Fake view analytics in online video services

Liang Chen; Yipeng Zhou; Dah Ming Chiu

doi:10.1145/2578260.2578263

Fake view analytics in online video services

Liang Chen, Yipeng Zhou, Dah Ming Chiu

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding contribution › peer-review

7 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 the 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 paper. We develop some algorithms and show their effectiveness for this problem.

Original language	English
Title of host publication	NOSSDAV '14
Subtitle of host publication	Proceedings of Network and Operating System Support on Digital Audio and Video Workshop
Publisher	Association for Computing Machinery (ACM)
Pages	1-6
Number of pages	6
ISBN (Electronic)	9781450327060
DOIs	https://doi.org/10.1145/2578260.2578263
Publication status	Published - 2014
Externally published	Yes
Event	24th ACM Workshop on Network and Operating Systems Support for Digital Audio and Video, NOSSDAV 2014 - Singapore, Singapore Duration: 19 Mar 2014 → 20 Mar 2014

Conference

Conference	24th ACM Workshop on Network and Operating Systems Support for Digital Audio and Video, NOSSDAV 2014
Country/Territory	Singapore
City	Singapore
Period	19/03/14 → 20/03/14

Keywords

measurement
experimentation
reliability
VoD service
Fake view

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10.1145/2578260.2578263

Cite this

@inproceedings{0f09022d996d41e895797117f0691da8,

title = "Fake view analytics in online video services",

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 the 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 paper. We develop some algorithms and show their effectiveness for this problem.",

keywords = "measurement, experimentation, reliability, VoD service, Fake view",

author = "Liang Chen and Yipeng Zhou and Chiu, {Dah Ming}",

year = "2014",

doi = "10.1145/2578260.2578263",

language = "English",

pages = "1--6",

booktitle = "NOSSDAV '14",

publisher = "Association for Computing Machinery (ACM)",

address = "United States",

note = "24th ACM Workshop on Network and Operating Systems Support for Digital Audio and Video, NOSSDAV 2014 ; Conference date: 19-03-2014 Through 20-03-2014",

}

Chen, L, Zhou, Y & Chiu, DM 2014, Fake view analytics in online video services. in NOSSDAV '14: Proceedings of Network and Operating System Support on Digital Audio and Video Workshop. Association for Computing Machinery (ACM), pp. 1-6, 24th ACM Workshop on Network and Operating Systems Support for Digital Audio and Video, NOSSDAV 2014, Singapore, Singapore, 19/03/14. https://doi.org/10.1145/2578260.2578263

TY - GEN

T1 - Fake view analytics in online video services

AU - Chen, Liang

AU - Zhou, Yipeng

AU - Chiu, Dah Ming

PY - 2014

Y1 - 2014

N2 - 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 the 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 paper. We develop some algorithms and show their effectiveness for this problem.

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 the 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 paper. We develop some algorithms and show their effectiveness for this problem.

KW - measurement

KW - experimentation

KW - reliability

KW - VoD service

KW - Fake view

UR - http://www.scopus.com/inward/record.url?scp=84898934521&partnerID=8YFLogxK

U2 - 10.1145/2578260.2578263

DO - 10.1145/2578260.2578263

M3 - Conference proceeding contribution

SP - 1

EP - 6

BT - NOSSDAV '14

PB - Association for Computing Machinery (ACM)

T2 - 24th ACM Workshop on Network and Operating Systems Support for Digital Audio and Video, NOSSDAV 2014

Y2 - 19 March 2014 through 20 March 2014

ER -

Fake view analytics in online video services

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Keywords

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