EP 6 — Penn State’s Bruce Desmarais on Social Data Analytics and Combating Misinformation

In this episode of The Data Fabric Show, Kaycee speaks with Bruce Desmarais, PhD, Professor & Director of the Center for Social Data Analytics, Penn State University. Together, they dive into the role of advanced data science tools in analyzing social media data to address issues like misinformation and online bullying. 

 

Bruce shares insights on the collaboration between academia and tech companies. The conversation also explores the transformative potential of generative AI in social research, including the creation of synthetic social systems for experimentation. 

 

Topics discussed:

 

• The significance of social data analytics in understanding human behavior and communication through digital traces left on social media.
• Challenges researchers face in accessing and integrating diverse data sources, especially with changing data formats and proprietary data restrictions from social media companies.
• The importance of collaboration between academia and industry in addressing real-world problems, with examples of successful partnerships that enhance data analytics efforts.
• The role of generative AI in streamlining data analysis processes, including its potential to automate data tagging and quality checks in research.
• Ethical considerations in social research, particularly when experimenting with interventions on social media platforms and the implications for freedom of speech.
• The impact of generative AI on survey design and data collection, allowing researchers to generate realistic synthetic responses for more efficient analysis.
• The need for data validation and quality checking in research, emphasizing the importance of unit testing to ensure accurate results from AI-generated outputs.
• The future of social data analytics and the potential for creating synthetic social systems to explore complex interactions without ethical concerns or real-world consequences.
• The ongoing challenges of data governance and security in enterprise analytics, highlighting why many organizations are hesitant to fully adopt generative AI technologies.