poster

Inferring audience partisanship for YouTube videos

Authors:

Venkata Rama Kiran Garimella,

Erik BorraAuthors Info & Claims

WWW '13 Companion: Proceedings of the 22nd International Conference on World Wide Web

Pages 43 - 44

https://doi.org/10.1145/2487788.2487803

Published: 13 May 2013 Publication History

Abstract

Political campaigning and the corresponding advertisement money are increasingly moving online. Some analysts claim that the U.S.~elections were partly won through a smart use of (i) targeted advertising and (ii) social media. But what type of information do politicized users consume online? And, the other way around, for a given content, e.g. a YouTube video, is it possible to predict its political audience? To address this latter question, we present a large scale study of anonymous YouTube video consumption of politicized users, where political orientation is derived from visits to "beacon pages", namely, political partisan blogs. Though our techniques are relevant for targeted political advertising, we believe that our findings are also of a wider interest.

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Cited By

Nielsen SSkov MHansen KKaszowska A(2022)Using User-Generated YouTube Videos to Understand Unguided Interactions with Robots in Public PlacesACM Transactions on Human-Robot Interaction10.1145/355028012:1(1-40)Online publication date: 1-Aug-2022
https://dl.acm.org/doi/10.1145/3550280
Stankova MPraet SMartens DProvost F(2021)Node classification over bipartite graphs through projectionMachine Language10.1007/s10994-020-05898-0110:1(37-87)Online publication date: 1-Jan-2021
https://dl.acm.org/doi/10.1007/s10994-020-05898-0
Komkaite ALavrinovica LVraka MSkov MBrewster SFitzpatrick GCox AKostakos V(2019)Underneath the SkinProceedings of the 2019 CHI Conference on Human Factors in Computing Systems10.1145/3290605.3300444(1-12)Online publication date: 2-May-2019
https://dl.acm.org/doi/10.1145/3290605.3300444
Show More Cited By

Index Terms

Inferring audience partisanship for YouTube videos
1. Applied computing
  1. Law, social and behavioral sciences
    1. Sociology
2. Information systems
  1. World Wide Web
    1. Web applications
    2. Web services

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Published In

cover image ACM Other conferences

WWW '13 Companion: Proceedings of the 22nd International Conference on World Wide Web

May 2013

1636 pages

ISBN:9781450320382

DOI:10.1145/2487788

General Chairs:
Daniel Schwabe
PUC-Rio - Brazil
,
Virgílio Almeida
UFMG - Brazil
,
Hartmut Glaser
CGI.br - Brazil
,
Program Chairs:
Ricardo Baeza-Yates
Yahoo! Labs - Spain & Chile
,
Sue Moon
KAIST - South Korea

Copyright © 2013 Copyright is held by the owner/author(s).

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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NICBR: Nucleo de Informatcao e Coordenacao do Ponto BR
CGIBR: Comite Gestor da Internet no Brazil

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SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 13 May 2013

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WWW '13

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NICBR
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WWW '13: 22nd International World Wide Web Conference

May 13 - 17, 2013

Rio de Janeiro, Brazil

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WWW '13 Companion Paper Acceptance Rate 831 of 1,250 submissions, 66%;

Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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Cited By

Nielsen SSkov MHansen KKaszowska A(2022)Using User-Generated YouTube Videos to Understand Unguided Interactions with Robots in Public PlacesACM Transactions on Human-Robot Interaction10.1145/355028012:1(1-40)Online publication date: 1-Aug-2022
https://dl.acm.org/doi/10.1145/3550280
Stankova MPraet SMartens DProvost F(2021)Node classification over bipartite graphs through projectionMachine Language10.1007/s10994-020-05898-0110:1(37-87)Online publication date: 1-Jan-2021
https://dl.acm.org/doi/10.1007/s10994-020-05898-0
Komkaite ALavrinovica LVraka MSkov MBrewster SFitzpatrick GCox AKostakos V(2019)Underneath the SkinProceedings of the 2019 CHI Conference on Human Factors in Computing Systems10.1145/3290605.3300444(1-12)Online publication date: 2-May-2019
https://dl.acm.org/doi/10.1145/3290605.3300444
Tobback EMartens D(2019)Retail credit scoring using fine‐grained payment dataJournal of the Royal Statistical Society: Series A (Statistics in Society)10.1111/rssa.12469182:4(1227-1246)Online publication date: 24-May-2019
https://doi.org/10.1111/rssa.12469
De Cnudde SMoeyersoms JStankova MTobback EJavaly VMartens D(2018)What does your Facebook profile reveal about your creditworthiness? Using alternative data for microfinanceJournal of the Operational Research Society10.1080/01605682.2018.143440270:3(353-363)Online publication date: 25-Apr-2018
https://doi.org/10.1080/01605682.2018.1434402
Hai-Jew S(2017)Exploring “User,” “Video,” and (Pseudo) Multi-Mode Networks on YouTube with NodeXLSocial Media Data Extraction and Content Analysis10.4018/978-1-5225-0648-5.ch009(242-295)Online publication date: 2017
https://doi.org/10.4018/978-1-5225-0648-5.ch009
Tobback EBellotti TMoeyersoms JStankova MMartens D(2017)Bankruptcy prediction for SMEs using relational dataDecision Support Systems10.1016/j.dss.2017.07.004102:C(69-81)Online publication date: 1-Oct-2017
https://dl.acm.org/doi/10.1016/j.dss.2017.07.004
Abisheva AGarimella VGarcia DWeber ICarterette BDiaz FCastillo CMetzler D(2014)Who watches (and shares) what on youtube? and when?Proceedings of the 7th ACM international conference on Web search and data mining10.1145/2556195.2566588(593-602)Online publication date: 24-Feb-2014
https://dl.acm.org/doi/10.1145/2556195.2566588
Weber IPopescu APennacchiotti MHe QIyengar ANejdl WPei JRastogi R(2013)PLEAD 2013Proceedings of the 22nd ACM international conference on Information & Knowledge Management10.1145/2505515.2505813(2553-2554)Online publication date: 27-Oct-2013
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