Maskinlæring:
@[BOK:] Berk, R. (2016) Statistical learning from a regression perspective, chapter 1, 3, 5, 9. Springer: https://link.springer.com/book/10.1007%2F978-3-319-44048-4 (164) (Finnes også i papirversjon)
@Siegel, Eric (2011) Uplift modeling: Predictive analytics can’t optimize marketing decisions without it. The Prediction Impact white paper, Pitney Bowes: http://www.predictiveanalyticsworld.com/pdf/YTW03080USEN/Uplift-Modeling-Optimizes-Marketing-Decisions-White-Paper.pdf (24 sider)
@[BOK:] Luke, Douglas A. (2015) A User’s Guide to Network Analysis in R, Springer, chapter 1, 2 og 7, https://link.springer.com/book/10.1007%2F978-3-319-23883-8 (26 sider)
@[BOK] James, Witten, Hastie og Tibshirani (2014) An introduction to statistical learning, Springer, chapter 10 (“Unsupervised learning”), https://link.springer.com/book/10.1007%2F978-1-4614-7138-7 (46 sider)
@Bhagat, Cormode & Muthukrisnan (2011) “Node classification in social networks”, in Aggarwal (ed.) Social Network Data Analytics. Springer, Boston, MA s 115-147 (32 sider), https://link.springer.com/book/10.1007/978-1-4419-8462-3
Dataproduksjon
@Jae-Gil Lee, Minseo Kang (2015) “Geospatial Big Data: Challenges and Opportunities”, Big Data Research, 2(2): 74-81, http://dx.doi.org/10.1016/j.bdr.2015.01.003 (6 sider)
@Schneier, Bruce (2015) “Data as a by-product of computing”, i: Data and Goliath, New York: W.W. Norton & Company, (7 sider)
@van Dijck, José. (2014). “Datafication, dataism and dataveillance: Big Data between scientific paradigm and ideology”, Surveillance & Society 12(2): 197-208. (11 sider)
Sosiologiske perspektiver:
@Golder, Scott A., and Michael W. Macy. (2014) "Digital footprints: Opportunities and challenges for online social research." Annual Review of Sociology 40. (23 sider)
@Gunderson, Ryan (2016) “The sociology of technology before the turn to technology”, Technology in society, 47:40-48 (8 sider)
@McFarland, D.A., Lewis, K. & Goldberg, A. (2016) “Sociology in the Era of Big Data: The Ascent of Forensic Social Science”, American Sociologist, 47(12): 12-35. (20 sider)
@Metcalf, Jacob & Kate Crawford (2016) “Where are human subjects in Big Data research? The emerging ethics divide”, Big Data & Society, 3(1): 1-14 (14 sider)
Overvåking:
@Golder, O.H. (2017). “Surveillance and the Formation of Public Policy”. Surveillance & Society 15(1): 158-171. (13 sider)
@Esposti, S.D. (2014). “When big data meets dataveillance: The hidden side of analytics”. Surveillance & Society 12(2):209-225 (16 sider)
@Lysne et al (2016) Digitalt grenseforsvar (DGF), Lysne II utvalget, , kap 2, 4-7, 9-10 (48 sider)
@Frade, Carlos (2016) “Social Theory and the Politics of Big Data and Method”, Sociology, 50(5): 863-877, (14 sider)
@Zwitter, A. (2014). Big Data ethics. Big Data & Society 1, DOI:10.1177/2053951714559253 . (6 sider)
Sosiale medier:
@Barbier (2011) “Data mining in social networks”, in Aggarwal (ed.) Social Network Data Analytics. Springer, Boston, MA, s. 327-352 (25 sider)
@Bennett, C.J. (2015). "Trends in Voter Surveillance in Western Societies: Privacy Intrusions and Democratic Implications”. Surveillance & Society 13(3/4): 370-384. (14 sider)
@Bond, Robert M., et al. (2012) "A 61-million-person experiment in social influence and political mobilization." Nature 489.7415: 295-298. (4 sider)
@Brandsar, Torgeir, and Torkild Hovde Lyngstad. (2014) "Transaction data from social media: An introduction with an example on networks of members of the Norwegian parliament." Tidsskrift for samfunnsforskning, 55(1): 90-105. (15 sider)
@Kramer, Adam D. I., Jamie E. Guillory, and Jeffrey T. Hancock (2014), “Experimental evidence of massive-scale emotional contagion through social networks” (3 sider)
[BOK:] Tufekci, Z (2017) Twitter and tear gas. The power and fragility of networked protest, Yale University Press, kapittel 1,2, 6, 9 og Epilogue (130 sider)
Justisfeltet:
@Berk, R (2016) “A Primer On Fairness in Criminal Justice Risk Assessments”, The criminologist, 41(6): 6-9, (3 sider)
@Berk, Richard A., Susan B. Sorenson, Geoffrey Barnes (2016) “Forecasting Domestic Violence: A Machine Learning Approach to Help Inform Arraignment Decisions”, Journal of empirical legal studies, 13(1): 94-115 (21 sider)
[BOK:] Ferguson, AG (2017) The rise of big data policing. Surveilance, race, and the future of law enforcement, New York University press, kapittel 1, 2, 4, 10, Conclusion (72 sider)
@Leigh, J., Dunnett, S. & Jackson, L. Ann (2017). “Predictive police patrolling to target hotspots and cover response demand”, Operational Research, doi:10.1007/s10479-017-2528-x (16 sider)
@Lum K. & W. Isaac (2016) “To predict and serve?”, Significance, 13(5): 14-19 (5 sider)
@ Mohler, G. O. , M. B. Short, Sean Malinowski, Mark Johnson, G. E. Tita, Andrea L. Bertozzi & P. J. Brantingham (2015) “Randomized Controlled Field Trials of Predictive Policing”, Journal of the American Statistical Association, (12 sider)
Arbeidsliv:
@Frey and Osborne (2017) “The future of employment: How susceptible are jobs to computerisation?” Technological Forecasting & Social Change 114 (2017) 254–280 (26 sider)
@Srnicek, N. (2017), The challenges of platform capitalism: Understanding the logic of a new business model. Juncture, 23: 254–257. doi:10.1111/newe.12023 (3 sider)
[BOK:] Susskind, Richad & Daniel Susskind (2015) The future of the professions. How technology will transform the work of human experts, UK: Oxford University Press, s. 9-45 og 188-308 (156 sider)
@Murphy, J. (2016) Quality of hire: Data makes the difference, Employment relations, 43(2): 5-15, (10 sider)
@KG King (2016) Data Analytics in Human Resources: A Case Study and Critical Review, Human Resource Development Review 2016, Vol. 15(4) 487 –495 (8 sider)
Andre anvendelser:
@Gelman, Andrew, Greggor Mattson, Daniel Simpson (2018) “Gaydar and the Fallacy of Decontextualized Measurement”, Sociological Science, 5: 270-280, (10 sider)
@Thurston H. & S. Miyamoto (2018) The use of model based recursive partitioning as an analytic tool in child welfare, Child abuse & neglect 79: 293-301 (8 sider)
@Schwartz, I.M., P. York, E. Nowakowski-Sims, A. Ramos-Hernandez (2017) Predictive and prescriptive analytics, machine learning and child welfare risk assessment: The Broward County experience, Children and youth services review, 81: 309-20, (11 sider)