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Fake tweet buster: a webtool to identify users promoting fake news ontwitter

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Diego Saez-TrumperAuthors Info & Claims

HT '14: Proceedings of the 25th ACM conference on Hypertext and social media

Pages 316 - 317

https://doi.org/10.1145/2631775.2631786

Published: 01 September 2014 Publication History

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Abstract

We present the "Fake Tweet Buster" (FTB), a web application that identifies tweets with fake images and users that are consistently uploading and/or promoting fake information on Twitter. To do that we mix three techniques: (i) reverse image searching, (ii) user analysis and (iii) a crowd sourcing approach to detected that kind of malicious users on Twitter. Using that information we provide a credibility classification for the tweet and the user.

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Cited By

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Dagan AGuy INovgorodov S(2023)Shop by image: characterizing visual search in e-commerceInformation Retrieval Journal10.1007/s10791-023-09418-126:1Online publication date: 3-Mar-2023
https://doi.org/10.1007/s10791-023-09418-1
Umair MBouguettaya ALakhdari A(2023)Detecting Changes in Crowdsourced Social Media ImagesService-Oriented Computing10.1007/978-3-031-48424-7_15(195-211)Online publication date: 20-Nov-2023
https://doi.org/10.1007/978-3-031-48424-7_15
Straub JSpradling MFedor B(2022)Assessment of Factors Impacting the Perception of Online Content Trustworthiness by Age, Education and GenderSocieties10.3390/soc1202006112:2(61)Online publication date: 31-Mar-2022
https://doi.org/10.3390/soc12020061
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Index Terms

Fake tweet buster: a webtool to identify users promoting fake news ontwitter
1. Applied computing
  1. Operations research
    1. Decision analysis
2. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Document filtering
      2. Information extraction
  2. Information systems applications
    1. Decision support systems

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Information & Contributors

Information

Published In

HT '14: Proceedings of the 25th ACM conference on Hypertext and social media

September 2014

346 pages

ISBN:9781450329545

DOI:10.1145/2631775

General Chairs:
Leo Ferres
University of Concepción, Chile
,
Gustavo Rossi
University of La Plata, Argentina
,
Program Chairs:
Virgilio Almeida
Federal University of Minas Gerais, Brazil
,
Eelco Herder
L3S Research Center, Germany

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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SIGCHI: ACM Special Interest Group on Computer-Human Interaction

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 01 September 2014

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Ministerio de Economía y Competitividad

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HT '14

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SIGWEB

HT '14: 25th ACM Conference on Hypertext and Social Media

September 1 - 4, 2014

Santiago, Chile

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HT '14 Paper Acceptance Rate 49 of 86 submissions, 57%;

Overall Acceptance Rate 378 of 1,158 submissions, 33%

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Cited By

View all

Dagan AGuy INovgorodov S(2023)Shop by image: characterizing visual search in e-commerceInformation Retrieval Journal10.1007/s10791-023-09418-126:1Online publication date: 3-Mar-2023
https://doi.org/10.1007/s10791-023-09418-1
Umair MBouguettaya ALakhdari A(2023)Detecting Changes in Crowdsourced Social Media ImagesService-Oriented Computing10.1007/978-3-031-48424-7_15(195-211)Online publication date: 20-Nov-2023
https://doi.org/10.1007/978-3-031-48424-7_15
Straub JSpradling MFedor B(2022)Assessment of Factors Impacting the Perception of Online Content Trustworthiness by Age, Education and GenderSocieties10.3390/soc1202006112:2(61)Online publication date: 31-Mar-2022
https://doi.org/10.3390/soc12020061
Spradling MStraub J(2022)Analysis of the Impact of Age, Education and Gender on Individuals’ Perception of Label Efficacy for Online ContentInformation10.3390/info1311051613:11(516)Online publication date: 28-Oct-2022
https://doi.org/10.3390/info13110516
Straub JSpradling MFedor B(2022)Assessment of Consumer Perception of Online Content Label Efficacy by Income Level, Party Affiliation and Online Use LevelsInformation10.3390/info1305025213:5(252)Online publication date: 13-May-2022
https://doi.org/10.3390/info13050252
Straub JSpradling M(2022)Americans’ Perspectives on Online Media Warning LabelsBehavioral Sciences10.3390/bs1203005912:3(59)Online publication date: 23-Feb-2022
https://doi.org/10.3390/bs12030059
Thakur N(2022)Twitter Big Data as a Resource for Exoskeleton Research: A Large-Scale Dataset of about 140,000 Tweets from 2017–2022 and 100 Research QuestionsAnalytics10.3390/analytics10200071:2(72-97)Online publication date: 23-Sep-2022
https://doi.org/10.3390/analytics1020007
Rauf FIrfan RMushtaq LAshraf M(2022)Fake News Detection in Urdu using Deep LearningVFAST Transactions on Software Engineering10.21015/vtse.v10i4.129010:4(151-167)Online publication date: 31-Dec-2022
https://doi.org/10.21015/vtse.v10i4.1290
Hoy NKoulouri T(2022)Exploring the Generalisability of Fake News Detection Models2022 IEEE International Conference on Big Data (Big Data)10.1109/BigData55660.2022.10020583(5731-5740)Online publication date: 17-Dec-2022
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Idrees AHelmy YKhedr A(2022)Credibility aspects’ perceptions of social networks, a surveySocial Network Analysis and Mining10.1007/s13278-022-00924-612:1Online publication date: 1-Aug-2022
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