Big Data research is rapidly expanding in its contribution across the social sciences and in public policy. We now have the ability to draw on rich, varied and linked datasets as well as new forms of data on behaviour, mobility, and social processes via the internet, social media and sensor data, generated in large volumes and in real time. At the same time, innovations in data science, new computational technologies, and novel analytical methods have the potential to unlock the potential of these datasets. Applying these theories and methods also raises ethical issues, making interdisciplinary collaboration essential for the application of big data to social science questions.
This interdisciplinary workshop brought together Cambridge research expertise in areas such as quantitative sociology, biostatistics, computing, mathematics, psychology, law and history and philosophy of science in order to explore what methodological insight can be offered from research advances in these disciplines.
The meeting featured a series of short talks, in-depth discussions and networking opportunities, allowing the opportunity to develop new connections and ideas for the application of big data to solve big questions in social science, as well as the societal, political and ethical implications of these new methods.
Read the Big Data Workshop Summary at the Cambridge Public Policy SRI website.
Programme at a glance
Workshop Chair: Professor Anna Vignoles, Faculty of Education
Time
|
Session
|
Presentations
|
09:00-09:30
|
Registration and welcome
|
|
09:30-11:00
|
People and place –Location and geospatial data
Chair: Jon Crowcroft, Computer Laboratory
|
Cecilia Mascolo, Computer Laboratory - Mobile Sensing and Geo-Social data analysis for Social Science
Steve Marsh, Computer Laboratory - Real-world insights through geospatial analysis
Elisabete Silva, Department of Land Economy - Soft artificial intelligence, linking socio-economic and land spatial-led data analysis for urban planning
Mike Bithell, Department of Geography - Is social data big data? Challenges for global social models
Panel Discussion
|
11:00-11:30
|
Break
|
|
11:30-13:00
|
Social media in social science and policy
Chair: Alex Kogan, Department of Psychology
|
David Stillwell, Psychometrics Centre - Predicting Psychology from Social Data
Joseph Chancellor, Department of Psychology - Combining data- and theory-driving insights using large, anonymous datasets of expressive online behavior
Rui Sun, Department of Psychology - Donations Predict Social Capital Gains for Low SES, But Not High SES Individuals and Countries
Kai Ruggeri, Department of Engineering - Using social media to inform global health policy: An example of major considerations regarding data for policy and policymakers
Panel Discussion
|
13:00-14:00
|
Lunch
|
|
14:00-15:30
|
Government and census data – linkage, search and analysis
Chair: David Howarth, Faculty of Law
|
Andrew Means, The Impact Lab - The Role of Prediction in the Targeting of Services
Mihály Fazekas, Department of Sociology - Exploring government administrative data to hold governments accountable in the Big Data Era
Miguel Morin, Faculty of Economics - Adapting to Workplace Technological Change over the Long Run: Evidence from US Longitudinal Data
Tanvi Desai and Aidan Condron, ADRN – Facilitating access to Administrative and Big Data in the UK: the Administrative Data Research Network and the Big Data Network
Panel Discussion
|
15:30-16:00
|
Break
|
|
16:00-17:30
|
New tools and methods
Chair: John Naughton, CRASSH
|
Gabriel Recchia, CRASSH - The Unreasonable Effectiveness of Co-occurrence Based Models
Liang Wang, Computer Laboratory - Needle in a Haystack: Understanding the Tradeoff between Accuracy and Efficiency in Searching of High Dimensional Big Data
Nigel Collier, Department of Theoretical and Applied Linguistics - Natural Language Processing for Digital disease detection in a fast-moving world
Panel Discussion and closing remarks
|
17:30-18:30
|
Drinks and Networking
|
|