Computational Sociology

The recent collision of computer science techniques with massive amounts of social data is creating a new institutional space: Computational Social Science. Right now this space is being filled by computer scientists, physicists, and applied mathematicians. I believe sociology needs to be at the center of this field. To move this field forward, and to make sure sociology isn't left behind, all sociology departments should be teaching the computer programming skills necessary to process and analyze big data, and physicists, mathematicians, and computer scientists who are analyzing social data should all be taught social science. Many of my activities are aimed at improving my own computational skills and building a community of social scientists who can process and work with big data, in particular big unstructured data.

My Computing Skills

Python: Pandas, NLTK, TextBlob, scikit-learn, StatsModel, gensim, matplotlib, BeautifulSoup

R: tm, stm, topicmodels, RTextTools, sna, igraph
Git

bash

Linux/UNIX

Zotero

Stata

SAS

Future and Past Events:

• SocInfo 2019 - Program Committee Chair
• Program Committees:
• SocInfo 2020 (senior PC)
• CWSM 2018-Present
• IC2S2 2016-Present
• TheWebConf 2018, 2019
• ICWS 2018
• International Conference on Computational Social Science
• 17-20 July 2020, MIT
• 17-20 July 2019, University of Amsterdam, Netherlands
• 12-15 July 2018, Northwestern University
• 10-13 July 2017, GESIS, Cologne, Germany
• 23-26 June 2016, Northwestern University
• Computational Social Science Summit, 15-17 May 2015, Northwestern University
• Big Cities, Big Data: Big Opportunities for Computational Social Science, 15-16 August 2014, D-Lab, UC Berkeley

Workshops I have taught:

Computer-Assisted Text Analysis:

• Digital Humanities Summer Institute 2017, University of California, Berkeley
• Digital Humanities Summer Institute 2017, The Claremont Colleges
• Digital Humanities Summer Institute 2016, University of California, Berkeley
• Computer-Assisted Text Analysis in R, D-Lab, University of California, Berkeley

Other Resources:

• The Hitchhiker's Guide to Python
• Neal Caren's tutorials on Python and text analysis
• Justin Grimmer's course on applied text analysis (pdf)
• Rodeo: A Data Science IDE for Python

Women in Tech

I'm passionate about making the tech scene more friendly toward women, and getting more women trained in computational methods. Here are some resources:

• Ada Initiative
• Girls who Code
• PyLadies

On Computational Social Science:

• "Computational social science: Obstacles and Opportunities."  David Lazer et al. Science.
• "Data ex Machina: Introduction to Big Data." David Lazer and Jason Radford. Annual Review of Sociology.
• "Big Data. Big Obstacles." Dalton Conley et al., The Chronicle of Higher Education.
• "Computational Social Science: Exciting Progress and Future Directions." Duncan J. Watts, National Academy of Engineering.
• "Computational Social Science." David Lazer et al. Science
• "Is Bigger Always Better?" Eszter Hargittai, The ANNALS of the American Academy of Political and Social Science.