Akaike, H. (1974). “A new look at the statistical model identification”.
IEEE Transactions on Automatic Control. 19(6): 716-723. doi:
10.1109/TAC.1974.1100705.
Anderson, C. (2008). “The end of theory, will the data deluge makes the scientific method obsolete?:.
Edge. retrieved at 12 September 2023 [Online] from:
http://www.edge.org/3rd_culture/anderson08/anderson08_index.html.
Andrejevic, M. (2013). Infoglut: How too Much Information Is Changing the Way We Think and Know. Routledge.
Badiou, A. (2008). Number and Numbers. Cambridge: Polity.
Bellman, R. (1961). Adoptive Control Processes: A Guided Tour. University Press.
Benjamini, Y. & Hochberg, Y. (1995). “Controlling the false discovery rate: a practical and powerful approach to multiple testing”.
Journal of the Royal Statistical Society: Series B (Methodological). 57(1): 289-300.
https://www.jstor.org/stable/2346101.
Berman, J.J. (2013). Principles of Big Data: Preparing, Sharing, and Analyzing Complex Information. Newness.
Berry, D. (2011). “The computational turn: Thinking about the digital humanities”.
Culture Machine. 12. Retrieved at 08 September 2023 [Online] from:
http://www.culturemachine.net/index.php/cm/article/view/440/470.
Beyer, K.; Goldstein, J.; Ramakrishnan, R. & Shaft, U. (1999). “When is ‘nearest neighbor’ meaningful?”. Database Theory—ICDT’99: 7th International Conference Jerusalem, Israel, January 10–12, 1999 Proceedings 7: 217-235. Springer Berlin Heidelberg.
Borgatti, S.P. & Halgin, D.S. (2011). “Network theorizing”.
Organization Science. 22(5): 1168-1181.
https://doi.org/10.1287/orsc.1100.0641.
Bowker, G.C. & Star, S.L. (2000). Sorting Things out: Classification and its Consequences. MIT press.
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.
https://doi.org/10.1080/1369118X.2012.678878.
Breiman, L. (2001). “Random forests”.
Machine Learning. 45: 5-32.
http://dx.doi.org/10.1023/A:1010933404324.
Chawla, N.V.; Bowyer, K.W.; Hall, L.O. & Kegelmeyer, W.P. (2002). “SMOTE: Synthetic minority over-sampling technique”.
Journal of Artificial Intelligence Research. 16(2002): 321-357.
https://doi.org/10.1613/jair.953.
Crotty, M.J. (1998). The foundations of social research: Meaning and perspective in the research process. London: Routledge.
Couldry, N. (2014). “Inaugural: A necessary disenchantment: Myth, agency and injustice in a digital world.”
The Sociological Review, 62(4), 880-897.
https://doi.org/10.1111/1467-954X.12158.
---------------. (2020). Recovering critique in an age of datafication.
New Media & Society, 22(7): 1125-1336.
https://doi.org/10.1177/1461444820912536.
Derrida, J. (1996). Archive Fever: A Freudian Impression. Translated by Prenowitz E. University of Chicago Press, Chicago.
Durbin, J. & Koopman, S.J. (2012). Time Series Analysis by State Space Methods. Vol. 38. OUP Oxford.
Durkheim, E. (1982[1895]). Rules of Sociological Method. New York: The Free Press.
Espeland, W.N. & Stevens, L.M. (1998). “Commensuration as a social process”. Annual Review of Sociology. 24(1): 313–343.
Eubanks, V. (2018).
Automating Inequality: How High-Tech Tools Profile, Police, and Punish the Poor. St. Martin’s Press.
https://www.jstor.org/stable/223484.
Fairclough, N. (2013). Critical Discourse Analysis: The Critical Study of Language. Routledge.
Fan, J. (2008). “Sure indepedence screening for ultra-high dimensional feature space”.
JR Stat Soc B. 70(5): 849-911.
https://doi.org/10.1111/j.1467-9868.2008.00674.x.
Fan, J. & Li, R. (2006). “Statistical challenges with high dimensionality: Feature selection in knowledge discovery”. arXiv preprint math/0602133. 595-622.
Freeman, L.C. (1977). “A set of measures of centrality based on betweenness”.
