Throughout history humanity has had the urge to predict the future. The Greeks consulted the Delphi Oracle, whereas the Romans inspected sheep entrails and modern day sages poke around tea leaves to get the skinny on the future. This desire to predict the future has found its way into finance where modern day Haruspices pop up on television to make confident boasts about the future direction of the share du jour. All, but the very fortunate of these modern day prophets fail at their impossible task.
Widely used apps like Facebook, Twitter or Google Maps count millions of users and are already deeply entrenched in our daily social life. However, while we know that mobile map applications are used quite often, we know very little about how they are used
A little over a week ago, I posted a blog celebrating 39 women in computational social science. We knew there would be so many more amazing researchers to add, and the social science community duly delivered, suggesting plenty of women that should also be celebrated. Therefore, rather fittingly on #AdaLovelaceDay we have published an updated list. The number has now more than doubled, and we hope that it is a good start for anyone looking for a supervisor for their PhD, or just wanting to see what other doctoral fellows are working on.
I want to share with you this list of 39 female researchers that are all crushing it in the social sciences and humanities with their innovative use of computational methods and very cool explorations of cutting edge tech. Follow them, read their papers and collaborate!
This month the Product Innovation team at SAGE have launched the SAGE Labs site that will showcase the experiments and projects that the team are currently working on.
What the Cambridge Analytica debacle and the resulting U.S. Senate hearing revealed in no uncertain terms is that the U.S. does not have adequate data privacy laws
Watch these short videos as leading academics present at this year’s inaugural Social Science Foo Camp discuss the opportunities and challenges presented by big data and the move to more computational methods.
Computational Social Science boils down to Social Scientists using data processing and data science computation tools (think R, Python etc) to analyze data about people and relationships.
In June, I attended the second iteration of the Summer Institute for Computational Social Science (SICSS), an intensive two-week program held at Duke that was intended to bring together researchers from across the social science and data science disciplines to learn and discuss topics in computational social science (CSS). Each day, the organizers Chris Bail and Matt Salganik taught mini-lectures on different CSS topics, we split into groups to work on activities together, and a speaker came in to present their research.
Today, there is a new window of opportunity to adopt agent computing as a mainstream analytic tool in economics. Here, I discuss four major aspects in which this technology can improve economic policymaking: causality and detail, scalability and response, unobservability and counterfactuals, and separating design from implementation. In addition, I highlight the crucial role that policy agencies and research funders have in this endeavor by supporting a new generation of computationally-enabled social scientists.
This project is a competition for researchers to build tools to help automate the discovery of data sets in the social sciences. The competition comes with prizes of $2,000 to each of the finalists, with up to $20,000 to be awarded to the winning team. Find out more and apply today.
Agent computing is a simulation tool that has been successfully adopted in many fields where policy interventions are critical. Economics, however, has failed in doing so. Today, there are new opportunities for bringing agent computing into economic policy. In this post, I discuss why this technology has not been adopted for economic policy and point out new opportunities to do it.
Ahead of this year’s APSA general meeting, we attended the Politics and Computational Social Science (PaCSS) pre-conference, hosted at Northeastern University. The event brought together political scientists working with large-scale data sets and emerging computational methods.
News media serves as a window into the society its readership represents. A newspaper’s description of a social group both demonstrates and constructs perceptions of that group within its audience. Understanding long-term trends or spatial differences in the representation of minority groups in news media can contribute to ongoing theoretical debates about the role and perception of minority groups in society.
Teaching and learning resources from the 2018 Summer Institute for Computational Social Science have been made free to access online, allowing more people to explore in depth the field of computational social science.
Fifty years after the "Summer of Love" transformed American youth culture, Andrew Anglin, the proprietor of the neo-Nazi website The Daily Stormer, announced to his followers that the summer of 2017 would be "The Summer of Hate."
In this follow-on post, we focus on data analysis and discuss co-occurrences of tags in images, and present an example of a co-occurrence network that represents a kind of "mental model" of SAGE journal images derived from the tags.
With so much diverse data to dig into, the future of quantitative social science is exciting, particularly for those studying the granularities of individual-level behavior. In doing so, we must make sure that this research is ethical, robust and ultimately useful