Social velocity based spatio-temporal anomalous daily activity discovery of social media users

Basit Öğe Kaydı
dc.authoridDokuz, Ahmet Sakir/0000-0002-1775-0954
dc.contributor.authorDokuz, Ahmet Sakir
dc.date.accessioned2024-11-07T13:25:04Z
dc.date.available2024-11-07T13:25:04Z
dc.date.issued2022
dc.departmentNiğde Ömer Halisdemir Üniversitesi
dc.description.abstractAnomalous daily activities are the activities that do not fit into normal daily behavior of social media users. Discovering anomalous daily activities is important for protecting social media users from harmful content and providing correct information about populated accounts, products, or hashtags. However, discovering anomalous daily activities is challenging due to hardness of detection of bot applications, complexity of anomalous activities, and the big data nature of social media datasets. In this study, a novel method that discovers anomalous daily activities with respect to spatio-temporal information of social media datasets is proposed. For this purpose, an interest measure, named as social velocity, is proposed to discover anomalous daily activities that is based on spatial distance and temporal difference of successive posts. Two novel algorithms are proposed that use proposed method and interest measure and experimentally evaluated on a real Twitter dataset. The experimental results show that proposed algorithms are successful for discovering anomalous activities of social media users with respect to spatio-temporal information.
dc.identifier.doi10.1007/s10489-021-02535-8
dc.identifier.endpage2762
dc.identifier.issn0924-669X
dc.identifier.issn1573-7497
dc.identifier.issue3
dc.identifier.scopus2-s2.0-85124793250
dc.identifier.scopusqualityQ2
dc.identifier.startpage2745
dc.identifier.urihttps://doi.org/10.1007/s10489-021-02535-8
dc.identifier.urihttps://hdl.handle.net/11480/14468
dc.identifier.volume52
dc.identifier.wosWOS:000664574700001
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherSpringer
dc.relation.ispartofApplied Intelligence
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20241106
dc.subjectSocial media anomaly detection
dc.subjectSpatial social media mining
dc.subjectAnomalous daily activity discovery
dc.subjectSocial velocity
dc.subjectSocial media big data
dc.subjectTwitter
dc.titleSocial velocity based spatio-temporal anomalous daily activity discovery of social media users
dc.typeArticle

Dosyalar

Koleksiyon

WoS İndeksli Yayınlar Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu