The 2020 SAGE Ocean Concept Grant program drew over 140 applications from all over the world. In this blog post, we’re giving you an insight into our judging criteria and sharing the most common reasons why applications did not progress further, to serve as feedback for this year’s applicants and guidance for future applicants.

And talking about gender equality in social data science means talking about the representation of women in tech and attitudes towards women in tech. It means confronting the stubborn prejudices and perceptions that women can’t code or can’t do stats. It means having a discussion about how as this new community of thought and practice is forming, we have a chance to make it look different than the communities that came before. And in particular, it seems vital to challenge ourselves to do so because of the questions social data scientists are asking and the methods they are using - because of the danger of biased algorithms, of reinforcing inequality through policies based on big but dirty data. 

Watch the panel discussion.

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.

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.

Text is everywhere, and everything is text. More textual data than ever before are available to computational social scientists—be it in the form of digitized books, communication traces on social media platforms, or digital scientific articles. Researchers in academia and industry increasingly use text data to understand human behavior and to measure patterns in language. Techniques from natural language processing have created a fertile soil to perform these tasks and to make inferences based on text data on a large scale.

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.

Today, researchers are using LinkedIn data in a variety of ways: to find and recruit participants for research and experiments (Using Facebook and LinkedIn to Recruit Nurses for an Online Survey), to analyze how the features of this network affect people’s behavior and identity or how data is used for hiring and recruiting purposes, or most often to enrich other data sources with publicly available information from selected LinkedIn profiles (Examining the Career Trajectories of Nonprofit Executive Leaders, The Tech Industry Meets Presidential Politics: Explaining the Democratic Party’s Technological Advantage in Electoral Campaigning). 

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!

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.

SAGE Research Methods has launched a new Data Science video collection, with hours of educational material for researchers of all levels and backgrounds.

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).

I recently got jazzed about two findings coming out of the world of computational social science, primarily because they hit so close to my home (hello, junior faculty feeling the pressure to produce)

Mirsad Hadžikadić, President of the Computational Social Science Society of the Americas (CSSSA) kicked off this year’s annual conference in Santa Fe.