Sociometry. 40(1): 35-41.
https://doi.org/10.2307/3033543.
Gayo-Avello, D.; Metaxas, P.T. & Mustafaraj, E. (2011). Predicting Elections with Twitter: What 140 Characters Reveal about Political Sentiment. Proceedings of the International Conference on Weblogs and Social Media (ICWSM).
Geertz, C. (1973). The Interpretation of Cultures: Selected Essays. Basic Books.
Gerlitz, C. & Lury, C. (2014). “Social media and self-evaluating assemblages: On numbers, orderings and values”.
Distinktion: Scandinavian Journal of Social Theory. 15(2): 174-188.
https://doi.org/10.1080/1600910X.2014.920267.
Gillespie, T. (2014). “The relevance of algorithms”. Edited by Gillespie T.; Boczkowski P.J. & Foot, K.A. Media Technologies: Essays on Communication, Materiality, and Society: 167-194. MIT Press.
---------------. (2010). “The politics of ‘platforms’”.
New Media & Society. 12(3): 347-364.
https://doi.org/10.1177/1461444809342738.
Gitelman, L. (2011).
Notes for the Upcoming Collection ‘Raw Data’ is an Oxymoron. retrieved at 10 October 2023 [Online] from:
https://files.nyu.edu/lg91/public/.
Granovetter, M.S. (1973). “The strength of weak ties”.
American Journal of Sociology. 78(6): 1360-1380.
https://www.jstor.org/stable/2776392.
Greene, W.H. (2003). Econometric Analysis. 8th ed. Pearson Education India.
Hansen, B. (2022). Econometrics. Princeton University Press.
Haraway, D. (2011). “A cyborg manifesto (1985)”. Cultural Theory: An Anthology. Edited by Szeman I.; Kaposy, T.: 454-471. WILEY Blackwell.
Harman, G. (2018). Object-Oriented Ontology: A New Theory Of Everything. Penguin UK.
Hastie, T.; Tibshirani, R.; Friedman, J.H. & Friedman, J.H. (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Vol. 2: 1-758. New York: Springer.
Hayles, N.K. (2000). “How we became posthuman: Virtual bodies in cybernetics, literature, and informatics”. Chicago: Chicago University Press.
He, H. & Garcia, E.A. (2009). “Learning from imbalanced data”.
IEEE Transactions on Knowledge and Data Engineering. 21(9): 1263-1284. doi:
10.1109/TKDE.2008.239.
Heckman, J. (2013). “Sample selection bias as a specification error”.
Applied Econometrics. 31(3): 129-137.
https://doi.org/10.1007/s11747-021-00816-9.
Hellberg, L. (2024). Reduce the Gender Gap in Computer Science Education Using Creative Programming. Master’s Programme, Interactive Media Technology. KTH/Skolan för elektroteknik och datavetenskap (EECS).
Ioffe, S. & Szegedy, C. (2015). “Batch normalization: Accelerating deep network training by reducing internal covariate shift”. International Conference on Machine Learning. Pmlr: 448-456.
Jablonka, E. & Bergsten, C. (2021). “Numbers don’t speak for themselves: Strategies of using numbers in public policy discourse”.
Educational Studies in Mathematics. 108(3): 579-596.
https://doi.org/10.1007/s10649-021-10059-8.
Jain, A.K.; Murty, M.N. & Flynn, P.J. (1999). “Data clustering: A review”.
ACM Computing Surveys (CSUR). 31(3): 264-323.
https://doi.org/10.1145/331499.331504.
Johnstone, I.M. (2001). “On the distribution of the largest eigenvalue in principal components analysis”.
The Annals of Statistics. 29(2): 295-327. doi:
10.1214/aos/1009210544.
Johnstone, I.M. & Lu, A.Y. (2009). “On consistency and sparsity for principal components analysis in high dimensions”.
Journal of the American Statistical Association. 104(486): 682-693.
https://doi.org/10.1198/jasa.2009.0121.
Kandel, E.R.; Schwartz, J.H. & Jessell, T.M. (2013). Principles of Neural Science. 5th ed. McGraw-Hill Education.
Kant, I. (1781[1908]). Critique of Pure Reason. Modern Classical Philosophers. Cambridge, MA: Houghton Mifflin.
