Different social media platforms allow different levels of access to the data they hold for academic research. In this cross-post Daniela Duca explores some of the ways in which LinkedIn has been used by social scientists and provides a list resources for researchers looking to work with LinkedIn data.
Back in 2012, when LinkedIn was close to the 200 million users mark, a young but very computational (and quite resourceful) assistant professor, hustled through his contacts and somehow managed to get access to the trove of LinkedIn data. Prasanna Tambe—at the NYU Stern School of Business at the time—was not the first to use the information on LinkedIn for research, but definitely the first to use LinkedIn data to this scale. Tambe mined the skills and roles of all 175 million users at the time, though he probably ended up working with a smaller sample, to understand how the rapid evolution of skills and know-how in the technology sector is impacting investments in new IT innovations.
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).Most of these uses involve manual lookups and graduate students spending days to sift through the site, copy pasting the information into a spreadsheet. A LinkedIn API is available for larger scale datasets, but there are limitations—such as no more than 100k lifetime users, no storing of content, and it cannot be used for research purposes. If you had a large enough network, you could also download your network’s data and work with that csv output. Essentially, you need some computational skills to collect and use the LinkedIn data, and you would still be limited in the type of research you could do. Gian Marco Campagnolo, a Turing Fellow and lecturer at the University of Edinburgh used some LinkedIn data for his team’s research into the career evolution of IT professionals, but they still needed to get a list of names from another database.
The LinkedIn Economic Graph team continues to work with the data independently of academics, forming partnerships with organisations such as The World Bank Group. I was recently looking at the data made available (to the public through this collaboration) to explore the migration patterns of highly trained people from my home country. I was surprised to find that UK is now #2 after Romania. As the website states, in this first Digital Data for Development collaboration, the two organizations opened up an anonymized and aggregated dataset on “100+ countries with at least 100,000 LinkedIn members each, distributed across 148 industries and 50,000 skills categories”.