Suggested reading

Basics for the seminar:

Introduction to Critical Data Studies


  • Baack, S. (2015). Datafication and empowerment: How the open data movement re-articulates notions of democracy, participation, and journalism. Big Data & Society, 2(2), 2053951715594634.
  • Lupton, D., & Williamson, B. (2017). The datafied child: The dataveillance of children and implications for their rights. New Media & Society, 19(5), 780-794.
  • Pentland, Alex. 2013. “The Data-Driven Society.” Scientific American 309 (4) (October 1): 78–83. doi:10.1038/scientificamerican1013-78.
  • Rheinberger, Hans-Jörg (2007): Wie werden aus Spuren Daten, und wie verhalten sich Daten zu Fakten? In: Gugerli, David u.a. (Hrsg.): Nach Feierabend: Zürcher Jahrbuch für Wissensge-schichte, Nr. 3, S. 117-125.
  • Taylor, L., & Broeders, D. (2015). In the name of Development: Power, profit and the datafication of the global South. Geoforum, 64, 229-237.
  • Van Dijck, J. (2014). Datafication, dataism and dataveillance: Big Data between scientific paradigm and ideology. Surveillance & Society, 12(2), 197.

Data journeys and (social) life of data

  • Bates, J., Lin, Y. W., & Goodale, P. (2016). Data journeys: Capturing the socio-material constitution of data objects and flows. Big Data & Society, 3(2), 2053951716654502.
  • Beer, D., & Burrows, R. (2013). Popular culture, digital archives and the new social life of data. Theory, culture & society, 30(4), 47-71.
  • Buckland MK (1991) Information as thing. Journal of the American Society for Information Science 42(5): 351–360.
  • Christl, W., Kopp, K., & Riechert, P. U. (2017). CORPORATE SURVEILLANCE IN EVERYDAY LIFE.
  • Dieter, M., Gerlitz, C., Helmond, A., Tkacz, N., van der Vlist, F., & Weltevrede, E. (2018). Store, interface, package, connection. SFB 1187 Medien der Kooperation-Working Paper Series, (4), 1-16.


  • Beer, David. 2017. “The Social Power of Algorithms.” Information, Communication & Society, 20(1).
  • Bullynck M. (2016) “Histories of algorithms: Past, present and future.” Historia Mathematica, 43(3), 332–341.
  • Goffey, Andrew. 2008. “Algorithm.” In Software Studies: A Lexicon, edited by Matthew Fuller. Cambridge, Mass: MIT Press.
  • Gillespie T (2014) The relevance of algorithms. In: Gillespie T, Boczkowski PJ, Foot KA (eds) Media Technologies: Essays on Communication, Materiality, and Society, Cambridge: MIT Press, pp. 167–194.
  • Stalder F (2015) The algorithms that we want (in German)

Big data

  • Anderson C (2008) The end of theory: The data deluge makes the scientific method obsolete. Wired, 23 June 2008. Available at:
  • Bail, CA. (2014) “The Cultural Environment: Measuring Culture with Big Data.” Theory and Society 43 (3-4): 465–82.
  • Beer D (2016) How should we do the history of Big Data? Big Data & Society 3(1): 1–10.
  • boyd D, Crawford K (2012) Critical questions for Big Data: Provocations for a cultural, technological and scholarly phenomenon. Information, Communication & Society 15(5): 662–679.
  • Kitchin, R., & McArdle, G. (2016). What makes Big Data, Big Data? Exploring the ontological characteristics of 26 datasets. Big Data & Society, 3(1), 2053951716631130.
  • Metcalf, Crawford (2016) Where are human subjects in Big Data research? The emerging ethics divide. Big Data & Society 3(1): 1–14.
  • Puschmann C, Burgess J (2014) Metaphors of Big Data. International Journal of Communication 8(2014): 1690–1709.
  • Rieder, G., & Simon, J. (2016). Datatrust: Or, the political quest for numerical evidence and the epistemologies of Big Data. Big Data & Society, 3(1), 2053951716649398.
  • Aus Politik und Zeitgeschichte special issue Big Data 2015:
  • Zook M, Barocas S, boyd d, Crawford K, Keller E, Gangadharan SP, et al. (2017) Ten simple rules for responsible big data research. PLoS Comput Biol 13(3): e1005399. 10.1371/journal.pcbi.1005399

Open Data

  • Open Definition
  • Gurstein, M. B. (2011). Open data: Empowering the empowered or effective data use for everyone?. First Monday, 16(2).
  • Levy KEC, Johns DM (2016) When open data is a Trojan Horse: The weaponization of transparency in science and governance. Big Data & Society 3(1): 1–6.

Data Science and Computational Social Science

  • Gayo-Avello, D. (2013). A meta-analysis of state-of-the-art electoral prediction from Twitter data. Social Science Computer Review, 31(6), 649-679.
  • Grimmer (2015), We Are All Social Scientists Now: How Big Data, Machine Learning, and Causal Inference Work Together
  • Kramer ADI, Guillory JE, Hancock JT (2016) Experimental evidence of massive-scale emotional contagion through social networks. Proceedings of the National Academy of Sciences 111(24): 8788–8790.
  • Lazer, D., Pentland, A. S., Adamic, L., Aral, S., Barabasi, A. L., Brewer, D., ... & Jebara, T. (2009). Life in the network: the coming age of computational social science. Science (New York, NY), 323(5915), 721.
  • Lazer, D., Kennedy, R., King, G., & Vespignani, A. (2014). The parable of Google Flu: traps in big data analysis. Science, 343(6176), 1203-1205.
  • Mattmann, C. A. (2013). Computing: A vision for data science. Nature, 493(7433), 473-475.
  • Ruths, D. and Pfeffer, J., 2014. Social media for large studies of behavior. Science, 346(6213), pp.1063-1064.