Kitchin, R. (2014). The Data Revolution: Big Data, Open Data, Data Infrastructures and their Consequences. Sage.
Koller, D. & Friedman, N. (2009). Probabilistic Graphical Models: Principles and Techniques. MIT press.
Kuhn, T.S. (1962). The Structure of Scientific Revolutions. University of Chicago Press.
Latour, B. (2007). Reassembling the Social: An Introduction to Actor-Network-Theory. Oup Oxford.
Leinweber, D. J. (2007). Stupid data miner tricks: overfitting the S&P 500.
Journal of Investing,
16(1), 15-22.
https://doi.org/10.3905/joi.2007.681820.
Little, R.J. & Rubin, D.B. (2019). Statistical Analysis with Missing Data. Vol. 793. John Wiley & Sons.
Lohr, S.L. (2021). Sampling: Design and Analysis. Chapman and Hall/CRC.
Manovich, L. (2011). “Trending: The promises and the challenges of big social data”.
Debates in the Digital Humanities. Edited by Gold M.K. The University of Minnesota Press, Minneapolis, MN. Retrieved at 18 September 2023 [Online] from:
http://www.manovich.net/DOCS/Manovich_trending_paper.pdf.
Marchenko, V.A. & Pastur, L.A. (1967). “Distribution of eigenvalues for some sets of random matrices”.
Matematicheskii Sbornik. 114(4): 507-536. doi:
10.1070/SM1967v001n04ABEH001994.
Marres, N. & Weltevrede, E. (2013). “Scraping the social? Issues in live social research”.
Journal of Cultural Economy. 6(3): 313-335.
https://doi.org/10.1080/17530350.2013.772070.
Marx, K. (1932[1845]). “Theses on Feuerbach”. The German Ideology. Edited by Pascal, R. New York: International Publishers. Viktor and Cukier
Mayer-Schönberger, V. & Cukier K. (2013). Big Data: A Revolution that Will Transform How We Live, Work, and Think. New York: Houghton Mifflin Harcourt.
McPherson, M.; Smith-Lovin, L. & Cook, J.M. (2001). “Birds of a feather: Homophily in social networks”.
Annual Review of Sociology. 27(1): 415-444.
https://doi.org/10.1146/annurev.soc.27.1.415.
Meinshausen, N. & Bühlmann, P. (2010). “Stability selection”.
Journal of the Royal Statistical Society Series B: Statistical Methodology. 72(4): 417-473.
https://doi.org/10.1111/j.1467-9868.2010.00740.x.
Meng, X.L. (2018). “Statistical paradises and paradoxes in big data (i) law of large populations, big data paradox, and the 2016 us presidential election”.
The Annals of Applied Statistics. 12(2): 1-14.
https://doi.org/10.1016/j.ijforecast.2024.04.008.
Metcalf, J. & Crawford, K. (2016). “Where are human subjects in big data research? The emerging ethics divide”.
Big Data & Society. 3(1): 2053951716650211.
https://doi.org/10.1177/2053951716650211.
Mohseni Ahooei, E. (2023). “The end of information age society 5.0 and the L [e] ast man”.
Journal of Cyberspace Studies. 7(1), 45-66. doi:
10.22059/JCSS.2022.346205.1078.
---------------. (2022). “Shifting from individualism to genericism: Personalization as a conspiracy theory.
Žurnalistikos Tyrimai. 16: 14-38.
https://doi.org/10.15388/ZT/JR.2022.1.
Moran, D. (2002). Introduction to Phenomenology. Routledge.
Narayanan, A. & Shmatikov, V. (2008). “Robust de-anonymization of large sparse datasets”. 2008 IEEE Symposium on Security and Privacy: 111-125. IEEE.
National Commission for the Protection of Human Subjects of Biomedical and Behavioral Research. (1979). “The belmont report: Ethical principles and guidelines for the protection of human subjects of research”. Retrieved at 21 September 2023 [Online] from:
https://www.hhs.gov/ohrp/sites/default/files/the-belmont-report-508c_FINAL.pdf.
Nissenbaum, H. (2011). “Privacy in context: Technology, policy, and the integrity of social life”.
