The 2nd-4th September 2019 marked the third in a series of symposia on Societal Challenges in Computational Social Science (Euro CSS). Computer scientists, political scientists, sociologists, physicists, mathematicians and psychologists from 24 countries gathered in Zurich for a day of workshops and tutorials followed by a two-day one track conference.
On the 28th of August, we visited sunny Georgetown University to discuss all things politics and Computational Social Science for the second annual PaCSS conference. Here are our highlights.
Coming from a social science background, I have had very limited exposure to data science. I was therefore excited to learn about the emerging field of computational social science and the Summer Institute in Computational Social Science (SICSS) presented the right opportunity. I applied to the 2019 SICSS and I was accepted for the Cape Town partner site. I went in not knowing what to expect but by the end of the first day I knew the experience at the two-week Summer Institute was going to be truly worthwhile.
Whether you call it ‘content analysis’, ‘textual data labeling’, ‘hand-coding’, or ‘tagging’, a lot more researchers and data science teams are starting up annotation projects these days. Many want human judgment labeled onto text to train AI (via supervised machine learning approaches). Others have tried automated text analysis and found it wanting. Now they’re looking for ways to label text that aren’t so hard to interpret and explain.
The baton was passed to the University of Amsterdam for the fifth addition of IC2S2 with the core conference taking place between 18-20 July.
Academics face various pressures, from research teaching and administrative duties. The best way to create a positive culture in academia is to share. However, it may sometimes feel like there is no incentive to share teaching materials, if I have spent so many hours developing this work, why should I just hand it over to someone, “what’s in it for me?”
It’s all about incentives. The current academic ecosystem incentivises publication in high impact factor journals and grant capture above all else, but there is more to being an academic than producing journal articles and winning grants. Luckily there are an increasing number of initiatives that are helping academics get credit for more of the work they do and increase their broader impact. This post rounds up some of the most interesting efforts.
The beginning of term is nearing. You’re teaching a new module on Computational Social Science (CSS). The field is developing rapidly and so are best practices around teaching the theory, methods and techniques to students.
Where do you start when you’re putting together your teaching materials? Do you visit the websites and blogs of academics who are experienced in teaching CSS to look for resources? Do you search online for syllabi, reading lists and tutorials? Maybe you scour YouTube for videos to include in your slides?
Together with a group of UK academics, the SAGE Ocean team have been digging into where academics go to find teaching materials and what the barriers are for academics who want to share, reuse and give and get credit for the materials they produce for teaching. This post includes thoughts from the group on what’s needed to promote a stronger culture of sharing teaching materials in CSS. And we’ve curated a list of our favorite resources for you too!
As the participants gear up for the 2019 Summer Institute in Computational Social Science (SICSS), starting June 16th at Princeton and the 11 alumni-led partner locations situated right across the globe, we caught up with the founders of the SICSS, Chris Bail and Matt Salganik to find out how it all got going, the move to a data intensive society and the benefits of learning data science skills to make the most of this new data.
Sam Gilbert demonstrates the value of big search data for social scientists, and suggests some practical steps to using internet search data in your own research.
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.
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.
Watch our panel event from the ESRC Festival of Social Science on what skills social scientists will need to be able to do the social research of the future.
SAGE Research Methods has launched a new Data Science video collection, with hours of educational material for researchers of all levels and backgrounds.
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.
Find out more about the field of collective intelligence by tuning into these vox pops, filmed
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.
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.
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).
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”.