{"title":"“TwitterSpamDetector”","authors":"A. T. Kabakus, R. Kara","doi":"10.4018/ijkss.2019070101","DOIUrl":null,"url":null,"abstract":"Twitter is the most popular microblogging platform which lets users post status messages called tweets. This popularity and the advanced API provided by Twitter to read and write Twitter data programmatically attracts the attention of spammers as well as legitimate users. Since Twitter has some unique characteristics, the traditional spam detecting methods cannot be directly used to detect spam on Twitter. Therefore, a spam detection framework which is specially designed for Twitter namely TwitterSpamDetector is proposed in this paper. TwitterSpamDetector uses Twitter-specific features to detect spam on Twitter. 77,033 tweets which are posted by 50,490 users collected using the API provided by Twitter. Naive Bayes is used to train TwitterSpamDetector using the selected features of Twitter which clearly classify the spammers from legitimate users. According to the evaluation result, TwitterSpamDetector's accuracy and sensitivity are calculated as 0.943 and 0.913, respectively.","PeriodicalId":43448,"journal":{"name":"International Journal of Knowledge and Systems Science","volume":null,"pages":null},"PeriodicalIF":0.6000,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"10","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Knowledge and Systems Science","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.4018/ijkss.2019070101","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"OPERATIONS RESEARCH & MANAGEMENT SCIENCE","Score":null,"Total":0}
引用次数: 10
Abstract
Twitter is the most popular microblogging platform which lets users post status messages called tweets. This popularity and the advanced API provided by Twitter to read and write Twitter data programmatically attracts the attention of spammers as well as legitimate users. Since Twitter has some unique characteristics, the traditional spam detecting methods cannot be directly used to detect spam on Twitter. Therefore, a spam detection framework which is specially designed for Twitter namely TwitterSpamDetector is proposed in this paper. TwitterSpamDetector uses Twitter-specific features to detect spam on Twitter. 77,033 tweets which are posted by 50,490 users collected using the API provided by Twitter. Naive Bayes is used to train TwitterSpamDetector using the selected features of Twitter which clearly classify the spammers from legitimate users. According to the evaluation result, TwitterSpamDetector's accuracy and sensitivity are calculated as 0.943 and 0.913, respectively.
期刊介绍：
The mission of the International Journal of Knowledge and Systems Science (IJKSS) is to promote the development of knowledge science and systems science as well as the collaboration between the two sciences among academics and professionals from various disciplines around the world. IJKSS establishes knowledge and systems science as a vigorous academic discipline in universities. Targeting academicians, professors, students, practitioners, and field specialists, this journal covers the development of new paradigms in the understanding and modeling of human knowledge process from mathematical, technical, social, psychological, and philosophical frameworks. The International Journal of Knowledge and Systems Science was originally launched by the International Society of Knowledge and Systems Science, which was initiated in 2000 in Japan and founded by Prof. Y. Nakamori, Professor Z. T. Wang and Professor J. Gu in 2003 in Guangzhou. Professor Z. T. Wang was its Founding Editor.
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