ph.D. Assistant Director, U.S. Government Accountability Office (GAO)
SoDa Symposium: How Can Large Language Models Help Us Identify and Use Constructs that We Can Trust?
Socio-algorithmic Foundations of Trustworthy News Recommendations Abstract: Social media are now the main source of news for the majority of Americans and for billions of people worldwide. The recommendation algorithms employed by these platforms are designed to maximize views and clicks by finding engaging content, which in turn often ends up also amplifying stories from dubious news sources, conspiracy theories, and unverified rumors. This situation is especially concerning for partisan news consumers, who have a tendency to engage with information that conforms to their beliefs, regardless of its accuracy: ranking the news by (either predicted or achieved) popularity creates a self-sustaining cycle that prioritizes pro-attitudinal information regardless of its quality. To break this loop, the project tests the hypothesis that prioritizing content that generates engagement in diverse audiences will improve the trustworthiness of recommendations. The objective of this research is to build a simulated social media platform that will allow researchers to test more robust news recommendation algorithms designed to improve the news consumption of social media users while recommending content that is still relevant to them. A key part of this endeavor is to design experiments combining survey-based collection of self-reported measures with observed behavioral outcomes of real-world social media behavior. Presenter and PI: Giovanni Luca Ciampaglia Assistant Professor College of Information Studies, University of Maryland, Giovanni Luca Ciampaglia is an assistant professor at the College of Information Studies at the University of Maryland. He holds a Laurea in Computer Science from the “Sapienza” University of Rome and a Ph.D. in Computer Science from the University of Lugano. He is interested in problems originating from the collective interplay between people and computing systems, such as information propagation on social media, knowledge production in online communities, and trustworthiness in cyberspace. His work has been covered in major news outlets, including the Wall Street Journal, Wired, NPR, and CBS News. Commentator: Ben Leff Co-Founder & CEO Verasight Ben Leff co-founded Verasight in 2020 with his former Cornell Government Professor, Peter K. Enns. Verasight’s mission is to expand access to high-quality survey data by building innovative methods to help organizations understand voters and consumers. Verasight’s clients include government agencies, academic researchers, Fortune 500 companies, and investors. Ben’s recent work has been featured in Bloomberg, Forbes, The Hill, and The Los Angeles Times. He began his career at Goldman Sachs and graduated from the Stanford Graduate School of Business as an Arjay Miller Scholar.
SoDa Seed Grant Series: Socio-algorithmic Foundations of Trustworthy News Recommendations
SoDa Symposium: Dealing with Missing Data in Education/Workforce Analysis Social Data Science Center University of Maryland Tuesday, January 30, 2024 Abstract: High quality data and data access are key to creating evidence for policy making; however, data that is systematically missing observations over time or for different groups of people can negatively impact the quality of that evidence. Participants in a recent Executive Certificate class jointly offered by New York University and the University of Maryland expressed a strong interest in building a community of practice around developing practical approaches to address missingness issues in their day-to-day work with education and workforce data. This webinar provides a hands-on approach by experts in the field who will discuss how to address common problems. They will walk participants through a Jupyter Notebook using synthetic data from the State of Kentucky. Presenters: Anna-Carolina (“Caro”) Haensch is a Lecturer at the Ludwig-Maximilians-Universität München (LMU), where she teaches the Introduction to Statistics lecture and contributes to various projects at the Chair for Statistics and Data Science in Social Sciences and the Humanities. Tian Lou is a Research Scientist in the John Glenn College of Public Affairs at The Ohio State University. Tian’s expertise are in the areas of policy analysis and evaluation, social policy, regional and urban economics, and policy and governance. Angela Tombari is a Senior Research analyst and Chief Methodologist at Kentucky Center for Statistics. Angie has a background in psychometrics and currently focuses on developing metrics and common data models as well as understanding the teacher workforce in Kentucky. Moderator: Frauke Kreuter, Professor, Joint Program in Survey Methodology, Co-Director of the Social Data Science Center, University of Maryland
SoDa Symposium: Dealing with Missing Data in Education/Workforce Analysis
SoDa Symposium: Challenges, Strategies, and Emerging Issues: Hearing from Privacy Stakeholders Presenters: Katie Shilton, Jessica Vitak, Paul Ohm, Trent D. Buskirk, Olga Kononykhina, Anna Lenhart Wednesday, January 24, 2024 Description: The UMD Transatlantic Privacy Project (https://privacyperceptions.org/) presents their findings from both qualitative (interviews) and quantitative (panel surveys) portions of the project, sharing insights on the panel data collected and the visualization tools being created by the research team. Commentary provided by Paul Ohm, Professor of Law, Georgetown University Law Center. Bios: Katie Shilton is an associate professor in the College of Information Studies at the University of Maryland, College Park, and leads the Ethics & Values in Design (EViD) Lab. Her research explores ethics and policy for the design of information technologies. She is the PI of the PERVADE project, a multi-campus collaboration focused on big data research ethics. Other projects include developing privacy-sensitive search for email collections; analyzing ethical cultures in computer security research; and building tools to facilitate ethics discussions in mobile application development. Jessica Vitak is an associate professor in the College of Information Studies and an affiliate professor in the Communication Department at the University of Maryland. Her research evaluates the privacy and ethical implications of big data, the internet of things, and other “smart” technologies. She seeks to understand how privacy