‘14 Strings | R for Data Science’, n.d. <https://r4ds.had.co.nz/strings.html>
Almehmadi, Abdulaziz, Zeinab Joudaki, and Roozbeh Jalali, ‘Language Usage on Twitter Predicts Crime Rates’, Proceedings of the 10th International Conference on Security of Information and Networks - SIN ’17, 2017, pp. 307–10, doi: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, ‘Us and Them: Identifying Cyber Hate on Twitter across Multiple Protected Characteristics’, EPJ Data Science, 5.1 (2016), doi:10.1140/epjds/s13688-016-0072-6
Chen, Xinyu, Youngwoon Cho, and Suk Young Jang, ‘Crime Prediction Using Twitter Sentiment and Weather’, 2015 Systems and Information Engineering Design Symposium, 24 April 2015, pp. 63–68, doi:10.1109/SIEDS.2015.7117012
Coveney, Peter V., Edward R. Dougherty, and Roger R. Highfield, ‘Big Data Need Big Theory Too’, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 374.2080 (2016), doi:10.1098/rsta.2016.0153
ElSherief, Mai, Hate Lingo: A Target-Based Linguistic Analysis of Hate Speech in Social Media, 2018 <https://arxiv.org/abs/1804.04257>
‘Example: Textual Data Visualization • Quanteda’, n.d. <https://quanteda.io/articles/pkgdown/examples/plotting.html>
Founta, Antigoni-Maria, Large Scale Crowdsourcing and Characterization of Twitter Abusive Behavior, n.d. <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, Easily Harvest (Scrape) Web Pages [R Package Rvest Version 0.3.2], n.d. <https://cran.r-project.org/web/packages/rvest/index.html>
Hastie, Trevor, Robert Tibshirani, and J. H. Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second editon (Springer Verlag, 2009) <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, ‘Mining Large-Scale Human Mobility Data for Long-Term Crime Prediction’, EPJ Data Science, 7.1 (2018), doi:10.1140/epjds/s13688-018-0150-z
Kleinberg, Bennett, Identifying the Sentiment Styles of YouTube’s Vloggers, n.d. <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., ‘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 (n.d.), pp. 282–99, doi:10.5281/zenodo.1467919
Kuhn, Max, and Kjell Johnson, Applied Predictive Modeling (Springer, 2013) <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., ‘Review of R for Data Science: Import, Tidy, Transform, Visualize, and Model Data by Hadley Wickham and Garrett Grolemund’, ACM SIGACT News, 48.3 (2017), pp. 14–19, doi:10.1145/3138860.3138865
Pérez-Rosas, Verónica, Automatic Detection of Fake News, n.d. <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, ‘Tampering with Twitter’s Sample API’, EPJ Data Science, 7.1 (2018), doi:10.1140/epjds/s13688-018-0178-0
Quijano-Sánchez, Lara, and others, ‘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 (2018), pp. 155–68, doi: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, ‘Relationships between Crime and Twitter Activity around Stadiums’, 2017 25th International Conference on Geoinformatics, August 2017, pp. 1–5, doi:10.1109/GEOINFORMATICS.2017.8090933
Solymosi, Reka, Kate J Bowers, and Taku Fujiyama, ‘Crowdsourcing Subjective Perceptions of Neighbourhood Disorder: Interpreting Bias in Open Data’, The British Journal of Criminology, 58.4 (2018), pp. 944–67, doi:10.1093/bjc/azx048
Wang, Mingjun, and Matthew S. Gerber, ‘Using Twitter for Next-Place Prediction, with an Application to Crime Prediction’, 2015 IEEE Symposium Series on Computational Intelligence, December 2015, pp. 941–48, doi: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, ‘Crime Sensing with Big Data: The Affordances and Limitations of Using Open Source Communications to Estimate Crime Patterns’, British Journal of Criminology, published online 31 March 2016, doi:10.1093/bjc/azw031
