Hi all,
Please note that we will be live streaming the workshop on computational
social science (program below). The url:
http://video.isites.harvard.edu/liveVideo/liveView.do?name=Comp_Soc_Science
The Twitter hashtag is: #compsocsci12. We will monitor this hashtag
during the workshops to enable remote Q&A.
If you would like to embed the stream in your website, use this code:
<iframe src="
http://video.isites.harvard.edu/liveVideo/liveEmbed.do?name=Comp_Soc_Science&width=auto&height=auto
" width="640" height="360" style='border: 0px;'></iframe>
Please feel free to forward this e-mail on to interested parties, and if
this has been forwarded to you, and you would like to be added to the list,
please contact [log in to unmask]
best,
David
*SPRING WORKSHOP ON COMPUTATIONAL SOCIAL SCIENCE*
May 30- June 1, 2012
The Institute for Quantitative Social Science, Harvard University
Contact [log in to unmask] to register (note registration fee $50/day)
Space is limited!
Note: a light lunch and afternoon coffee will be served.
Sponsored by:
The Northeastern Centers for Computational Social Science and Digital
Humanities
The Institute for Quantitative Social Science, Harvard
The Human Dynamics Lab, MIT
*May 30*
8:30am-9am: *Registration*
9-10am: *Opportunities and challenges in the study of digital traces*
David Lazer, welcome and introductory remarks
10am-6pm (with breaks)
*Workshop 1: From Tweets to Results: How to obtain, mine, and analyze
Twitter data*
Derek Ruths (McGill University)
Since Twitter's creation in 2006, it has become one of the most
popular microblogging platforms in the world. By virtue of its popularity,
the relative structural simplicity of Twitter posts, and a tendency towards
relaxed privacy settings, Twitter has also become a popular data source for
research on a range of topics in sociology, psychology, political science,
and anthropology. Nonetheless, despite its widespread use in the research
community, there are many pitfalls when working with Twitter data.
In this day-long workshop, we will lead participants through the
entire Twitter-based research pipeline: from obtaining Twitter data all the
way through performing some of the sophisticated analyses that have been
featured in recent high-profile publications. In the morning, we will
cover the nuts and bolts of obtaining and working with a Twitter dataset
including: using the Twitter API, the firehose, and rate limits; strategies
for storing and filtering Twitter data; and how to publish your dataset for
other researchers to use. In the afternoon, we will delve into techniques
for analyzing Twitter content including constructing retweet, mention, and
follower networks; measuring the sentiment of tweets; and inferring the
gender of users from their profiles and unstructured text.
We assume that participants will have little to no prior experience
with mining Twitter or other social network datasets. As the workshop will
be interactive, participants are encouraged to bring a laptop. Code
examples and exercises will be given in Python, thus participants should
have some familiarity with the language. However, all concepts and
techniques covered will be language-independent, so any individual with
some background in scripting or programming will benefit from the workshop.
*May 31*
9am-5pm (with breaks): *Workshop 2: Network Visualization*
Yu-Ru Lin (Northeastern/Harvard Universities)
The recent availability of new cutting edge datasets such as open
government data, cell phone call records and social media communication
streams offers unprecedented opportunities to study human behaviors and
their relationship to the social system. Relationships between various
types of entities arise naturally in the study of social networks as well
as many applications such as information retrieval and business
intelligence. The interrelated information can be effectively represented
as networks, where nodes are various types of entities and edges are
relationships. Network visualization serves as a powerful tool to build
intuitions, to systematically explore the structures or peculiar patterns
of the data, and to communicate findings.
This tutorial aims to provide practical knowledge on network visualization,
using the open source tool Gephi. The tutorial will cover three components:
(1) Understand the visual complexity and an effective way of communicating
networked data.
(2) Convey network properties and structure through Gephi’s functionality.
(3) Use Gephi’s advanced features to explore the networks of political
contributions, political texts, etc. The tutorial is intended for scholars
and researchers who wish to learn how to incorporate network visualization
to speed up the data exploration and to communicate the data insights.
Requirements: Familiarity with basic network concepts is preferred but not
essential. Participants should come with their own laptop with Gephi
installed (The installation instructions will be given to the participants
prior to the tutorial).
*June 1*
10am-12pm: *Self-organized discussions*
This will be an opportunity for workshop participants to organize into
groups to discuss particular opportunities and challenges in specific
substantive domains.
1pm-5pm: *Workshop 3: Studying the dynamics of human proximity*
Human Dynamics Lab, MIT/ Prof. Alex Pentland, Director.
During the last decade we have developed measurement toolkits based on
electronic badges, smart phones, and signal processing that allow us to
accurately quantify human behavior in everyday situations on a continuous
basis over long time periods. In this tutorial we will describe the
sociometric badges and Android platform sociometric software that we have
developed, covering their function, capability, and typical use. These
tools will be made available to interested participants. We will also
cover the mathematical toolkit we have developed, describing the theory,
capability, and typical use. These tools will also be made available to
participants.
Finally, we will illustrate the use of our sociometric measurement
tools together with our mathematical analysis tools on a variety of
problems, including individual (e.g., passive screening for health
problems), small group (e.g., providing a real-time performance meter for
groups), organizations (e.g., reengineering communication patterns for
greater productivity), and large-scale sociocultural outcomes (e.g.,
diabetes risk, crime risk). For additional information see
http://hd.media.mit.edu
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