ANOMALOUS ACTIVITY DETECTION FROM DAILY SOCIAL MEDIA USER MOBILITY DATA
Küçük Resim Yok
Tarih
2019
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
Anomalous 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.
Açıklama
Anahtar Kelimeler
Bilgisayar Bilimleri, Yazılım Mühendisliği
Kaynak
Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi
WoS Q Değeri
Scopus Q Değeri
Cilt
8
Sayı
2
