Making sensitive text data accessible for computational social science

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.

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Skills, Teaching Methods, Tools & Technology Chris Burnage Skills, Teaching Methods, Tools & Technology Chris Burnage

How to get a DOI for your teaching materials with Zenodo

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?”

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Tools & Technology, Data Analysis Chris Burnage Tools & Technology, Data Analysis Chris Burnage

No more tradeoffs: The era of big data content analysis has come

For centuries, being a scientist has meant learning to live with limited data. People only share so much on a survey form. Experiments don’t account for all the conditions of real world situations. Field research and interviews can only be generalized so far. Network analyses don’t tell us everything we want to know about the ties among people. And text/content/document analysis methods allow us to dive deep into a small set of documents, or they give us a shallow understanding of a larger archive. Never both. So far, the truly great scientists have had to apply many of these approaches to help us better see the world through their kaleidoscope of imperfect lenses.

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Credit where credit is due: The startups, products and organizations giving academics credit for more of their work

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.

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Tools & Technology Chris Burnage Tools & Technology Chris Burnage

Social scientists working with LinkedIn data

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). 

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Instead of seeing criticisms of AI as a threat to innovation, can we see them as a strength?

At CogX, the Festival of AI and Emergent Technology, two icons appeared over and over across the King’s Cross location. The first was the logo for the festival itself, an icon of a brain with lobes made up of wires. The second was for the 2030 UN Sustainable Development Goals (SDGs), a partner of the festival. The SDG icon is a circle split into 17 differently colored segments, each representing one of the goals for 2030—aims like zero hunger and no poverty. The idea behind this partnership was to encourage participants of CogX—speakers, presenters, expo attendees—to think about how their products and innovations could be used to help achieve these SDGs.

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Tools & Technology, Data Analysis Chris Burnage Tools & Technology, Data Analysis Chris Burnage

2018 Concept Grant winners: An interview with MiniVan

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 chatted to MiniVan, a project of the Public Data Lab.

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Methods Innovation, Teaching Methods Chris Burnage Methods Innovation, Teaching Methods Chris Burnage

It’s good to share! Encouraging the sharing, reuse, and citation of teaching materials in computational social science

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!

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Teaching Methods Chris Burnage Teaching Methods Chris Burnage

'A great measure of our success is the community that SICSS creates'. Chris Bail and Matt Salganik on the Summer Institute in Computational Social Science

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.

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