University of Maryland

Social Data Science Center

Social Data Science Center

Videos

Our presenters will discuss innovation, entrepreneurship in social data science, and creators on social media. The research opportunities in these areas provide a glimpse into the future of these platforms regarding the Metaverse and the AI revolution in search that is now upon us. Dr. DiSogra has had senior positions at Meta and Google. At Meta he was director of user experience research for Facebook app and more recently director of research with Meta’s Demography and Survey Sciences group. Prior to Meta, he built the survey and statistics team for Google’s customer experience lab that tracks global advertiser satisfaction for the company. He was also the founding director of the California Health Interview Survey at UCLA and was Chief Statistician and Senior Vice President at both Abt SRBI and Knowledge Networks/GfK. As a member of the American Association for Public Opinion Research, Dr. DiSogra served on the Standards Committee addressing the professional code of ethics regarding surveys and polls in the United States. Dr. DiSogra formerly held a leadership role as chair of the Applied Public Health Statistics Section of the American Public Health Association. He received their National Applied Statistics Award for Achievement in Industry. He is also a member of the Survey Methods Section of the American Statistical Association. Ginger Zhe Jin is currently Professor of Economics at the University of Maryland, College Park. She is also the ADVANCE Professor of the College of Behavioral and Social Sciences at the University since September 2021. In 2015-2017, she was on leave at the Federal Trade Commission, serving as the Director of the FTC Bureau of Economics from January 2016 to July 2017. From January 2019 to May 2020, she was on leave at Amazon.com as Amazon Scholar and Senior Principal Economist. Most of her research focuses on information asymmetry among economic agents and how to provide information to overcome the information problem. The applications she has studied include retail food safety, health insurance, prescription drugs, e-commerce, regulatory inspection, scientific innovation, air quality, blood donation, vaccination, intrafamilial interaction, data regulation, and consumer protection. Her research has been published in leading economics, management and marketing journals, with support from the National Science Foundation, the Net Institute, the Alfred P. Sloan Foundation, and the Washington Center for Equitable Growth. Many of her works have been covered by major media outlets including Wall Street Journal, New York Times, Forbes, Bloomberg, and Los Angeles Times. She is currently a managing editor of the International Journal of Industrial Organization, associate editor of RAND Journal of Economics, advisory council member of Journal of Industrial Economics, and board member of Industrial Organization Society. She has been Research Associate of NBER since 2012. In October 2014, she co-founded Hazel Analytics, an analytics company that promotes the use of open government data. She received her PhD in Economics from UCLA in 2000.
SoDa Symposium: Social Data Science Research, Innovation, and EntrepreneurshipDate: Mar 14, 2023
SODA: How to Balance Data Sharing and Data Protection in the Development and Use of Al?
The Diversity of Transatlantic Stakeholders’ Privacy Perceptions Jessica Vitak | Leah von der Heyde | Zothan Mawii January 26, 2023
The Diversity of Transatlantic Stakeholders'Privacy Perceptions
essica Vitak (University of Maryland), Leah von der Heyde (LMU Munich), and Zothan Mawii (University of Maryland) will present first results from TAPP’s quantitative and qualitative research on the diversity of transatlantic stakeholders’ privacy perceptions. Vasilka Stoilova (University of Mannheim) and Markus Herklotz (LMU Munich) from BERD@NFDI will show how the interactive Virtual Assistant (iVA) contributes to privacy literacy in practice. A panel discussion featuring Alena Buyx (TU Munich), Kobbi Nissim (Georgetown University), and Philipp Räther (Allianz) will explore how data sharing and protection can be balanced in the development and use of AI, moderated by Frauke Kreuter (LMU Munich & University of Maryland).
