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Verifying Multimedia Use at MediaEval 2015

Presentation of the task overview in MediaEval 2015, Wurzen, Germany. Verifying Multimedia Use is about detecting tweets that carry misleading information and content.

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Verifying Multimedia Use at MediaEval 2015
Christina Boididou1, Katerina Andreadou1, Symeon Papadopoulos1,
Duc-Tien Dang-Nguyen2, Giulia Boato2, Michael Riegler3 & Yiannis Kompatsiaris1
1 Information Technologies Institute (ITI), CERTH, Greece
2 University of Trento, Italy
3 Simula Research Lab, Norway
MediaEval 2015 Workshop, Sept. 14-15, 2015, Wurzen, Germany
Real or Fake
#2
Real or Fake
#3
Real photo
captured April 2011 by WSJ
but
heavily tweeted during Hurricane Sandy
(29 Oct 2012)
Tweeted by multiple sources &
retweeted multiple times
Original online at:
http://blogs.wsj.com/metropolis/2011/04/28/weather-
journal-clouds-gathered-but-no-tornado-damage/
Task at a Glance
#4
TWEET
IMAGE
MEDIAEVAL
SYSTEM
FAKE
REAL
Systems may use:
• Tweet text
• Tweet metadata
• Twitter user profile
• Image content
AUTHOR
(PROFILE)
A Typology of Fake: Reposting of Real
• Photos from past events reposted as being
associated to current event
#5
A Typology of Fake: Reposting of Art
• Artworks presented as real imagery
#6
Ad

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Verifying Multimedia Use at MediaEval 2015

  • 1. Verifying Multimedia Use at MediaEval 2015 Christina Boididou1, Katerina Andreadou1, Symeon Papadopoulos1, Duc-Tien Dang-Nguyen2, Giulia Boato2, Michael Riegler3 & Yiannis Kompatsiaris1 1 Information Technologies Institute (ITI), CERTH, Greece 2 University of Trento, Italy 3 Simula Research Lab, Norway MediaEval 2015 Workshop, Sept. 14-15, 2015, Wurzen, Germany
  • 3. Real or Fake #3 Real photo captured April 2011 by WSJ but heavily tweeted during Hurricane Sandy (29 Oct 2012) Tweeted by multiple sources & retweeted multiple times Original online at: http://blogs.wsj.com/metropolis/2011/04/28/weather- journal-clouds-gathered-but-no-tornado-damage/
  • 4. Task at a Glance #4 TWEET IMAGE MEDIAEVAL SYSTEM FAKE REAL Systems may use: • Tweet text • Tweet metadata • Twitter user profile • Image content AUTHOR (PROFILE)
  • 5. A Typology of Fake: Reposting of Real • Photos from past events reposted as being associated to current event #5
  • 6. A Typology of Fake: Reposting of Art • Artworks presented as real imagery #6
  • 7. A Typology of Fake: Speculations • Speculations regarding the association of persons or actions to current event #7
  • 8. A Typology of Fake: Photoshopping • Digitally manipulated photos #8
  • 12. Ground Truth Generation • Data (tweet) collection – Historic (known cases discussed online) using Topsy – Real-time during major events using streaming API • Tweet set expansion – Near-duplicate image search + human inspection was used to increase the number of associated tweets • Label assignment – Fake/real labels were manually assigned after consulting online reports that were posted after each event #12
  • 13. Annotation Challenges • Tweets declaring that the embedded image is fake • Tweets with obvious manipulations • All those cases were manually checked and removed from both the development and test set! #13
  • 14. Verification Corpus - Dev #14 Event Name fake real #images #tweets #users #images #tweets #users Hurricane Sandy 62 5,559 5,432 148 4,664 4,446 Boston Marathon bombing 35 189 187 28 344 310 Sochi Olympics 26 274 252 - - - MH370 Flight 29 501 493 - - - Bring Back Our Girls 7 131 126 - - - Columbian Chemicals 15 185 87 - - - Passport hoax 2 44 44 - - - Rock Elephant 1 13 13 - - - Underwater bedroom 3 113 112 - - - Livr mobile app 4 9 9 - - - Pig fish 1 14 14 - - - Total 185 7,032 6,769 176 5,008 4,756
  • 15. Verification Corpus - Test #15 Event Name fake real #images #tweets #users #images #tweets #users Solar Eclipse 6 137 135 4 140 133 Samurai with girl 4 218 212 - - - Nepal Earthquake 21 356 343 11 1004 934 Garissa Attack 2 6 6 2 73 72 Syrian boy 1 1786 1692 - - - Varoufakis 1 61 59 - - - • Evaluation was based on classic IR/ML measures: Precision, Recall, F-measure (target class: fake) • Participants were allowed to mark a tweet as “unknown” (expected to result in reduced recall)
  • 16. Results #16 Team Run Recall Precision F-Score MCG-ICT run1 0.921 0.964 0.942 run2 0.922 0.937 0.930 UoS-ITI run1 0.032 1.000 0.063 run2 0.017 1.000 0.034 run3 0.034 1.000 0.065 run4 0.720 1.000 0.837 CERTH- UNITN run1 0.794 0.733 0.762 run2 0.749 0.994 0.854 run3 0.922 0.736 0.819 run4 0.798 0.860 0.828 run5 0.967 0.862 0.911
  • 17. Results: Examples #1 • All participants failed to classify those correctly • True label: Fake / Predicted: Real #17
  • 18. Results: Examples #2 • All participants classified these correctly. #18 fake real
  • 19. Results: Examples #3 • Only participant#1 predicted those correctly. #19 fake real
  • 20. Results: Examples #4 • Only participant#2 predicted those correctly. #20 real real
  • 21. Results: Examples #5 • Only participant#3 predicted those correctly. #21 fake real
  • 22. Future Plans • Move beyond tweets + images – Blog/news articles – Public Facebook posts (in pages) – Other? • Move beyond the simple fake/real distinction – Real, but inaccurate – Messages expressing doubt – Other? • Use different evaluation measures – AUC probably better especially when there is class imbalance #22
  • 23. Thank you! • Code: https://github.com/MKLab-ITI/image-verification-corpus • Get in touch: @sympapadopoulos / papadop@iti.gr @CMpoi / boididou@iti.gr