ANOMALOUS ACTIVITY DETECTION FROM DAILY SOCIAL MEDIA USER MOBILITY DATA

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dc.contributor.authorDokuz, Ahmet
dc.date.accessioned2024-11-07T13:20:00Z
dc.date.available2024-11-07T13:20:00Z
dc.date.issued2019
dc.departmentNiğde Ömer Halisdemir Üniversitesi
dc.description.abstractAnomalous activities are the activities that do not fit into normal and routine behavior of people or objects.Anomalous activity, account, or sharing detection from social networks play an important role for preventingsocial media users from harmful and annoying contents. However, detecting anomalous activities is challengingdue to the difficulty of separating anomalous activities from real ones, limitations of current algorithms andinterest measures, the challenge of analyzing social media big data, and hardness of handling spatial andtemporal dimensions. In this study, anomalous activities are detected using daily social media user mobility data.In particular, two features are extracted from daily social media user mobility, namely, daily total number ofvisited locations and daily total distance, and these features are used for detecting anomalous activities. Analgorithm, that employs DBSCAN clustering algorithm, is proposed for detecting such activities. The resultsshow that proposed algorithm could learn normal daily activities of social media users and detect anomalousactivities.
dc.identifier.endpage651
dc.identifier.issn2564-6605
dc.identifier.issue2
dc.identifier.startpage638
dc.identifier.trdizinid354000
dc.identifier.urihttps://search.trdizin.gov.tr/tr/yayin/detay/354000
dc.identifier.urihttps://hdl.handle.net/11480/13538
dc.identifier.volume8
dc.indekslendigikaynakTR-Dizin
dc.language.isoen
dc.relation.ispartofNiğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi
dc.relation.publicationcategoryMakale - Ulusal Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_20241107
dc.subjectBilgisayar Bilimleri
dc.subjectYazılım Mühendisliği
dc.titleANOMALOUS ACTIVITY DETECTION FROM DAILY SOCIAL MEDIA USER MOBILITY DATA
dc.typeArticle

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