Bruce Desmarais Catalyzex Com

Bonisiwe Shabane
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bruce desmarais catalyzex com

Abstract:Despite recent advances in understanding the capabilities and limits of generative artificial intelligence (GenAI) models, we are just beginning to understand their capacity to assess and reason about the veracity of content. We evaluate multiple GenAI models across tasks that involve the rating of, and perceived reasoning about, the credibility of information. The information in our experiments comes from content that subnational U.S. politicians post to Facebook. We find that GPT-4o, one of the most used AI models in consumer applications, outperforms other models, but all models exhibit only moderate agreement with human coders. Importantly, even when GenAI models accurately identify low-credibility content, their reasoning relies heavily on linguistic features and ``hard'' criteria, such as the level of detail, source reliability, and language formality, rather than an understanding...

We also assess the effectiveness of summarized versus full content inputs, finding that summarized content holds promise for improving efficiency without sacrificing accuracy. While GenAI has the potential to support human fact-checkers in scaling misinformation detection, our results caution against relying solely on these models. I am the DeGrandis-McCourtney Early Career Professor in Political Science, Director of the Center for Social Data Analytics, and an Affiliate of the Institute for Computational and Data Sciences at Penn State University. In my research I develop and apply social data science methods to advance our understanding of systems of policymaking and digital communication with policymakers. Methodologically, I focus on machine learning, network analysis, generative artificial intelligence, and causal inference. I am also CEO and Co-Founder of Public Square Analytics LLC., a technology and consulting firm dedicated to the improvement of the digital public square.

I hold a Ph.D. in Political Science from the University of North Carolina at Chapel Hill (2010). Prior to coming to Penn State, I was an Assistant Professor in the Department of Political Science and an Affiliate of the Computational Social Science Institute at UMass Amherst (2010-2015). Department of Political Science | Institute for Computational and Data Sciences | Penn State Professor in the Department of Political Science and Faculty Co-Hire of the Institute for Computational and Data Sciences. Professor Desmarais' research focuses on the development and application of statistical methods in the study of Social and Political systems that are characterized by interdependence and structural complexity.

Network analysis is the primary methodological approach in his research. Areas of application include international conflict and cooperation among countries, campaign finance and co-sponsorship networks in the US Congress, digital communication networks in local government, diffusion of public policies across the US states, and... His current research agenda is generously supported by three grants from the US National Science foundation and one from the Russell Sage Foundation. Professor Desmarais holds a Ph.D. in Political Science from the University of North Carolina at Chapel Hill (2010) and a B.A. in Political Science and Economics from Eastern Connecticut State University (2005).

Prior to coming to Penn State, he was Assistant Professor in the Department of Political Science and an Affiliate of the Computational Social Science Institute at the University of Massachusetts Amherst (2010-2015). Leifeld, P., Cranmer, S., & Desmarais, B. (2018). Temporal Exponential Random Graph Models with xergm: Estimation and Bootstrap Confidence Intervals. Journal of Statistical Software. Linder, F.*, Desmarais, B., Burgess, M., & Giraudy, E.

Text as Policy: Measuring Policy Similarity Through Bill Text Reuse. Policy Studies Journal. Desmarais, B. Discussion of, "Inferring social structure from continuous-time interaction data." Loved by 700,000+ engineers & researchers Artificial intelligence models that generate text, images, videos, audio, code, and other data

Detecting vehicles, lanes, pedestrians, faces, contours, license plates etc. Personalizing content, tailoring product/service recommendations, etc. Style transfer from an image to another, super resolution (enhancement), image synthesis, etc. Deptartment of Political Science | Institute for Computational and Data Sciences | Penn State My lab's research is focused on understanding policymaking and policy-related digital communication through the advancement of the social data science toolkit. Approximately half of the Lab's work involves the development of computational and statistical methods in the domains of machine learning, network analysis, natural language processing, and causal inference.

The other half involves applications in the study of policymaking and digital communication systems, specifically on policy diffusion and cross-platform digital communication with subnational officials. The Lab's funding has been provided by the National Science Foundation, the Russel Sage Foundation and the Hewlett Foundation. Jack is a Ph.D. student in political science. His research agenda focuses on estimating causal effects in complex social networks. Methodologically, he studies causal inference (Pearl's approach), social network analysis (under complex systems), machine learning, and formal theory.

Specifically, he looks into (1) how the features of a specific social network can help predict nodes' behaviors, (2) how power, information, resources, and conflicts are spread and distributed via social ties that are... Rebekah Fyfe is a graduate student in political science. She studies international relations, with a focus on international political economy and supply chain networks. She is particularly interested in understanding what drives firm-level political behavior. Methodologically, she uses network analysis and causal inference in her research. Nitheesha Nakka is a Ph.D.

student in Political Science. Her research investigates political framing via different social media platforms including Twitter, YouTube and TikTok. She is more generally interested in applying computational methods (such as text analysis and machine learning) to understand online political communication and the impacts of these communications on offline political participation. Isaac Pollert is a postdoctoral scholar at the Center for Social Data Analytics. He received his Ph.D. in American Politics from the University of Illinois at Chicago in 2022.

His specific interests are in state policy and policy diffusion, with a particular focus on health and election policy. His recent work has focused on state legislative behavior and the effects of state policy on the health of infants and young mothers. In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all. This person’s work contributes towards the following SDG(s): Select a country/territory to view shared publications and projects Desmarais, Jr., B.

A. (PI), Rajtmajer, S. (CoPI) & Munger, K. (CoPI) Deringer, V. L.

V. L. (CoI), Nicholas, T. C. T. C.

(CoI) & Desmarais, Jr., B. A. (PI) Desmarais, Jr., B. A. (PI) & Olvera Esquivel, J.

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Abstract:Despite Recent Advances In Understanding The Capabilities And Limits Of

Abstract:Despite recent advances in understanding the capabilities and limits of generative artificial intelligence (GenAI) models, we are just beginning to understand their capacity to assess and reason about the veracity of content. We evaluate multiple GenAI models across tasks that involve the rating of, and perceived reasoning about, the credibility of information. The information in our expe...

We Also Assess The Effectiveness Of Summarized Versus Full Content

We also assess the effectiveness of summarized versus full content inputs, finding that summarized content holds promise for improving efficiency without sacrificing accuracy. While GenAI has the potential to support human fact-checkers in scaling misinformation detection, our results caution against relying solely on these models. I am the DeGrandis-McCourtney Early Career Professor in Political ...

I Hold A Ph.D. In Political Science From The University

I hold a Ph.D. in Political Science from the University of North Carolina at Chapel Hill (2010). Prior to coming to Penn State, I was an Assistant Professor in the Department of Political Science and an Affiliate of the Computational Social Science Institute at UMass Amherst (2010-2015). Department of Political Science | Institute for Computational and Data Sciences | Penn State Professor in the D...

Network Analysis Is The Primary Methodological Approach In His Research.

Network analysis is the primary methodological approach in his research. Areas of application include international conflict and cooperation among countries, campaign finance and co-sponsorship networks in the US Congress, digital communication networks in local government, diffusion of public policies across the US states, and... His current research agenda is generously supported by three grants...

Prior To Coming To Penn State, He Was Assistant Professor

Prior to coming to Penn State, he was Assistant Professor in the Department of Political Science and an Affiliate of the Computational Social Science Institute at the University of Massachusetts Amherst (2010-2015). Leifeld, P., Cranmer, S., & Desmarais, B. (2018). Temporal Exponential Random Graph Models with xergm: Estimation and Bootstrap Confidence Intervals. Journal of Statistical Software. L...