Methods Innovation

Three exciting possibilities for combining data science and social science

As the leader of a data science team at the Urban Institute, I get to work on interesting issues that intersect data science and social science every day. By data science, I mean technical tools, architectures, and processes that are borrowed from computer science and are atypical in the social sciences. This is a slightly more limited definition than most would have for the term data science, but because so much of what defines a data scientist at Urban also defines a researcher — cleaning data, analyzing it, visualizing results, etc. — my definition draws a finer line.

An interview with the Allen Institute for Artificial Intelligence, winners of the NYU Coleridge Initiative's Rich Context Competition

Earlier this year Allen AI were announced as the winners of the NYU Coleridge Initiative’s Rich Context Competition. The goal of the competition was to automate the discovery of research datasets and the associated research methods and fields of social science research publications. You can find out about all the finalists and their work here.

We caught up with Allen AI to talk about the work and their involvement in this year’s competition.

Event roundup: Future or fad? VR in social science research

At the end of February we ran a most enthralling event experience. Three panelists, two hosts and about 20 attendees all put their headsets on from their labs, offices and homes to join a virtual classroom decorated with trees, a castle, a slightly scary tiger and a hippo, to talk about the future of VR in social science research.

Social Science Foo Camp 2019

The second annual Social Science Foo Camp took place at Facebook’s headquarters in Menlo Park at the start of this month, convening an eclectic mix of more than 200 social scientists, technologists, funders, policy makers, businesspeople and writers.

Final results in NYU’s rich context competition to be webcast Feb 15

We are excited to announce that the finalists for the NYU Coleridge Initiative’s Rich Context Competition have been selected. The competition challenged computer scientists to find ways of automating the discovery of research datasets, fields and methods behind social science research publications.  20 teams from 8 countries submitted letters of intent and four finalists have been chosen. We will be live webcasting the finalists’ presentations as well as the announcement of the winner on February 15.

Starting out in computational social science

It’s an exciting time to be in social science. Social media, digital identities and the world of big data has opened up new ways for social scientists to study and examine social phenomenon.

Some examples include using online search patterns to predict the spread of disease, tracking near real-time Twitter data to understand political movements or using location data to understand interpersonal interactions.

The move to a digital world has created a innovative new area of social science called computational social science (CSS).

Training social scientists for the future

Calling all social scientists. How were you trained? How are you keeping up (or not) with new developments in this rapidly changing digital world? How are you training your students?

This was the subject of an event sponsored by SAGE Ocean as part of the ESRC’s 2018 Festival of Social Science. In case you are not aware, Sage, who have been at the forefront of publishing qualitative work, have now launched SAGE Ocean – an initiative “to help social scientists to navigate vast datasets and work with new technologies”.

Roundup: European Symposium on Societal Challenges in Computational Social Science

Last week, a mix of PhD students, early career and tenured researchers met in Cologne to discuss their latest projects around bias and discrimination on social media, and the algorithms underpinning many of the most pervasive services we use today.