concerns play a role in technology adoption and use, and she develops tools and resources to help children and adults make more informed decisions when using technology and sharing sensitive data. Paul Ohm is a Professor of Law at the Georgetown University Law Center in Washington, D.C. Professor Ohm’s research and teaching bridges computer science and law, utilizing his training and experience as a lawyer, policymaker, computer programmer, and network systems administrator. His research focuses on information privacy, computer crime law, surveillance, technology and the law, and artificial intelligence and the law. He serves as a faculty director for the Institute for Technology Law and Policy; the Center on Privacy and Technology at Georgetown Law; and the Georgetown University Tech & Society Initiative. Trent D. Buskirk, Ph.D., has recently joined the new School of Data Science at Old Dominion University as one of several founding faculty members. Prior to this appointment, Trent was the Novak Family Distinguished Professor of Data Science and outgoing Chair of the Applied Statistics and Operations Research Department at Bowling Green State University. Dr. Buskirk is a Fellow of the American Statistical Association and his research interests include big data quality, recruitment methods through social media, the use of big data and machine learning methods for health, social and survey science design and analysis, mobile and smartphone survey designs and in methods for calibrating and weighting non-probability samples and fairness in AI models and interpretable ML methods. Anna Lenhart is a non-resident scholar in the Carnegie Technology and International Affairs Program. She is also a policy fellow at the Institute for Data Democracy and Politics (IDDP) at George Washington University and a researcher at the University of Maryland Ethics and Values in Design Lab. Her research focuses on public engagement in tech policy and the intersections of privacy, transparency, and competition. She most recently served in the House of Representatives as senior technology legislative aide to Representative Lori Trahan (117th Congress) and as a Congressional Innovation Fellow for the House Judiciary Digital Markets Investigation (116th). Olga Kononykhina, a mid-career PhD researcher at LMU Munich, holds degrees in Applied Mathematics and Sociology. Over the past 15 years, she has actively engaged in research and consulting, focusing on the development of measurement systems and indicator frameworks, as well as the integration of data analysis into social and political contexts. She is keen on improving data literacy and data communication, particularly through the creation of interactive data platforms. @SoDaUMD
SoDa Privacy Week - Challenges, Strategies, and Emerging Issues: Hearing from Privacy Stakeholders
SoDa Symposium: Job Quality Measures – Data Literacy & Evidence Building Abstract: The importance of creating good quality jobs has been highlighted in much recent legislation, including the Chips and Science Act, the Inflation Reduction Act, and the Infrastructure Investment and Jobs Act, as well as the renewal of existing legislation such as the Workforce Innovation and Opportunity Act. Participants in a recent Executive Certificate class jointly offered by New York University and the University of Maryland expressed a strong interest in building a community of practice around developing operational measures using UI wage record data to inform associated policy implementations. This webinar provides a set of lightning talks from experts from universities, think tanks and states that review current approaches, identify lessons learned, and sketches a possible future agenda.
SoDaSymposium Job Quality Measures - Data Literacy & Evidence Building
Presented by Sallie Ann Keller, Phd, Chief Scientist and Associate Director, Research and Methodology Directorate, U.S. Census Bureau This is an exciting time to be part of official statistics. There is growing demand for statistical products that traditional surveys alone cannot address. Stakeholders want timelier, more accurate, more granular, and differently structured information about people, places, and the economy than ever before. New data sources and data science innovations allow us to meet those demands. In today’s digital era, massive amounts of data are generated as we go about our daily lives. This volume of data generated every day, through commercial and personal transactions and the management of federal, state, and local programs, continues to grow exponentially. This provides an incredible opportunity to revolutionize how we capture and use data to develop relevant products. Instead of limiting ourselves to the data our surveys produce, we can flip the paradigm to design products based on what data users need. To do this we must integrate our survey data with other data sources. This presentation will share how the U.S. Census Bureau plans to re‑envision its data enterprise based on a statistical product‑first approach. This approach includes eliciting the purposes and uses our data are to support, collaborating with internal and external data users to develop the products using ALL our data assets, and then embracing varying access modes for statistical product dissemination to support stakeholder needs at all levels of data acumen. This work will modernize and transform our official statistical infrastructure.
SoDa - Evolving a Data Enterprise to Support Relevant, Timely, and Equitable Statistical Products
Our project examines media coverage of lynching from 1789 to the current era. Researchers examined metadata of 60,000 pages of news coverage in the Library of Congress’ Chronicling America database and conducted a computational text analysis of a stratified sample. We find that some news reports sought to normalize this violence by describing members of lynch mobs as citizens seeking to pursue justice. A similar examination of Black press coverage showed divergent priorities in news narratives. Our sample of 714 Black newspaper articles tended to emphasize civil society narratives such as the legal system whereas descriptions of violence had greater prominence in a sample of 1,387 mainstream newspaper articles. Article from The Conversation: This course uses big data to examine how American newspapers covered lynchings (READ MORE) @socialdatasciencecentersod9084
SoDA Seed Grant Series Newspapers and the Lynching Story
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