Social data science

Esnaashari, S., Bright, J., Francis, J., Hashem, Y., Straub, V., & Morgan, D. (2023). Approaches to the Algorithmic Allocation of Public Resources: A Cross-disciplinary Review. arXiv preprint arXiv:2310.06475. DOI: https://doi.org/10.48550/arXiv.2310.06475

E. Benetos, A. Ragano, D. Sgroi, and A. Tuckwell, “Measuring National Mood with Music: Using Machine Learning to Construct a Measure of National Valence from Audio Data”, Behavior Research Methods, Feb. 2022. https://doi.org/10.3758/s13428-021-01747-7

Full report Young, E., Wajcman, J. and Sprejer, L. (2021). Where are the Women? Mapping the Gender Job Gap in AI. Policy Briefing: Full Report. The Alan Turing Institute. Summary Young, E., Wajcman, J. and Sprejer, L. (2021). Where are the Women? Mapping the Gender Job Gap in AI. Policy Briefing: Summary. The Alan Turing Institute.

B. Vidgen, A. Harris, D. Nguyen, R. Tromble, S. Hale and H. Margetts. Challenges and frontiers in abusive content detection. To appear at the 3rd Workshop on Abusive Language Online at ACL 2019.

Klemmer, K., Brandt, T. and Jarvis, S., 2018. Isolating the effect of cycling on local business environments in London. PLOS ONE, 13(12), p.e0209090.

Ojanperä, Sanna, O'Clery, Neave, & Graham, Mark. (2018, October 24). Data science, artificial intelligence and the futures of work. Zenodo. http://doi.org/10.5281/zenodo.1475162

Guerrero, Omar A and Matter, Ulrich, Uncovering Vote Trading through Networks and Computation (November 4, 2016). Available at SSRN: https://ssrn.com/abstract=2864421 or http://dx.doi.org/10.2139/ssrn.2864421