Videos

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

Load More... Subscribe