globe
14 Strings | R for Data Science (no date). Available at: https://r4ds.had.co.nz/strings.html.
Almehmadi, A., Joudaki, Z. and Jalali, R. (2017) ‘Language usage on Twitter predicts crime rates’, in Proceedings of the 10th International Conference on Security of Information and Networks  - SIN ’17. ACM Press, pp. 307–310. doi: 10.1145/3136825.3136854.
An Introduction to Machine Learning with R (no date). Available at: https://lgatto.github.io/IntroMachineLearningWithR/unsupervised-learning.html.
Burnap, P. and Williams, M. L. (2016) ‘Us and them: identifying cyber hate on Twitter across multiple protected characteristics’, EPJ Data Science, 5(1). doi: 10.1140/epjds/s13688-016-0072-6.
Chen, X., Cho, Y. and Jang, S. Y. (2015) ‘Crime prediction using Twitter sentiment and weather’, in 2015 Systems and Information Engineering Design Symposium. IEEE, pp. 63–68. doi: 10.1109/SIEDS.2015.7117012.
Coveney, P. V., Dougherty, E. R. and Highfield, R. R. (2016) ‘Big data need big theory too’, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 374(2080). doi: 10.1098/rsta.2016.0153.
ElSherief, Mai (2018) ‘Hate Lingo: A Target-based Linguistic Analysis of Hate Speech in Social Media.’ Available at: https://arxiv.org/abs/1804.04257.
Example: textual data visualization • quanteda (no date). Available at: https://quanteda.io/articles/pkgdown/examples/plotting.html.
Founta, Antigoni-Maria (no date) ‘Large Scale Crowdsourcing and Characterization of Twitter Abusive Behavior.’ Available at: https://ucl-new-primo.hosted.exlibrisgroup.com/primo-explore/fulldisplay?docid=TN_arxiv1802.00393&context=PC&vid=UCL_VU2&lang=en_US&search_scope=CSCOP_UCL&adaptor=primo_central_multiple_fe&tab=local&query=any,contains,Founta,%20A.-M.,%20Djouvas,%20C.,%20Chatzakou,%20D.,%20Leontiadis,%20I.,%20Blackburn,%20J.,%20Stringhini,%20G.,%20%E2%80%A6%20Kourtellis,%20N.%20(2018).%20Large%20Scale%20Crowdsourcing%20and%20Characterization%20of%20Twitter%20Abusive%20Behavior.%20ArXiv:1802.00393%20%5BCs%5D.%20Retrieved%20from%20%5Bhttp:%2F%2Farxiv.org%2Fabs%2F1802.00393%5D(http:%2F%2Farxiv.org%2Fabs%2F1802.00393)&sortby=rank.
Hadley Wickham (no date) ‘Easily Harvest (Scrape) Web Pages [R package rvest version 0.3.2].’ Available at: https://cran.r-project.org/web/packages/rvest/index.html.
Hastie, T., Tibshirani, R. and Friedman, J. H. (2009) The elements of statistical learning: data mining, inference, and prediction. Second editon. New York: Springer Verlag. Available at: http://ucl.alma.exlibrisgroup.com/view/action/uresolver.do?operation=resolveService&package_service_id=4193318630004761&institutionId=4761&customerId=4760.
HTML basics | MDN (no date). Available at: https://developer.mozilla.org/en-US/docs/Learn/Getting_started_with_the_web/HTML_basics.
Kadar, C. and Pletikosa, I. (2018) ‘Mining large-scale human mobility data for long-term crime prediction’, EPJ Data Science, 7(1). doi: 10.1140/epjds/s13688-018-0150-z.
Kleinberg, Bennett (no date) ‘Identifying the sentiment styles of YouTube’s vloggers.’ Available at: https://ucl-new-primo.hosted.exlibrisgroup.com/primo-explore/fulldisplay?docid=TN_arxiv1808.09722&context=PC&vid=UCL_VU2&lang=en_US&search_scope=CSCOP_UCL&adaptor=primo_central_multiple_fe&tab=local&query=any,contains,Kleinberg,%20B.,%20Mozes,%20M.,%20&%20Van%20der%20Vegt,%20I.%20(2018).%20Identifying%20the%20sentiment%20styles%20of%20YouTube%E2%80%99s%20vloggers.%20Proceedings%20of%20the%202018%20Conference%20on%20Empirical%20Methods%20in%20Natural%20Language%20Processing,%203581%E2%80%933590.%20Retrieved%20from%20%5Bhttp:%2F%2Faclweb.org%2Fanthology%2FD18-1394%5D(http:%2F%2Faclweb.org%2Fanthology%2FD18-1394)&offset=0.
