- Message from the Chair
- Info Sci Colloquium
- High-stakes decisions from low-quality data: Learning and planning for wildlife conservation
- Dynamic Allocation of Scarce Resources
- Augment Human Thought and Creativity with the Power of AR and AI
- Content Curation in Online Platforms
- Generative AI for the Cyberphysical World
- Making Better Decisions with Human-AI Teams
- Operationalizing Responsible Machine Learning: From Equality Towards Equity
- Data Values: Digital Surveillance and the New Epistemology of Psychiatry
- Computational Methods for Police Oversight and Reform Under Incomplete Data
- Why the First Amendment Protects Misinformation, and Why It Should Continue to Do So
- Tech / Law Colloquium
- IS Engaged
- Graduation Info
- Ethics and Politics in Computing Colloquium
- Info Sci Colloquium
- Contact Us
- Computational Social Science
- Critical Data Studies
- Data Science
- Economics and Information
- Education Technology
- Ethics, Law and Policy
- Human-Computer Interaction
- Human-Robot Interaction
- Incentives and Computation
- Infrastructure Studies
- Interface Design and Ubiquitous Computing
- Natural Language Processing
- Network Science
- Social Computing and Computer-supported Cooperative Work
- Technology and Equity
Please join us for the Information Science Colloquium with guest, David Mimno, a postdoctoral researcher in the Computer Science department at Princeton University. He received his PhD from the University of Massachusetts, Amherst. Before graduate school, he served as Head Programmer at the Perseus Project, a digital library for cultural heritage materials, at Tufts University. He is supported by a CRA Computing Innovation fellowship.
Title: Building scholarly methodologies with large-scale automated topic analysis.
Abstract: In the last ten years we have seen the creation of massive digital text collections, from Twitter feeds to million-book libraries. At the same time, researchers have developed text mining methods that go beyond simple word frequency analysis to uncover thematic patterns. When we combine big data with powerful algorithms, we enable analysts in many different fields to enhance qualitative perspectives with quantitative measurements. But these methods are only useful if we can apply them at massive scale and distinguish consistent patterns from random variations. In this talk I will describe my work building reliable topic-mining methodologies for humanists, social scientists and science policy officers.
Information Science Colloquium talks are free and open to the public.
A reception will be held immediately after.