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AGGREGATING &
ANALYZING THE CONTEXT
OF SOCIAL MEDIA CONTENT
Symeon (Akis) Papadopoulos
Senior Research Scientist @ CERTH
Workshop on Content Verification Tools
June 6, 2018 @ Thessaloniki, Greece
Rumours and their spread
Example rumour cascade
Vosoughi, S., Roy, D., & Aral, S. (2018). The spread of true and false news online.
Science, 359(6380), 1146-1151.
Volume of cascades per year
Topic frequency of cascades
Falsehood diffused significantly farther,
faster, deeper, and more broadly than
the truth in all categories of information!
Verification of social media content
News organisations / professionals
Verification processes
Social media streams
Verification tools
Credibility Signals
Signals Framework, by Aviv Ovadya @ Center for Social Media Responsibility
https://docs.google.com/presentation/d/1pgy43Qbo2yOR13Z7N_1yuuTnR0O4390zSyj2PCHKX4g/
Context Analysis & Aggregation Service
Data inference
and aggregation
Input Video
Metadata
from source
User Interface
Context Analysis and Aggregation Toolkit
• Back-end service implementation
• Orchestrate data collection from third party services
• Perform data aggregation and indexing
• Perform inferences
• REST API
• http://caa.iti.gr:8080/swagger/?url=http://caa.iti.gr:808
0/Context_Aggregation_and_Analysis_v8.json
• Web-based user interface
• http://caa.iti.gr/
• InVID Plug-in tab
• To be demonstrated in hands-on session
Overview of features
• Video metadata from platform of interest
• Video publisher metadata (e.g. YouTube channel,
Twitter account, Facebook page)
• Comments & verification comments
• Instant generation of video thumbnails and
shortcuts for reverse image search
• Weather at the claimed time and place of video
• Twitter sharing timeline
• Includes credibility-oriented classification of tweets
In InVID Plugin
Context Analysis in InVID Plugin
Web-based User Interface
Linked tool: Tweet Verification Assistant
http://tiny.cc/tw_verify
The Fake Video Corpus
• 200 fake and 188 real newsworthy videos
• 2206 fake and 1209 real near-duplicates
• YouTube search & visual similarity filtering
• 388 cascades of near-duplicate videos
Temporal profiles of video cascades
• Fake videos keep
reappearing years
later
• Real videos tend
to be reproduced
mostly during the
first month
Automatic Contextual Verification
• Different classification approaches
• Comment cred: Use Tweet Verification Assistant and aggregate
comment cred scores into a histogram that is used as feature vector
• Video metadata: Train SVM classifier using text-based metadata
from video description and channel statistics
• Fusion: concatenate comment cred and video metadata vectors
• Training on Fake Video Corpus
• Challenge: Generalize on future cases
F1-Score
Comment cred 0.64
Video metadata 0.77
Fusion 0.78
Ideal fusion 0.96
Ideal fusion is a
hypothetical optimal way
to combine comment cred
and video metadata.
Summary
• Media-based disinformation is complex!
• Misleading content tends to be shared more!
• There are several technologies available for tackling
the problem, each with its limitations
• End users (journalists) should be the beginning and
the end of the verification process!
• Technology on its own is not sufficient: we need to
take into account the human and social facets of
the problem: media literacy!
Thank you for your attention!
https://revealproject.eu/
http://www.invid-project.eu/
Get in touch!
Symeon Papadopoulos papadop@iti.gr / @sympap
Olga Papadopoulou olgapapa@iti.gr / @olgapapa
Ex. 1: Unbelievable Explosion Moment In Brussel Airport
https://www.youtube.com/watch?v=mZes8-tzZ0w
Ex.2: Video claiming to depict a Hezbollah sniper
https://www.youtube.com/watch?v=nkQ-ij3LTTM
Ex. 3: Huge Pod of Mermaids Caught on Tape by DRONE
https://www.youtube.com/watch?v=c7GJPhwSFHQ
Ex. 4: Snowboarder Girl Chased By Bear
https://www.youtube.com/watch?v=vT_PNKg3v7s
Ex. 5: Syrian hero boy rescue girl in shootout
https://www.youtube.com/watch?v=UTeqpMQKZaY
Ex. 6: Unbelievable Explosion Moment In Brussel Airport
https://www.youtube.com/watch?v=mSFf2mzCrTg
Ex. 7: Pope Francis SLAPS Donald Trump's Hand….
https://www.youtube.com/watch?v=Q8AQler0Dq0
Ex. 8: Lion takes revenge on trophy hunter
https://www.facebook.com/uniladmag/videos/2293398324016547/
Ex. 9: Terry Tufferson channel
https://www.youtube.com/channel/UCHH9FQBvo3X4Vmf78c0OASw/videos

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Aggregating and Analyzing the Context of Social Media Content