Kostakos, P. (no date) ‘Public perceptions on organised crime, Mafia, and Terrorism: A big data analysis based on Twitter and Google Trends’, International Journal of Cyber Criminology, 12(1), pp. 282–299. doi: 10.5281/zenodo.1467919.
Kuhn, M. and Johnson, K. (2013) Applied predictive modeling. New York, NY: Springer. Available at: http://UCL.eblib.com/patron/FullRecord.aspx?p=1317001.
Learn To Create Your Own Datasets — Web Scraping in R (no date). Available at: https://towardsdatascience.com/learn-to-create-your-own-datasets-web-scraping-in-r-f934a31748a5.
Miller, A. M. (2017) ‘Review of R for Data Science: Import, Tidy, Transform, Visualize, and Model Data by Hadley Wickham and Garrett Grolemund’, ACM SIGACT News, 48(3), pp. 14–19. doi: 10.1145/3138860.3138865.
Pfeffer, J., Mayer, K. and Morstatter, F. (2018) ‘Tampering with Twitter’s Sample API’, EPJ Data Science, 7(1). doi: 10.1140/epjds/s13688-018-0178-0.
Pérez-Rosas, Verónica (no date) ‘Automatic Detection of Fake News.’ Available at: https://ucl-new-primo.hosted.exlibrisgroup.com/primo-explore/fulldisplay?docid=TN_arxiv1708.07104&context=PC&vid=UCL_VU2&lang=en_US&search_scope=CSCOP_UCL&adaptor=primo_central_multiple_fe&tab=local&query=any,contains,P%C3%A9rez-Rosas,%20V.,%20Kleinberg,%20B.,%20Lefevre,%20A.,%20&%20Mihalcea,%20R.%20(2018).%20Automatic%20Detection%20of%20Fake%20News.%20In%20Proceedings%20of%20the%2027th%20International%20Conference%20on%20Computational%20Linguistics%20(pp.%203391%E2%80%933401).%20Santa%20Fe,%20New%20Mexico,%20USA:%20Association%20for%20Computational%20Linguistics.%20Retrieved%20from%20%5Bhttp:%2F%2Faclweb.org%2Fanthology%2FC18-1287%5D(http:%2F%2Faclweb.org%2Fanthology%2FC18-1287)&sortby=rank.
Quijano-Sánchez, L. et al. (2018) ‘Applying automatic text-based detection of deceptive language to police reports: Extracting behavioral patterns from a multi-step classification model to understand how we lie to the police’, Knowledge-Based Systems, 149, pp. 155–168. doi: 10.1016/j.knosys.2018.03.010.
R Web Scraping Tutorial with rvest (article) - DataCamp (no date). Available at: https://www.datacamp.com/community/tutorials/r-web-scraping-rvest.
R: Unsupervised Learning | DataCamp (no date). Available at: https://www.datacamp.com/courses/unsupervised-learning-in-r.
Replication of Chapter 5 of <em>Quantitative Social Science: An Introduction</em> • quanteda (no date). Available at: https://quanteda.io/articles/pkgdown/replication/qss.html.
Ristea, A., Langford, C. and Leitner, M. (2017) ‘Relationships between crime and Twitter activity around stadiums’, in 2017 25th International Conference on Geoinformatics. IEEE, pp. 1–5. doi: 10.1109/GEOINFORMATICS.2017.8090933.
Solymosi, R., Bowers, K. J. and Fujiyama, T. (2018) ‘Crowdsourcing Subjective Perceptions of Neighbourhood Disorder: Interpreting Bias in Open Data’, The British Journal of Criminology, 58(4), pp. 944–967. doi: 10.1093/bjc/azx048.
Wang, M. and Gerber, M. S. (2015) ‘Using Twitter for Next-Place Prediction, with an Application to Crime Prediction’, in 2015 IEEE Symposium Series on Computational Intelligence. IEEE, pp. 941–948. doi: 10.1109/SSCI.2015.138.
Web scraping tutorial in R – Towards Data Science (no date). Available at: https://towardsdatascience.com/web-scraping-tutorial-in-r-5e71fd107f32.
Williams, M. L., Burnap, P. and Sloan, L. (2016) ‘Crime Sensing with Big Data: The Affordances and Limitations of using Open Source Communications to Estimate Crime Patterns’, British Journal of Criminology. doi: 10.1093/bjc/azw031.