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). 

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.

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.

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.

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

By Timo Hannay

Here's a multiple-choice question: Is the internet (a) the most open, egalitarian and empowering means of communication ever devised, or (b) a dystopian nightmare populated by hucksters, trolls and miscellaneous abusers of human rights? The answer is, of course, (c) all of the above and much else besides. This stark contrast between the internet's light and dark sides has become a defining characteristic of the digital age, but is not an inevitable consequence of the mostly innocuous technologies on which it's built. Rather, it is the product of their bewilderingly diverse and eccentric user base – otherwise known as humanity.

In this Social Science Bite, Professor Gary King, uses text analysis as an example of this big data analysis... King, spotlights the difference between computer scientists’ goals and social scientists’ goals, then talks about work examining social media and censorship in China.

Data is being created faster than ever before however without access to these data-sets or the expertise to analyse them, research is confronted with a replication crisis and is vulnerable to commercial motivations. The problem is growing as Katie Metzler points out, "Firstly, because replication is the engine of science, and irreproducible research slows progress... secondly the motivations of industry researchers and social scientists may differ in ways that may really matter."