{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T18:44:23Z","timestamp":1772822663255,"version":"3.50.1"},"reference-count":90,"publisher":"SAGE Publications","issue":"6","license":[{"start":{"date-parts":[[2022,9,15]],"date-time":"2022-09-15T00:00:00Z","timestamp":1663200000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Journal of Information Science"],"published-print":{"date-parts":[[2024,12]]},"abstract":"<jats:p> The communication revolution has perpetually reshaped the means through which people send and receive information. Social media is an important pillar of this revolution and has brought profound changes to various aspects of our lives. However, the open environment and popularity of these platforms inaugurate windows of opportunities for various cyber threats, thus social networks have become a fertile venue for spammers and other illegitimate users to execute their malicious activities. These activities include phishing hot and trendy topics and posting a wide range of contents in many topics. Hence, it is crucial to continuously introduce new techniques and approaches to detect and stop this category of users. This article proposes a novel and effective approach to detect social spammers. An investigation into several attributes to measure topic-dependent and topic-independent users\u2019 behaviours on Twitter is carried out. The experiments of this study are undertaken on various machine learning classifiers. The performance of these classifiers is compared and their effectiveness is measured via a number of robust evaluation measures. Furthermore, the proposed approach is benchmarked against state-of-the-art social spam and anomalous detection techniques. These experiments report the effectiveness and utility of the proposed approach and embedded modules. <\/jats:p>","DOI":"10.1177\/01655515221124062","type":"journal-article","created":{"date-parts":[[2022,9,15]],"date-time":"2022-09-15T07:25:56Z","timestamp":1663226756000},"page":"1471-1498","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":13,"title":["An intelligent system for multi-topic social spam detection in microblogging"],"prefix":"10.1177","volume":"50","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9875-4369","authenticated-orcid":false,"given":"Bilal","family":"Abu-Salih","sequence":"first","affiliation":[{"name":"The University of Jordan, Jordan"}]},{"given":"Dana Al","family":"Qudah","sequence":"additional","affiliation":[{"name":"The University of Jordan, Jordan"}]},{"given":"Malak","family":"Al-Hassan","sequence":"additional","affiliation":[{"name":"The University of Jordan, Jordan"}]},{"given":"Seyed Mohssen","family":"Ghafari","sequence":"additional","affiliation":[{"name":"Macquarie University, Australia"}]},{"given":"Tomayess","family":"Issa","sequence":"additional","affiliation":[{"name":"Curtin University, Australia"}]},{"given":"Ibrahim","family":"Aljarah","sequence":"additional","affiliation":[{"name":"The University of Jordan, Jordan"}]},{"given":"Amin","family":"Beheshti","sequence":"additional","affiliation":[{"name":"Macquarie University, Australia"}]},{"given":"Sulaiman","family":"Alqahtani","sequence":"additional","affiliation":[{"name":"Macquarie University, Australia"}]}],"member":"179","published-online":{"date-parts":[[2022,9,15]]},"reference":[{"key":"bibr1-01655515221124062","volume-title":"Proceeding of the 33rd international ACM SIGIR conference on research and development in information retrieval \u2013 SIGIR \u201910","author":"Lee K"},{"key":"bibr2-01655515221124062","volume-title":"Fifth international AAAI conference on weblogs and social media","author":"Lee K"},{"key":"bibr3-01655515221124062","unstructured":"Zantal-Wiener A. 47% of social media users report seeing more spam in their feeds, even as networks fight to stop it. 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