Social media has brought about rapid change in society, from our social interactions and complaint systems to our elections and media outlets. It is increasingly used by individuals and organizations in both the public and private sectors. Over 30% of the world’s population is on social media. We spend most of our waking hours attached to our devices, with every minute in the US, 2.1M snaps are created and 1M people are logging in to Facebook. With all this use, comes a great amount of data.

SAGE Campus are pleased to announce that we are launching two new courses to our suite of online data science courses for social scientists. The new short courses, Research Design in Social Data Science and Collecting Social Media Data, are aimed at those studying, teaching or working in social science disciplines who are looking to take their first steps toward working with big-data driven approaches to social science research.

We catch up with Ken Benoit, who developed Quanteda, a large R package originally designed for the quantitative analysis of textual data, from which the name is derived. In 2018, Quanteda received $35,000 of seed funding as inaugural winners of the SAGE Concept Grants program. We find out what challenges Ken faced and how the funding helped in the development of the package.

Following the launch of the SAGE Ocean initiative in February 2018, the inaugural winners of the SAGE Concept Grant program were announced in March of the same year. As we build up to this year’s winner announcement we’ve caught up with the three winners from 2018 to see what they’ve been up to and how the seed funding has helped in the development of their tools.

In this post, we spoke with the Digital DNA Toolbox (DDNA) winners, Stefano Cresci and Maurizio Tesconi about their initial idea, the challengers they faced along the way and the future of tools for social science research.

View the Get Creative! Research with Pictures & Stories webinar

Zoufan posted her last words on Weibo on 18, March, 2012. She was suffering from a major depressive disorder, and shortly after - committed suicide. Weibo is a microblogging application, launched by Sina Corporation back in 2009, based on user relationships to share, disseminate and get information. In essence, it is similar to Twitter, although it has a number of other useful capabilities. The app has more than 400 million users (compared to Twitter’s 300 million) and features that enable the study of emotional states and responses to the topics being discussed or spread across the web.

Last year saw the launch of Social Science One—a model devised to allow academic researchers access to the huge amounts of data generated by private industry, including Facebook data which will constitute the inaugural project. This week the first grants have been announced in partnership with the Social Science Research Council. Twelve projects have been awarded grants, as over 60 researchers come together from 11 countries and 30 academic institutions to study social media’s impact upon our society and democratic systems.

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.

Human social behavior has rapidly shifted to digital media services, whether Gmail for email, Skype for phone calls, Twitter and Facebook for micro-blogging, or WhatsApp and SMS for private messaging.  This digitalization of social life offers researchers an unprecedented world of data with which to study human life and social systems. However, accessing this data has become increasingly difficult.

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.

I would argue that computational social science necessitates collaboration, and indeed is tamed by it. A collaborative approach provides the necessary structure, goals, and a critical approach to research methods. In response to the question of what computational social science has helped me achieve, it may seem obvious to mention the concrete projects, the outputs, the measurable outcomes. However, for me computational social science has achieved something more substantial and enduring—a new way of working, a new way of thinking, and a new kind of enthusiasm for research.