‘14 Strings | R for Data Science’. n.d. https://r4ds.had.co.nz/strings.html.
Almehmadi, Abdulaziz, Zeinab Joudaki, and Roozbeh Jalali. 2017. ‘Language Usage on Twitter Predicts Crime Rates’. In Proceedings of the 10th International Conference on Security of Information and Networks  - SIN ’17, 307–10. ACM Press. https://doi.org/10.1145/3136825.3136854.
‘An Introduction to Machine Learning with R’. n.d. https://lgatto.github.io/IntroMachineLearningWithR/unsupervised-learning.html.
Burnap, Pete, and Matthew L Williams. 2016. ‘Us and Them: Identifying Cyber Hate on Twitter across Multiple Protected Characteristics’. EPJ Data Science 5 (1). https://doi.org/10.1140/epjds/s13688-016-0072-6.
Chen, Xinyu, Youngwoon Cho, and Suk Young Jang. 2015. ‘Crime Prediction Using Twitter Sentiment and Weather’. In 2015 Systems and Information Engineering Design Symposium, 63–68. IEEE. https://doi.org/10.1109/SIEDS.2015.7117012.
Coveney, Peter V., Edward R. Dougherty, and Roger R. Highfield. 2016. ‘Big Data Need Big Theory Too’. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 374 (2080). https://doi.org/10.1098/rsta.2016.0153.
ElSherief, Mai. 2018. ‘Hate Lingo: A Target-Based Linguistic Analysis of Hate Speech in Social Media’. https://arxiv.org/abs/1804.04257.
‘Example: Textual Data Visualization • Quanteda’. n.d. https://quanteda.io/articles/pkgdown/examples/plotting.html.
Founta, Antigoni-Maria. n.d. ‘Large Scale Crowdsourcing and Characterization of Twitter Abusive Behavior’. 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. n.d. ‘Easily Harvest (Scrape) Web Pages [R Package Rvest Version 0.3.2]’. https://cran.r-project.org/web/packages/rvest/index.html.
Hastie, Trevor, Robert Tibshirani, and J. H. Friedman. 2009. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Second editon. New York: Springer Verlag. http://ucl.alma.exlibrisgroup.com/view/action/uresolver.do?operation=resolveService&package_service_id=4193318630004761&institutionId=4761&customerId=4760.
‘HTML Basics | MDN’. n.d. https://developer.mozilla.org/en-US/docs/Learn/Getting_started_with_the_web/HTML_basics.
Kadar, Cristina, and Irena Pletikosa. 2018. ‘Mining Large-Scale Human Mobility Data for Long-Term Crime Prediction’. EPJ Data Science 7 (1). https://doi.org/10.1140/epjds/s13688-018-0150-z.
Kleinberg, Bennett. n.d. ‘Identifying the Sentiment Styles of YouTube’s Vloggers’. 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. n.d. ‘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): 282–99. https://doi.org/10.5281/zenodo.1467919.
Kuhn, Max, and Kjell Johnson. 2013. Applied Predictive Modeling. New York, NY: Springer. http://UCL.eblib.com/patron/FullRecord.aspx?p=1317001.
‘Learn To Create Your Own Datasets — Web Scraping in R’. n.d. https://towardsdatascience.com/learn-to-create-your-own-datasets-web-scraping-in-r-f934a31748a5.
Miller, Allan 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): 14–19. https://doi.org/10.1145/3138860.3138865.
Pérez-Rosas, Verónica. n.d. ‘Automatic Detection of Fake News’. 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.
Pfeffer, Jürgen, Katja Mayer, and Fred Morstatter. 2018. ‘Tampering with Twitter’s Sample API’. EPJ Data Science 7 (1). https://doi.org/10.1140/epjds/s13688-018-0178-0.
Quijano-Sánchez, Lara, Federico Liberatore, José Camacho-Collados, and Miguel Camacho-Collados. 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 (June): 155–68. https://doi.org/10.1016/j.knosys.2018.03.010.
‘R: Unsupervised Learning | DataCamp’. n.d. https://www.datacamp.com/courses/unsupervised-learning-in-r.
‘R Web Scraping Tutorial with Rvest (Article) - DataCamp’. n.d. https://www.datacamp.com/community/tutorials/r-web-scraping-rvest.
‘Replication of Chapter 5 of <em>Quantitative Social Science: An Introduction</Em> • Quanteda’. n.d. https://quanteda.io/articles/pkgdown/replication/qss.html.
Ristea, Alina, Chad Langford, and Michael Leitner. 2017. ‘Relationships between Crime and Twitter Activity around Stadiums’. In 2017 25th International Conference on Geoinformatics, 1–5. IEEE. https://doi.org/10.1109/GEOINFORMATICS.2017.8090933.
Solymosi, Reka, Kate J Bowers, and Taku Fujiyama. 2018. ‘Crowdsourcing Subjective Perceptions of Neighbourhood Disorder: Interpreting Bias in Open Data’. The British Journal of Criminology 58 (4): 944–67. https://doi.org/10.1093/bjc/azx048.
Wang, Mingjun, and Matthew S. Gerber. 2015. ‘Using Twitter for Next-Place Prediction, with an Application to Crime Prediction’. In 2015 IEEE Symposium Series on Computational Intelligence, 941–48. IEEE. https://doi.org/10.1109/SSCI.2015.138.
‘Web Scraping Tutorial in R – Towards Data Science’. n.d. https://towardsdatascience.com/web-scraping-tutorial-in-r-5e71fd107f32.
Williams, Matthew L., Pete Burnap, and Luke Sloan. 2016. ‘Crime Sensing with Big Data: The Affordances and Limitations of Using Open Source Communications to Estimate Crime Patterns’. British Journal of Criminology, March. https://doi.org/10.1093/bjc/azw031.