Completed Papers:

Ackermann, K., Angus, S.A., Raschky, P.A. (2016),  The Internet as Quantitative Social Science Platform: Insights from a Trillion Observations. , arXiv:1701.05632 [q-fin.EC]

With the large-scale penetration of the internet, for the first time, humanity has become linked by a single, open, communications platform. Harnessing this fact, we report insights arising from a unified internet activity and location dataset of an unparalleled scope and accuracy drawn from over a trillion (1.5×1012) observations of end-user internet connections, with temporal resolution of just 15min over 2006-2012. We first apply this dataset to the expansion of the internet itself over 1,647 urban agglomerations globally. We find that unique IP per capita counts reach saturation at approximately one IP per three people, and take, on average, 16.1 years to achieve; eclipsing the estimated 100- and 60- year saturation times for steam-power and electrification respectively. Next, we use intra-diurnal internet activity features to up-scale traditional over-night sleep observations, producing the first global estimate of over-night sleep duration in 645 cities over 7 years. We find statistically significant variation between continental, national and regional sleep durations including some evidence of global sleep duration convergence. Finally, we estimate the relationship between internet concentration and economic outcomes in 411 OECD regions and find that the internet’s expansion is associated with negative or positive productivity gains, depending strongly on sectoral considerations. To our knowledge, our study is the first of its kind to use online/offline activity of the entire internet to infer social science insights, demonstrating the unparalleled potential of the internet as a social data-science platform.


Ackermann, K. and Angus, S.A. (2014), A Resource Efficient Big Data Analysis Method for the Social Sciences: The Case of Global IP Activity , Procedia Computer Science, pp. 2360-2369.

This paper presents a novel and efficient way of analysing big datasets used in social science research. We provide and demonstrate a way to deal with such datasets without the need for high performance distributed computational facilities. Using an Internet census dataset and with the help of freely available tools and programming libraries, we visualize global IP activity in a spatial and time dimension. We observe a considerable reduction in storage size of our dataset coupled with a faster processing time.

Ongoing Research:


  • Ackermann, K., Angus, S.A., Lassiter, A., Raschky, P.A.,  Internet and Regional Economic Growth
  • Ackermann, K., Angus, S.A., Hodler, R., Raschky, P.A.,  Internet and Political Mobilization
  • Ackermann, K., Angus, S.A., Oberhofer, H., Raschky, P.A.,  Internet and Firm Productivity