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Multimedia Tools and Applications

, Volume 77, Issue 12, pp 15545–15571 | Cite as

Verifying information with multimedia content on twitter

A comparative study of automated approaches
  • Christina Boididou
  • Stuart E. Middleton
  • Zhiwei Jin
  • Symeon Papadopoulos
  • Duc-Tien Dang-Nguyen
  • Giulia Boato
  • Yiannis Kompatsiaris
Article

Abstract

An increasing amount of posts on social media are used for disseminating news information and are accompanied by multimedia content. Such content may often be misleading or be digitally manipulated. More often than not, such pieces of content reach the front pages of major news outlets, having a detrimental effect on their credibility. To avoid such effects, there is profound need for automated methods that can help debunk and verify online content in very short time. To this end, we present a comparative study of three such methods that are catered for Twitter, a major social media platform used for news sharing. Those include: a) a method that uses textual patterns to extract claims about whether a tweet is fake or real and attribution statements about the source of the content; b) a method that exploits the information that same-topic tweets should be also similar in terms of credibility; and c) a method that uses a semi-supervised learning scheme that leverages the decisions of two independent credibility classifiers. We perform a comprehensive comparative evaluation of these approaches on datasets released by the Verifying Multimedia Use (VMU) task organized in the context of the 2015 and 2016 MediaEval benchmark. In addition to comparatively evaluating the three presented methods, we devise and evaluate a combined method based on their outputs, which outperforms all three of them. We discuss these findings and provide insights to guide future generations of verification tools for media professionals.

Keywords

Fake detection Verification Credibility Veracity Trust Social media Twitter Multimedia 

Notes

Acknowledgements

This work has been supported by the REVEAL and InVID projects, partially funded by the European Commission (FP7-610928 and H2020-687786 respectively).

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Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  • Christina Boididou
    • 1
  • Stuart E. Middleton
    • 2
  • Zhiwei Jin
    • 3
  • Symeon Papadopoulos
    • 1
  • Duc-Tien Dang-Nguyen
    • 4
    • 5
  • Giulia Boato
    • 4
  • Yiannis Kompatsiaris
    • 1
  1. 1.Information Technologies Institute, CERTHThessalonikiGreece
  2. 2.IT Innovation CentreUniversity of SouthamptonSouthamptonEngland
  3. 3.University of Chinese Academy of SciencesBeijingChina
  4. 4.University of TrentoTrentoItaly
  5. 5.Dublin City UniversityDublinIreland

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