  • Tufte, Edward R. 2011. “Visual & Statistical Thinking: Displays of Evidence for Making Decisions.” In Envisioning Information, 27–54. Cheshire, CT.: Graphics Press.
  • Yau, Nathan. 2013. “Representing Data.” In Data Points, 91–134. Hoboken: Wiley. Economy 6 (3): 313–35.
  • by Nikola Sander, Guy J. Abel & Ramon Bauer
  • by Max Rosner

Examples - "Bot Wars"

Case of Wikipedia:

Tsvetkova, M. et al (2017). Even good bots fight: The case of Wikipedia, Plos One, February 2017

Replication study, which failed to reproduce the results from study above: GEIGER, R. S., & HALFAKER, A. (2017). Operationalizing Conflict and Cooperation between Automated Software Agents in Wikipedia: A Replication and Expansion of “Even Good Bots Fight”. (preprint)

Detailed description:


  • Barocas, S., Bradley, E., Honavar, V., & Provost, F. (2017). Big Data, Data Science, and Civil Rights. arXiv preprint arXiv:1706.03102.
  • Borgman C (2015) Big Data, Little Data, No Data, Cambridge, MA: MIT Press
  • Bowker G, Baker K, Millerand F, (2010) Toward information infrastructure studies: Ways of knowing in a networked environment. In: Hunsinger J, Klastrup L, Allen M (eds) International Handbook of Internet Research, Dordrecht: Springer, pp. 97–117.
  • boyd, danah, Karen Levy, and Alice Marwick. 2014. “The Networked Nature of Algorithmic Discrimination.” Open Technology Institute.
  • Burrell, J. (2016). How the machine ‘thinks’: Understanding opacity in machine learning algorithms. Big Data & Society, 3(1), 2053951715622512.
  • Calo, Ryan and Rosenblat, Alex (2017): The Taking Economy: Uber, Information, and Power (March 9, 2017). Columbia Law Review, Vol. 117, 2017; University of Washington School of Law Research Paper No. 2017-08. Available at:
  • Chen, et al. “Enhancing Transparency and Control when Drawing Data-Driven Inferences about Individuals,” 2016 ICML Workshop on Human Interpretability in Machine Learning,
  • Edwards, P. N. (2010). A vast machine: Computer models, climate data, and the politics of global warming. Mit Press.
  • Elmer G, Langlois G, Redden J (2015) Compromised Data: From Social Media to Big Data, London: Bloomsbury.
  • Gitelman L (ed.) (2013) “Raw Data” Is an Oxymoron. Cambridge: MIT Press.
  • Jones ML (2016) Ctrl + Z: The Right to Be Forgotten, New York: New York University Press.
  • Kitchin R (2014) The Data Revolution: Big Data, Open Data, Data Infrastructures & Their Consequences, Los Angeles, CA: Sage
  • Kitchin R (2014a) Big Data, new epistemologies and paradigm shifts. Big Data and Society 1(1): 1–12.
  • Mackenzie, Adrian. 2015. “The Production of Prediction: What Does Machine Learning Want?” European Journal of Cultural Studies 18(4/5): 429–45.
  • Mayer, Katja. 2009. The sociometry of search engines, in: Becker, Stalder (Eds.): Deep Search, Studien Verlag, Innsbruck. DOI 10.5281/zenodo.199100
  • Nissenbaum, Helen. 2001. “How Computer Systems Embody Values,” IEEE Computer, 120, 118-119.
  • O'Neil, C. (2017). Weapons of math destruction: How big data increases inequality and threatens democracy. Broadway Books.
  • Pasquale, F. (2015). The black box society: The secret algorithms that control money and information. Harvard University Press.
  • Pomerantz, Jeffrey. 2015. Metadata. Cambridge, MA: MIT Press..
  • Reichert R (ed.) Big Data. Analysen zum digitalen Wandel von Wissen, Macht und Ökonomie. Bielefeld: transcript.
  • Robinson D, Yu H, Rieke A (2014) Civil Rights, Big Data, and our Algorithmic Future. Robinson + Yu. Available at:
  • Wallach, H. (2014). Big data, machine learning, and the social sciences: Fairness, accountability, and transparency. In NIPS Workshop on Fairness, Accountability, and Transparency in Machine Learning.
  • Ruhmann, Ingo und Ute Bernhardt. 2014. Information Warfare und Informationsgesellschaft. Beilage zu Wissenschaft und Frieden 1-2014 und zu FIfF Kommunikation 1-2014 Herausgegeben von der Informationsstelle Wissenschaft und Frieden und dem Forum InformatikerInnen für Frieden und gesellschaftliche Verantwortung e.V.