ANOMALOUS ACTIVITY DETECTION FROM DAILY SOCIAL MEDIA USER MOBILITY DATA

Yıl 2019, , 638 - 651, 31.07.2019

Ahmet Şakir Dokuz

https://doi.org/10.28948/ngumuh.535232

Cited By: 1

Öz

   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 preventing social media users from
harmful and annoying contents. However, detecting anomalous activities is
challenging due to the difficulty of separating anomalous activities from real
ones, limitations of current algorithms and interest measures, the challenge of
analyzing social media big data, and hardness of handling spatial and temporal
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 of visited locations and
daily total distance, and these features are used for detecting anomalous
activities. An algorithm, that employs DBSCAN clustering algorithm, is proposed
for detecting such activities. The results show that proposed algorithm could
learn normal daily activities of social media users and detect anomalous
activities.

Anahtar Kelimeler

anomalous activity detection, social networks, spatial social media mining, DBSCAN algorithm

GÜNLÜK SOSYAL MEDYA KULLANICI HAREKETLİLİK VERİLERİNDEN ANORMAL AKTİVİTELERİN TESPİTİ

Öz

   Anormal aktiviteler, insanlar veya
nesnelerin normal ve rutin davranışlarına uymayan aktiviteleri ifade
etmektedir. Sosyal ağlardan anormal aktivite, hesap veya paylaşımların tespiti,
sosyal medya kullanıcılarını zararlı ve rahatsız edici içeriklerden uzak tutmak
için önem taşımaktadır. Ancak anormal aktivitelerin tespiti, anormal
aktivitelerin gerçek olanlardan ayrılmasının zor olması, mevcut algoritmalar ve
değerlendirme ölçütlerinin yetersiz olması, sosyal medya büyük verisinin
analizinin zorlukları ve mekânsal ve zamansal boyutların ele alınmasının
zorluklarından dolayı zordur. Bu çalışmada günlük sosyal medya kullanıcı
hareketlilik verisi üzerinden anormal aktivitelerin tespiti yapılmıştır.
Ayrıntılı olarak, sosyal medya kullanıcı hareketliliklerinden, günlük toplam
ziyaret edilen lokasyon sayısı ve günlük toplam uzaklık adında iki özellik
çıkarılmış ve bu özellikler anormal aktivitelerin tespitinde kullanılmıştır.
Anormal aktivitelerin tespiti için DBSCAN kümeleme algoritmasını kullanan bir
algoritma önerilmiştir. Elde edilen sonuçlar önerilen algoritmanın sosyal medya
kullanıcılarının normal günlük aktivitelerini öğrenebildiğini ve anormal
aktiviteleri tespit edebildiğini göstermiştir.

Anahtar Kelimeler

anormal aktivite tespiti, sosyal ağlar, mekânsal sosyal medya madenciliği, DBSCAN algoritması

Kaynakça

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