Journal of Information Policy. 1: 149-151.
https://doi.org/10.1145/3547299.
O’Neil, C. (2016). Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy. Crown Publishing Group.
Pearl, J. (2009). Causality. Cambridge University Press.
Pessach, D. & Shmueli, E. (2022). “A review on fairness in machine learning”.
ACM Computing Surveys (CSUR). 55(3): 1-44.
https://doi.org/10.1145/3494672.
Pietsch, W. (2021). Big Data. Cambridge University Press.
Pond, P. (2020). Complexity, digital media and post truth politics: a theory of interactive systems. Springer Nature.
Porter, T.M. (2020). The Rise of Statistical Thinking, 1820–1900. Princeton University Press.
Raudenbush, S.W. & Bryk, A.S. (2002). Hierarchical Linear Models: Applications and Data Analysis Methods. Vol. 1. Sage.
Resnyansky, L. (2019). “Conceptual frameworks for social and cultural Big Data analytics: Answering the epistemological challenge”.
Big Data & Society.
6(1): 1-12.
https://doi.org/10.1177/2053951718823815.
Schwarz, G. (1978). “Estimating the dimension of a model”.
The Annals of Statistics. 6(2): 461-464.
https://doi.org/10.1214/aos/1176344136.
Shor, P.W. (1994). “Algorithms for quantum computation: Discrete logarithms and factoring”.
Proceedings of the 35th Annual Symposium on Foundations of Computer Science: 124-134. IEEE.
https://doi.org/10.1109/SFCS.1994.365700.
Smith, K.E. (2010). Meaning, Subjectivity, Society: Making Sense of Modernity. Leiden and Boston: Brill.
Sporns, O.; Bullmore, E. & Kaiser, M. (2008). “The human connectome: A structural description of the human brain”.
PLoS Biology. 6(7): 0245-0251. doi:
10.1371/journal.pcbi.0010042.
Srivastava, N.; Hinton, G.; Krizhevsky, A.; Sutskever, I. & Salakhutdinov, R. (2014). “Dropout: A simple way to prevent neural networks from overfitting”.
The Journal of Machine Learning Research. 15(1): 1929-1958. doi:
10.5555/2627435.2670313.
Taylor, C. (1986). Self-Interpreting Animals. In Martin Heidegger. Edited by Mulhall S. London: Routledge.
Tenenbaum, J.B.; Silva, V.D. & Langford, J.C. (2000). “A global geometric framework for nonlinear dimensionality reduction”.
Science. 290(5500): 2319-2323. doi:
10.1126/science.290.5500.2319.
Tibshirani, R. (1996). “Regression shrinkage and selection via the lasso”.
Journal of the Royal Statistical Society Series B: Statistical Methodology. 58(1): 267-288.
https://www.jstor.org/stable/2346178.
Turkle, S. (2011). Alone Together: Why We Expect More from Technology and Less from Each Other. Basic Books.
Tversky, A. & Kahneman, D. (1974). “Judgment under uncertainty: Heuristics and biases”.
Science. 185(4157): 1124-1131. doi:
10.1126/science.185.4157.1124.
Van Dijck, J. (2013). The Clture of Connectivity: A Critical History of Social Media. Oxford University Press.
---------------. (2012). “Tracing Twitter: The rise of a microblogging platform”.
International Journal of Media and Cultural Politics. 7: 333–348.
https://doi.org/10.1386/macp.7.3.333_1.
Van Es, K. & Schäfer, M.T. (2017). The Datafied Society. Studying Culture through Data. Amsterdam University Press.
Vapnik, V. (2013). The Nature of Statistical Learning Theory. Springer Science & Business Media.
Wasserman, S. & Faust, K. (1994). Social network analysis: Methods and applications. Cambridge University Press.
Wellman, B. & Wortley, S. (1990). “Different strokes from different folks: Community ties and social support”.
American Journal of Sociology. 96(3): 558-588.
https://doi.org/10.1086/229572.
Yeo, G. (2021). Record-Making and Record-Keeping in Early Societies. Routledge.
Ziegel, E.R. (2002). “Statistical inference”.
Technometrics. 44(4): 407–408.
https://doi.org/10.1198/tech.2002.s94.