Analysis and detection of fake views in online video services

Liang Chen; Yipeng Zhou; Dah Ming Chiu

doi:10.1145/2700290

Analysis and detection of fake views in online video services

Liang Chen, Yipeng Zhou, Dah Ming Chiu

Research output: Contribution to journal › Article › peer-review

18 Citations (Scopus)

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.

Original language	English
Article number	44
Number of pages	20
Journal	ACM Transactions on Multimedia Computing, Communications and Applications
Volume	11
Issue number	2s
DOIs	https://doi.org/10.1145/2700290
Publication status	Published - Feb 2015
Externally published	Yes

Keywords

fake view
online video service
Fake view
Online video service

Access to Document

10.1145/2700290

Link to publication in Scopus

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AB - 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.

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