  • 1. AGGREGATING & ANALYZING THE CONTEXT OF SOCIAL MEDIA CONTENT Symeon (Akis) Papadopoulos Senior Research Scientist @ CERTH Workshop on Content Verification Tools June 6, 2018 @ Thessaloniki, Greece
  • 2.
  • 3. Rumours and their spread Example rumour cascade Vosoughi, S., Roy, D., & Aral, S. (2018). The spread of true and false news online. Science, 359(6380), 1146-1151. Volume of cascades per year Topic frequency of cascades Falsehood diffused significantly farther, faster, deeper, and more broadly than the truth in all categories of information!
  • 4.
  • 5. Verification of social media content News organisations / professionals Verification processes Social media streams Verification tools
  • 6. Credibility Signals Signals Framework, by Aviv Ovadya @ Center for Social Media Responsibility https://docs.google.com/presentation/d/1pgy43Qbo2yOR13Z7N_1yuuTnR0O4390zSyj2PCHKX4g/
  • 7. Context Analysis & Aggregation Service Data inference and aggregation Input Video Metadata from source User Interface
  • 8. Context Analysis and Aggregation Toolkit • Back-end service implementation • Orchestrate data collection from third party services • Perform data aggregation and indexing • Perform inferences • REST API • http://caa.iti.gr:8080/swagger/?url=http://caa.iti.gr:808 0/Context_Aggregation_and_Analysis_v8.json • Web-based user interface • http://caa.iti.gr/ • InVID Plug-in tab • To be demonstrated in hands-on session
  • 9. Overview of features • Video metadata from platform of interest • Video publisher metadata (e.g. YouTube channel, Twitter account, Facebook page) • Comments & verification comments • Instant generation of video thumbnails and shortcuts for reverse image search • Weather at the claimed time and place of video • Twitter sharing timeline • Includes credibility-oriented classification of tweets In InVID Plugin
  • 10. Context Analysis in InVID Plugin
  • 12. Linked tool: Tweet Verification Assistant http://tiny.cc/tw_verify
  • 13. The Fake Video Corpus • 200 fake and 188 real newsworthy videos • 2206 fake and 1209 real near-duplicates • YouTube search & visual similarity filtering • 388 cascades of near-duplicate videos
  • 14. Temporal profiles of video cascades • Fake videos keep reappearing years later • Real videos tend to be reproduced mostly during the first month
  • 15. Automatic Contextual Verification • Different classification approaches • Comment cred: Use Tweet Verification Assistant and aggregate comment cred scores into a histogram that is used as feature vector • Video metadata: Train SVM classifier using text-based metadata from video description and channel statistics • Fusion: concatenate comment cred and video metadata vectors • Training on Fake Video Corpus • Challenge: Generalize on future cases F1-Score Comment cred 0.64 Video metadata 0.77 Fusion 0.78 Ideal fusion 0.96 Ideal fusion is a hypothetical optimal way to combine comment cred and video metadata.
  • 16. Summary • Media-based disinformation is complex! • Misleading content tends to be shared more! • There are several technologies available for tackling the problem, each with its limitations • End users (journalists) should be the beginning and the end of the verification process! • Technology on its own is not sufficient: we need to take into account the human and social facets of the problem: media literacy!
  • 17. Thank you for your attention! https://revealproject.eu/ http://www.invid-project.eu/ Get in touch! Symeon Papadopoulos papadop@iti.gr / @sympap Olga Papadopoulou olgapapa@iti.gr / @olgapapa
  • 18. Ex. 1: Unbelievable Explosion Moment In Brussel Airport https://www.youtube.com/watch?v=mZes8-tzZ0w
  • 19. Ex.2: Video claiming to depict a Hezbollah sniper https://www.youtube.com/watch?v=nkQ-ij3LTTM
  • 20. Ex. 3: Huge Pod of Mermaids Caught on Tape by DRONE https://www.youtube.com/watch?v=c7GJPhwSFHQ
  • 21. Ex. 4: Snowboarder Girl Chased By Bear https://www.youtube.com/watch?v=vT_PNKg3v7s
  • 22. Ex. 5: Syrian hero boy rescue girl in shootout https://www.youtube.com/watch?v=UTeqpMQKZaY
  • 23. Ex. 6: Unbelievable Explosion Moment In Brussel Airport https://www.youtube.com/watch?v=mSFf2mzCrTg
  • 24. Ex. 7: Pope Francis SLAPS Donald Trump's Hand…. https://www.youtube.com/watch?v=Q8AQler0Dq0
  • 25. Ex. 8: Lion takes revenge on trophy hunter https://www.facebook.com/uniladmag/videos/2293398324016547/
  • 26. Ex. 9: Terry Tufferson channel https://www.youtube.com/channel/UCHH9FQBvo3X4Vmf78c0OASw/videos

Editor's Notes

  1. https://twitter.com/Thomas_Binder/status/984934979451879424 https://twitter.com/Thomas_Binder/status/985665154695262211 https://www.dailysabah.com/syrian-crisis/2018/04/18/cardiologist-apologizes-after-falsely-accusing-white-helmets-of-staging-syria-chemical-attack
  2. Many different types of fake Web image forensics Tweet verification assistant and a very challenging issue in the coming years
  3. http://truepublica.org.uk/global/video-used-mainstream-media-reports-brussels-terror-attacks-emerge-fakes/
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  7. http://truepublica.org.uk/global/video-used-mainstream-media-reports-brussels-terror-attacks-emerge-fakes/
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