An Empirical Approach for Extreme Behavior Identification through Tweets Using Machine Learning

Sharif, WaqasMumtaz, ShahzadShafiq, ZubairRiaz, OmerAli, TenvirHusnain, MujtabaChoi, Guy San2024-03-112024-03-112019-09-06Sharif, W, Mumtaz, S, Shafiq, Z, Riaz, O, Ali, T, Husnain, M & Choi, G S 2019, 'An Empirical Approach for Extreme Behavior Identification through Tweets Using Machine Learning', Applied Sciences, vol. 9, no. 18, 3723. https://doi.org/10.3390/app91837232076-3417crossref: 10.3390/app9183723ORCID: /0000-0003-2606-2405/work/143115729https://hdl.handle.net/2164/22959This research was supported by the Ministry of Trade, Industry & Energy (MOTIE, Korea) under Industrial Technology Innovation Program. No.10063130, Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2019R1A2C1006159), and MSIT(Ministry of Science and ICT), Korea, under the ITRC(Information Technology Research Center) support program (IITP-2019-2016-0-00313) supervised by the IITP (Institute for Information & communications Technology Promotion), and the 2018 Yeungnam University Research Grant.202421573engSDG 16 - Peace, Justice and Strong Institutionscyber-extremeTwitterexploratory data analysisprincipal component analysisterm frequency—inverse document frequencysupport vector machineensemble classificationQA75 Electronic computers. Computer scienceQA75An Empirical Approach for Extreme Behavior Identification through Tweets Using Machine LearningJournal article10.3390/app9183723https://www.mdpi.com/2076-3417/9/18/3723918