1.
Miller AM. Review of R for Data Science: Import, Tidy, Transform, Visualize, and Model Data by Hadley Wickham and Garrett Grolemund. ACM SIGACT News. 2017;48(3):14-19. doi:10.1145/3138860.3138865
2.
Williams ML, Burnap P, Sloan L. 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
3.
Chen X, Cho Y, Jang SY. Crime prediction using Twitter sentiment and weather. In: 2015 Systems and Information Engineering Design Symposium. IEEE; 2015:63-68. doi:10.1109/SIEDS.2015.7117012
4.
Wang M, Gerber MS. Using Twitter for Next-Place Prediction, with an Application to Crime Prediction. In: 2015 IEEE Symposium Series on Computational Intelligence. IEEE; 2015:941-948. doi:10.1109/SSCI.2015.138
5.
Ristea A, Langford C, Leitner M. Relationships between crime and Twitter activity around stadiums. In: 2017 25th International Conference on Geoinformatics. IEEE; 2017:1-5. doi:10.1109/GEOINFORMATICS.2017.8090933
6.
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):282-299. doi:10.5281/zenodo.1467919
7.
Almehmadi A, Joudaki Z, Jalali R. Language usage on Twitter predicts crime rates. In: Proceedings of the 10th International Conference on Security of Information and Networks - SIN ’17. ACM Press; 2017:307-310. doi:10.1145/3136825.3136854
8.
Pfeffer J, Mayer K, Morstatter F. Tampering with Twitter’s Sample API. EPJ Data Science. 2018;7(1). doi:10.1140/epjds/s13688-018-0178-0
9.
Solymosi R, Bowers KJ, Fujiyama T. Crowdsourcing Subjective Perceptions of Neighbourhood Disorder: Interpreting Bias in Open Data. The British Journal of Criminology. 2018;58(4):944-967. doi:10.1093/bjc/azx048
10.
Founta, Antigoni-Maria. 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
11.
HTML basics | MDN. https://developer.mozilla.org/en-US/docs/Learn/Getting_started_with_the_web/HTML_basics
12.
R Web Scraping Tutorial with rvest (article) - DataCamp. https://www.datacamp.com/community/tutorials/r-web-scraping-rvest
13.
Web scraping tutorial in R – Towards Data Science. https://towardsdatascience.com/web-scraping-tutorial-in-r-5e71fd107f32
14.
Learn To Create Your Own Datasets — Web Scraping in R. https://towardsdatascience.com/learn-to-create-your-own-datasets-web-scraping-in-r-f934a31748a5
15.
Hadley Wickham. Easily Harvest (Scrape) Web Pages [R package rvest version 0.3.2]. https://cran.r-project.org/web/packages/rvest/index.html
16.
ElSherief, Mai. Hate Lingo: A Target-based Linguistic Analysis of Hate Speech in Social Media. Published online 2018. https://arxiv.org/abs/1804.04257
17.
14 Strings | R for Data Science. https://r4ds.had.co.nz/strings.html
18.
Replication of Chapter 5 of <em>Quantitative Social Science: An Introduction</em> • quanteda. https://quanteda.io/articles/pkgdown/replication/qss.html
19.
Example: textual data visualization • quanteda. https://quanteda.io/articles/pkgdown/examples/plotting.html
20.
Kleinberg, Bennett. 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
21.
Pérez-Rosas, Verónica. 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
22.
Kuhn M, Johnson K. Applied Predictive Modeling. Springer; 2013. http://UCL.eblib.com/patron/FullRecord.aspx?p=1317001
23.
Hastie T, Tibshirani R, Friedman JH. 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
24.
An Introduction to Machine Learning with R. https://lgatto.github.io/IntroMachineLearningWithR/unsupervised-learning.html
25.
R: Unsupervised Learning | DataCamp. https://www.datacamp.com/courses/unsupervised-learning-in-r
26.
Coveney PV, Dougherty ER, Highfield RR. Big data need big theory too. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences. 2016;374(2080). doi:10.1098/rsta.2016.0153
27.
Quijano-Sánchez L, Liberatore F, Camacho-Collados J, Camacho-Collados M. 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. 2018;149:155-168. doi:10.1016/j.knosys.2018.03.010
28.
Kadar C, Pletikosa I. Mining large-scale human mobility data for long-term crime prediction. EPJ Data Science. 2018;7(1). doi:10.1140/epjds/s13688-018-0150-z
29.
Burnap P, Williams ML. Us and them: identifying cyber hate on Twitter across multiple protected characteristics. EPJ Data Science. 2016;5(1). doi:10.1140/epjds/s13688-016-0072-6
