• Uncover the mechanisms and implications of digitization on human behaviour
• Explore the potential and boundaries for positive behavioural change
• Understand the underlying theory of machine learning and how it works in practice
• Be able to interpret, apply and deploy machine learning models in R for a given task related to digital (consumer) behaviour
• Understand the risks and benefits associated with machine learning in practice

The course deals with questions about digital behaviour in general and its relation to machine learning in particular. Students will learn how the digitalized world influences the behaviour of individuals and society and - beyond that - what risks and benefits are associated with it. To this end, the course will follow a straightforward structure in two blocks:

In the first block we will explore human behaviour along major digital domains of interest, covering mainstream areas like social media and networks but also touch on more advanced phenomena in human computer interaction, virtual environments, characters and gaming. We will then move into how these insights translate into insights of relevance to large application areas of societal importance, like health and sustainability.

In the second block, students will build on the foundational nature of the earlier sessions and delve deeper on how to approach practically these broader domains with the use of applied machine learning. This block features exercise / lab sessions where students hear and learn about machine learning models typically used in this field (for instance, how insurance companies use algorithms to predict the risk of customers). For each topic, students work together in groups to conduct a specific case study aimed at replicating a typical machine learning algorithm.

Typical topics which students can expect to deal with during the lectures are (subject to modifications):

• Consumer Behaviour and digital sociality
• Proteus, bad robots and human behaviour
• Health and digital behaviour; e-trust, e-literacy and the uneasy affairs
• Sustainability, prosumption and the role of digitization
• Technology acceptance and the machine (that learns)

Typical topics which students can expect to deal with during the lab-sessions are (subject to modifications):

• Neural networks (possible application: face recognition)
• Logistic regression (possible application: calculation of credit status)
• Naïve Bayes classifiers (possible application: spam filters)
• KNN (possible application: movie rating)
• K-Means algorithms (possible application: customer grouping)
• EM algorithms (possible application: identify different buying habits)
• Topic models (possible application: sentiment analysis of tweets)

The course aims to bridge the gap between theoretical models of behaviour in the digital age and the practical application that is associated with it. 

Offered for the first time in Autumn 2020

Literature on digital behaviour:

Gerson, J., Plagnol, A. C., & Corr, P. J. (2016). Subjective well-being and social media use: Do personality traits moderate the impact of social comparison on Facebook? Computers in Human Behavior, 63, 813–822. https:/​/​doi.org/​10.1016/​j.chb.2016.06.023

Lin, C. A., & Kim, T. (2016). Predicting user response to sponsored advertising on social media via the technology acceptance model. Computers in Human Behavior, 64, 710–718. https:/​/​doi.org/​10.1016/​j.chb.2016.07.027

Lyons, E. J., Tate, D. F., Ward, D. S., Ribisl, K. M., Bowling, J. M., & Kalyanaraman, S. (2014). Engagement, enjoyment, and energy expenditure during active video game play. Health Psychology : Official Journal of the Division of Health Psychology, American Psychological Association, 33(2), 174–181. https:/​/​doi.org/​10.1037/​a0031947

Tay, B., Jung, Y., & Park, T. (2014). When stereotypes meet robots: The double-edge sword of robot gender and personality in human–robot interaction. Computers in Human Behavior, 38, 75–84. https:/​/​doi.org/​10.1016/​j.chb.2014.05.014

Tinwell, A., Grimshaw, M., Nabi, D. A., & Williams, A. (2011). Facial expression of emotion and perception of the Uncanny Valley in virtual characters. Computers in Human Behavior, 27(2), 741–749. https:/​/​doi.org/​10.1016/​j.chb.2010.10.018

Wilcox, K., & Stephen, A. T. (2013). Are Close Friends the Enemy? Online Social Networks, Self-Esteem, and Self-Control. Journal of Consumer Research, 40(1), 90–103. https:/​/​doi.org/​10.1086/​668794

Yee, N., & Bailenson, J. (2007). The Proteus Effect: The Effect of Transformed Self-Representation on Behavior. Human Communication Research, 33(3), 271–290. https:/​/​doi.org/​10.1111/​j.1468-2958.2007.00299.x

Literature on machine learning and statistics:

Bertsekas, D., & Tsitsiklis, J. (2008). Introduction to probability, ser. Athena Scientific optimization and computation series. Athena Scientific.

Hastie, T., Tibshirani, R., & Friedman, J. (2009). The elements of statistical learning: data mining, inference, and prediction. Springer Science & Business Media. https:/​/​web.stanford.edu/​~hastie/​ElemStatLearn/​

Irizarry, R. A. (2019). Introduction to Data Science: Data Analysis and Prediction Algorithms with R. CRC Press. https:/​/​rafalab.github.io/​dsbook/​

James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An introduction to statistical learning (Vol. 112, pp. 3-7). New York: springer. http:/​/​faculty.marshall.usc.edu/​gareth-james/​ISL/​

Wasserman, L. (2013). All of statistics: a concise course in statistical inference. Springer Science & Business Media. https:/​/​www.ic.unicamp.br/​~wainer/​cursos/​1s2013/​ml/​livro.pdf