Jaideep Srivastava
University of Minnesota, MN·Computer Science
People increasingly go online for health advice, services and support. Yet scams and fake material make it hard to know what to trust, and this loss of trust spreads to major institutions.
- Fields
Computer Science
Social Science
- Topics
misinformation
information authenticity
social network analysis
- Grants
- 1 known grant, through Mar 2027
What they work on
- Investigate how information spreads through online social networks and develop tools that trace content back to its original sources and identify authentic information.
- Study how resilient audiences are to inauthentic material using inoculation theory, and test how peer networks can be used to spread authentic information.
- Build vulnerability assessment models that identify the most effective content labels and the individuals and communities most in need of information support.
More about the research
This project carries out basic research toward a framework that helps people find and share safe, accurate information. Rather than judging specific types of content, the framework focuses on the structure of social networks, meaning who is connected to whom and how information travels between them.
Why it matters
Tools that trace information to its original source and reach vulnerable communities through their peer networks could help people avoid scams and unreliable advice online. Insights into which content labels work best could guide platforms and public agencies in strengthening trust in online information and in the institutions that rely on it.
Known grants
From NIH and NSF research grants. Labs often have other funding we can't see.
Closest labs
Filippo Menczer
Indiana University, IN·Computer Science
Build an agent-based simulation of how content spreads through human social networks, modeling individual traits and network structure to predict large-scale information flow.
Carolyn Parkinson
University of California-Los Angeles, CA·Psychology·Early-career
Map the real-world social networks of several bounded communities, then use behavioral and neuroimaging experiments with their members to measure how knowing others' network positions shapes thought, emotion and behavior.
Kenneth Joseph
University at Buffalo, NY·Computer Science·Early-career
Develop mixed methods combining graph-based and natural-language machine learning with qualitative analysis to identify dominant beliefs about social issues in large datasets from X and Parler.
The school
University of Minnesota: 88 labs on PI Pages, $1.4B a year on research, 722 research doctorates a year.
Reach out
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