SoDa: Celebration of Privacy Day: Quo Vadis, Digital Privacy? Perceptions, Practices, and Policies
Presenter 1: Carol Haney Head of Research and Data Science Qualtrics Presenter 2: Philip Resnik Professor, Institute for Advanced Computer Studies and Department of Linguistics University of Maryland Moderator: Frauke Kreuter, Co-Director, Social Data Science Center (SoDa) Professor, Joint Program in Survey Methodology University of Maryland Tuesday, December 13, 2022 Abstract Open-ends are a well known problem in survey research: language can yield extremely rich responses, including bringing to the surface aspects of a question or issue that the researcher might not have known to look for, but the analysis of text is costly and labor-intensive. As a result, there is a tendency to include open-ends as an afterthought, to use them minimally, or to avoid them altogether. Computational methods can potentially help, but they often raise concerns about whether the results they provide are as trustworthy and actionable as other kinds of responses. We will talk about approaches we've been taking to the analysis of open-ends, which combine automation with human intervention in order to navigate the balance between automation and trustworthiness. Two experiments were run independently on the same set of 16,648 responses on Reddit to a question about reasons that people who considered suicide did not end up killing themselves. The first experiment had human intervention at the start using a machine learning process that included word clouds and TF/IDF techniques to help human coders develop a codebook that was actionable. The second experiment used topic modeling, an unsupervised machine learning approach, to pull out latent categories from the open-ends, which then guided a step-by-step content analysis protocol carried out by subject matter experts to identify category labels and descriptions. We will compare/contrast our results at the symposium and more generally discuss the potential for techniques of this kind to bring open-ends out of the shadows in survey research. The SoDa Center at UMD The powerful information available in large social science data sets is critical to understanding and addressing many of our nation and world’s most pressing challenges: from Covid-19 to racial, social and economic injustice; and from climate change to deep and damaging political and cultural divides. To help address these challenges, the University of Maryland has launched a new Social Data Science Center (SoDa) designed to advance research, education, and applications of social data measurement and analysis. This center leverages UMD’s strengths in survey methods, measurement, information management, visualization, and analytics. Facebook is providing support for the center’s research and education programs over the next three years. #SoDaUMD
Q&A- Rehabilitation of Open-ends: Carol Haney & Philip Resnik. Moderator: Olga Kononykhina -12/13/22
Presenter 1:<br />Carol Haney<br />Head of Research and Data Science Qualtrics<br /><br />Presenter 2:<br />Philip Resnik<br />Professor, Institute for Advanced Computer Studies and Department of Linguistics<br />University of Maryland<br /><br />Moderator:<br />Frauke Kreuter, <br />Co-Director, Social Data Science Center (SoDa)<br />Professor, Joint Program in Survey Methodology<br />University of Maryland<br /><br />Tuesday, December 13, 2022<br /><br />Abstract<br />Open-ends are a well known problem in survey research: language can yield extremely rich responses, including bringing to the surface aspects of a question or issue that the researcher might not have known to look for, but the analysis of text is costly and labor-intensive. As a result, there is a tendency to include open-ends as an afterthought, to use them minimally, or to avoid them altogether. Computational methods can potentially help, but they often raise concerns about whether the results they provide are as trustworthy and actionable as other kinds of responses.<br /><br />We will talk about approaches we've been taking to the analysis of open-ends, which combine automation with human intervention in order to navigate the balance between automation and trustworthiness. Two experiments were run independently on the same set of 16,648 responses on Reddit to a question about reasons that people who considered suicide did not end up killing themselves. The first experiment had human intervention at the start using a machine learning process that included word clouds and TF/IDF techniques to help human coders develop a codebook that was actionable. The second experiment used topic modeling, an unsupervised machine learning approach, to pull out latent categories from the open-ends, which then guided a step-by-step content analysis protocol carried out by subject matter experts to identify category labels and descriptions. We will compare/contrast our results at the symposium and more generally discuss the potential for techniques of this kind to bring open-ends out of the shadows in survey research.<br /><br />The SoDa Center at UMD<br />The powerful information available in large social science data sets is critical to understanding and addressing many of our nation and world’s most pressing challenges: from Covid-19 to racial, social and economic injustice; and from climate change to deep and damaging political and cultural divides. To help address these challenges, the University of Maryland has launched a new Social Data Science Center (SoDa) designed to advance research, education, and applications of social data measurement and analysis. This center leverages UMD’s strengths in survey methods, measurement, information management, visualization, and analytics. Facebook is providing support for the center’s research and education programs over the next three years.<br /><br />#SoDaUMD
