The Mimir Project Page: What Drives the Dynamics of Science?
Our multidisciplinary project draws upon sociology, computer
science, and linguistics to study how ideas are created and propagate
through scientific communities, how these communities are formed and
change over time, and how multidisciplinary networks spanning these
communities shape scientific innovation. We are creating sophisticated
new computational models for extracting and representing ideas and
measuring their impact and novelty, and for extracting and
representing social relations and identifying forms of
multidisciplinary collaboration. Our methods integrate the network
analytic tools of social science with the language processing tools of
computer science. We use network analysis to improve the ability of
computational tools to identify ideas in scientific texts, and we use
the tools of computational linguistics to help explain the
co-evolution of scientific collaborations and innovations. We are
using our models of ideas and their diffusion to investigate
hypotheses such as whether multidisciplinary research accelerates or
decelerates scientific innovation, and how multidisciplinarity
influences student and faculty careers. We combine a shallow
large-scale study of knowledge corpora (ISI Web of Knowledge, Proquest
dissertations, NSF/NIH grants, US Patent Office, Federal committees)
with a richer organization-level study of Stanford University (their
publications, grants, affiliations, advising, teaching, etc) in order
to explore and analyze the complex interrelationships of innovation
and multidisciplinary collaboration.
Our research agenda is to produce new and unique data, create new
computational tools, and extend theory so that scholars change their
conceptions of scientific innovation, multidisciplinarity, and
research communities more generally. Our integration of social network
and natural language processing techniques is helping develop a new
vein of research in computational social science, simultaneously
offering empirical rigor and scale to the sociology of science and
extending natural language processing from its previous engineering
focus toward true explanatory social science models.
- Post-Docs and Research Associates
You can find more and more up to date information at this site:
The Stanford Topic Modeling Toolbox
Nallapati, R., X. Shi, D. McFarland, J. Leskovec & D. Jurafsky. 2011. LeadLag LDA: Estimating Topic Specific Leads and Lags of Information Outlets. International AAAI Conference on Weblogs and Social Media (ICWSM).
Rawlings, C. & D. McFarland. 2011. "The Ties that Influence: How Social Networks Channel Faculty Grant Productivity." Social Science Research.
Chuang, J, D. Ramage, J. Heer, & C. Manning. 2009. Thesis Explorer.
Krawczyk, S. 2010. Semi-supervised Learning For Author Disambiguation in Bibliographic Data. Master's thesis in computer science, Stanford University.
Nallapati, R., D. McFarland & C. Manning. 2011. TopicFlow Model: Unsupervised Learning of Topic-specific Influences of Hyperlinked Documents. Artificial Intelligence and Statistics (AISTATS).
Xiaolin Shi, Ramesh Nallapati, Jure Lescovec, Dan McFarland and Dan Jurafsky.
2010. Who Leads Whom: Topical Lead-Lag analysis across corpora.
NIPS Workshop on Computational Social Science and Wisdom of Crowds.
Daniel Ramage, Christopher D. Manning, and Daniel A. McFarland. 2010.
Which universities lead and lag? Toward university rankings based on scholarly output.
NIPS 2010 Workshop on Computational Social Science and the Wisdom of the Crowds.
Xiaolin Shi, Jure Leskovec, Daniel A. McFarland. 2010.
Citing for High Impact. Joint Conference on Digital Libraries (JCDL), 2010.
Steven Bethard and Dan Jurafsky. 2010.
Who should I cite? Learning literature search models from citation behavior. In ACM Conference on Information and Knowledge Management.
Ramesh Nallapati and Christopher Manning. 2010.
TopicFlow model: Unsupervised learning of topic specific influences of hyperlinked documents.
Neural Information Processing Systems Workshop on Machine Learning for Social Computing.
Daniel Ramage, Evan Rosen, Jason Chuang, Christopher D. Manning and Daniel A. McFarland. 2010.
Topic Modeling for the Social Sciences.
NIPS 2009 Workshop on Applications for Topic Models.
Daniel Ramage, David Hall, Ramesh Nallapati, and Christopher Manning. 2009.
Labeled LDA: a supervised topic model for credit attribution in multi-labeled corpora.
- Chuang, Jason, Daniel Ramage, Jeff Heer, and Christopher Manning. 2009. Thesis Topic Explorer. http://graphics.stanford.edu/~jcchuang/thesis-explorer/
Christopher D. Manning,
and Daniel A. McFarland.
Topic Modeling for the Social Sciences.
In NIPS 2009 Workshop on Applications for Topic Models: Text and Beyond.
- David LW Hall, Daniel Jurafsky, and Christopher D. Manning. 2008.
Using Topic Models to Study the History of Ideas. EMNLP 2008
- Biancani, Susan, Daniel A. McFarland, Linus Dahlander and Lindsay Owens.
Extending Intellectual Communities: How Interdisciplinary Centers Extend
Traditional Disciplinary Associations. Presented at Association for the
Study of Higher Education in November 2009.
- Dahlander, Linus and Daniel McFarland. 2009b. Intellectual Tie Formation:
Multiple Networks and Refunctionality. Presented at the annual SUNBELT
meeting of the International Network for Network Analysis (April 2009) and
the Academy of Management in August 2009.
- Rawlings, Craig and Daniel McFarland. The Ties That Influence. Presented at
the annual SUNBELT meeting of the International Network for Network Analysis
- Rawlings, Craig, Linus Dahlander, Dan Wang, and Daniel McFarland.
Contemporary Forms of the Academic Life: Social Structure and Knowledge
Structure in 74 University Departments, 1993 to 2007. Presented at
Association for the Study of Higher Education in November 2009.
If you have papers that are contained in the ISI Web of Knowledge
, please consider providing your publication information
to help us match authors to publications.