Analysis and detection of fake views in online video services
Author(s)
Author(s)
Chen, L.
Zhou, Y.
Date Issued
2015
Publisher
Association for Computing Machinery
Journal
ACM Transactions on Multimedia Computing, Communications, and Applications
Volume
11
Issue
2s
Abstract
Online video-on-demand (VoD) services invariably maintain a view count for each video they serve, and it has become an important currency for various stakeholders, from viewers, to content owners, advertizers, and the online service providers themselves. There is often significant financial incentive to use a robot (or a botnet) to artificially create fake views. How can we detect fake views? Can we detect them (and stop them) efficiently? What is the extent of fake views with current VoD service providers? These are the questions we study in this article. We develop some algorithms and show that they are quite effective for this problem.
SFU Affiliated Publication
No
Availability at SFU Library
No database links found.