SoDa Symposium: Rehabilitation of Open-ends - Carol Haney & Philip Resnik - December 13, 2022
Gabriella Waters - Director of Public Relations Center for Equitable AI and Machine Learning Systems (CEAMLS) AI Innovation, Global Impact, Integrative Influence Morgan State University Presenting: Reimagining Variance: AI and Neurodiverse PopulationsAbstract:Artificial intelligence (AI) has experienced exponential growth in application and innovation, and we’re only at the tip of the iceberg in exploring the many ways in which this technology can be deployed. Despite AI’s far-reaching influence, there are still populations that cannot access or engage with these technologies in a meaningful way. Neurodiversity isn’t always considered when designing the systems AI supports. As the methods used to diagnose and assess diverse neurological presentations become more sensitive and sophisticated, the need for attention to, and inclusion of neurodiverse populations is an area of significance. Given what we know about artificial intelligence, how can we capitalize on the strengths of the technology to ensure it is accessible for as many people as possible? With what we know about neurodiversity, how can we inform the development of new intelligent systems? Gabriella Waters is a data scientist, cognitive researcher, and the founder of Progressive Heuristics – a tech company focused on innovations that make artificial intelligence and neural network technologies more accessible and equitable across all populations. She is also the Director of Public Relations for Morgan State University’s Center for Equitable AI and Machine Learning Systems (CEAMLS). In her research, Gabriella is interested in studying the intersection between human neurobiology/learning and intelligent systems that make use of that foundation for improved human-computer synergy. She develops technology innovations, with an emphasis on support for neurodiverse populations, as well as the creation of more robust systems that function in the absence of human interaction. Jhillika Kumar CEO and Founder, Mentra Presenting: Empowering the Neurodiverse Workforce of the Future Abstract: At Mentra, we believe it is time that hiring platforms serve the neurodiversity of the human population. Today’s algorithms often screen out candidates primarily based on education background and employment history. This approach fails to take into account aptitude and non-traditional measures of success which can make or break a good job fit for a neurodivergent. To turn these barriers into bridges, Mentra’s platform collects holistic data on cognitive strengths, aptitude, and environmental sensitivities, and accommodations, avoiding the typical “screen out” of those who think differently. By considering data that goes far beyond the resume, Mentra’s AI determines a candidate’s likelihood of success in a particular role by maximizing job retention and job fulfillment. By showcasing a multi-dimensional profile to companies, Mentra acts as a translation layer between employers and neurodivergent professionals who may not display the behavior and experiences recruiters are accustomed with, unlocking a talent pool that has been immensely overlooked.Jhillika is the founder and CEO of Mentra, Georgia Tech founder, and honorary degree recipient, and previous Grace Hopper Keynote speaker for neurodiversity empowerment. Her experiences as the sister of a non-speaking autistic individual served as a catalyst for Jhillika to found of Mentra at the Georgia Institute of Technology to search for a solution to the need to empower those who society often overlooks. Moderator: Nancy Forsythe Senior Faculty Specialist/Disability Lead EmployABILITY Program Lead University Career Center & The President’s Promise University of Maryland
SoDa Symposium: Equal Input: Diversity, Equity, and Inclusion in Social Data Science Research
The iFARM Workshop, jointly organized by the U.S. Department of Agriculture’s (USDA) Economic Research Service (ERS) and the Social Data Science Center (SoDa) at the University of Maryland, was held on 20-21 October, 2022. The goal of the workshop was to initiate the discussion between researchers from different disciplines and countries in methods for collecting and processing food acquisition data.
International Food Acquisition Research and Methods (iFARM) Workshop, Day 1, Part 1
The iFARM Workshop, jointly organized by the U.S. Department of Agriculture’s (USDA) Economic Research Service (ERS) and the Social Data Science Center (SoDa) at the University of Maryland, was held on 20-21 October, 2022. The goal of the workshop was to initiate the discussion between researchers from different disciplines and countries in methods for collecting and processing food acquisition data.
International Food Acquisition Research and Methods (iFARM) Workshop, Day 1, Part 2
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