Social velocity based spatio-temporal anomalous daily activity discovery of social media users
Küçük Resim Yok
Tarih
2022
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Springer
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
Anomalous 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.
Açıklama
Anahtar Kelimeler
Social media anomaly detection, Spatial social media mining, Anomalous daily activity discovery, Social velocity, Social media big data, Twitter
Kaynak
Applied Intelligence
WoS Q Değeri
Q2
Scopus Q Değeri
Q2
Cilt
52
Sayı
3
