{"id":2368,"date":"2023-12-31T14:53:23","date_gmt":"2023-12-31T07:53:23","guid":{"rendered":"https:\/\/research.binus.ac.id\/airdc\/?p=2368"},"modified":"2024-08-12T14:54:32","modified_gmt":"2024-08-12T07:54:32","slug":"automatic-smart-crawling-on-twitter-for-weather-information-in-indonesia","status":"publish","type":"post","link":"https:\/\/research.binus.ac.id\/airdc\/2023\/12\/automatic-smart-crawling-on-twitter-for-weather-information-in-indonesia\/","title":{"rendered":"Automatic Smart Crawling on Twitter for Weather Information in Indonesia"},"content":{"rendered":"<p style=\"text-align: justify\">As a popular resource for analyzing social interactions and text data mining, Twitter utilization is facing an automation problem in collecting Twitter users&#8217; geolocation. To surpass this problem, the research proposes Support Vector Machine (SVM) model that can be used to automatically design a smart crawling system on Twitter. Twint, a Python-based Twitter scraping program is utilized to perform data crawling based on keywords related to the weather in Indonesia. Null-geolocations are filled toward using aliases generated based on Indonesians&#8217; behavior of reporting about Indonesia&#8217;s location in Twitter tweets. The accuracy of the outcomes of automated smart crawling using the SVM model is 85%.<\/p>\n<p style=\"text-align: justify\"><strong>Authors:<\/strong><br \/>\nKartika Purwandari, Reza Bayu Perdana, Join W.C. Sigalingging, Reza Rahutomo, and Bens Pardamean<\/p>\n<p style=\"text-align: justify\"><em>8th International Conference on Computer Science and Computational Intelligence, ICCSCI 2023<\/em><\/p>\n<p style=\"text-align: justify\"><a href=\"https:\/\/www.researchgate.net\/publication\/355202381_Automatic_Smart_Crawling_on_Twitter_for_Weather_Information_in_Indonesia\" target=\"_blank\" rel=\"noopener\">Read Full Article<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>As a popular resource for analyzing social interactions and text data mining, Twitter utilization is facing an automation problem in collecting Twitter users&#8217; geolocation. To surpass this problem, the research proposes Support Vector Machine (SVM) model that can be used to automatically design a smart crawling system on Twitter. Twint, a Python-based Twitter scraping program [&hellip;]<\/p>\n","protected":false},"author":14,"featured_media":2369,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[16],"tags":[],"class_list":["post-2368","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-publications"],"_links":{"self":[{"href":"https:\/\/research.binus.ac.id\/airdc\/wp-json\/wp\/v2\/posts\/2368","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/research.binus.ac.id\/airdc\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/research.binus.ac.id\/airdc\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/research.binus.ac.id\/airdc\/wp-json\/wp\/v2\/users\/14"}],"replies":[{"embeddable":true,"href":"https:\/\/research.binus.ac.id\/airdc\/wp-json\/wp\/v2\/comments?post=2368"}],"version-history":[{"count":1,"href":"https:\/\/research.binus.ac.id\/airdc\/wp-json\/wp\/v2\/posts\/2368\/revisions"}],"predecessor-version":[{"id":2370,"href":"https:\/\/research.binus.ac.id\/airdc\/wp-json\/wp\/v2\/posts\/2368\/revisions\/2370"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/research.binus.ac.id\/airdc\/wp-json\/wp\/v2\/media\/2369"}],"wp:attachment":[{"href":"https:\/\/research.binus.ac.id\/airdc\/wp-json\/wp\/v2\/media?parent=2368"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/research.binus.ac.id\/airdc\/wp-json\/wp\/v2\/categories?post=2368"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/research.binus.ac.id\/airdc\/wp-json\/wp\/v2\/tags?post=2368"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}