VOLTIME: Unsupervised anomaly detection on users' online activity volume

Abstract

Is it possible to spot review frauds and spamming on social media and online stores? In this paper we analyze the joint distribution of the inter-arrival times and volume of events such as comments and online reviews and show that it is possible to accurately rank and detect suspicious users such as spammers, bots and fraudsters. We propose VOLTIME, a generative model that fits well the inter-arrival time distribution (IAT) of real users. Thus, VOLTIME automatically spots and ranks suspicious users. Experiments on several real datasets, ranging from Reddit comments and phone calls to Flipkart product reviews, show that VOLTIME is able to accurately fit the activity volume and IAT of real data. Additionally, we show that VOLTIME ranks suspicious users with a precision higher than 90% for a sensitivity of 70%.