4 research outputs found
Tracing the Use of Practices through Networks of Collaboration
An active line of research has used on-line data to study the ways in which
discrete units of information---including messages, photos, product
recommendations, group invitations---spread through social networks. There is
relatively little understanding, however, of how on-line data might help in
studying the diffusion of more complex {\em practices}---roughly, routines or
styles of work that are generally handed down from one person to another
through collaboration or mentorship. In this work, we propose a framework
together with a novel type of data analysis that seeks to study the spread of
such practices by tracking their syntactic signatures in large document
collections. Central to this framework is the notion of an "inheritance graph"
that represents how people pass the practice on to others through
collaboration. Our analysis of these inheritance graphs demonstrates that we
can trace a significant number of practices over long time-spans, and we show
that the structure of these graphs can help in predicting the longevity of
collaborations within a field, as well as the fitness of the practices
themselves.Comment: To Appear in Proceedings of ICWSM 2017, data at
https://github.com/CornellNLP/Macro
Prestige drives epistemic inequality in the diffusion of scientific ideas
The spread of ideas in the scientific community is often viewed as a
competition, in which good ideas spread further because of greater intrinsic
fitness, and publication venue and citation counts correlate with importance
and impact. However, relatively little is known about how structural factors
influence the spread of ideas, and specifically how where an idea originates
might influence how it spreads. Here, we investigate the role of faculty hiring
networks, which embody the set of researcher transitions from doctoral to
faculty institutions, in shaping the spread of ideas in computer science, and
the importance of where in the network an idea originates. We consider
comprehensive data on the hiring events of 5032 faculty at all 205
Ph.D.-granting departments of computer science in the U.S. and Canada, and on
the timing and titles of 200,476 associated publications. Analyzing five
popular research topics, we show empirically that faculty hiring can and does
facilitate the spread of ideas in science. Having established such a mechanism,
we then analyze its potential consequences using epidemic models to simulate
the generic spread of research ideas and quantify the impact of where an idea
originates on its longterm diffusion across the network. We find that research
from prestigious institutions spreads more quickly and completely than work of
similar quality originating from less prestigious institutions. Our analyses
establish the theoretical trade-offs between university prestige and the
quality of ideas necessary for efficient circulation. Our results establish
faculty hiring as an underlying mechanism that drives the persistent epistemic
advantage observed for elite institutions, and provide a theoretical lower
bound for the impact of structural inequality in shaping the spread of ideas in
science.Comment: 10 pages, 8 figures, 1 tabl
The Road to Success: Assessing the Fate of Linguistic Innovations in Online Communities
We investigate the birth and diffusion of lexical innovations in a large
dataset of online social communities. We build on sociolinguistic theories and
focus on the relation between the spread of a novel term and the social role of
the individuals who use it, uncovering characteristics of innovators and
adopters. Finally, we perform a prediction task that allows us to anticipate
whether an innovation will successfully spread within a community.Comment: 13 pages, Proceedings of the 27th International Conference on
Computational Linguistics (COLING 2018