LTVSDISINFO: THE CHALLENGES OF APPLYING LANGUAGE. TECHNOLOGIES IN DISINFORMATION DETECTION AND ANALYSIS

1. LTVSDISINFO: THE CHALLENGES OF APPLYING LANGUAGE
TECHNOLOGIES IN DISINFORMATION DETECTION AND ANALYSIS
24 November 2020, Language Technology vs. Disinformation Workshop
Kalina Bontcheva
K.Bontcheva@sheffield.ac.uk
@kbontcheva

2. Verifying Rumours and Analysing
Conversational Threads
➢ Rumour is “a circulating story of questionable veracity, which is apparently
credible but hard to verify, and produces sufficient skepticism and/or anxiety”
➢ Key is not only whether it is true or false, but also:
• Who is spreading it?
• Are people believing it?
• Rumour stance: support, deny, question, comment
• Do we need to debunk it or not?
➢ Assist journalists through automated analysis embedded in a user-friendly tool
➢ The tool is undergoing testing, to be integrated within the WeVerify Browser
Plugin
2

3. RUMOUR STANCE CLASSIFICATION
➢ What is being said about a rumour?

4. RUMOUR STANCE CLASSIFICATION
➢ What is being said about a rumour?

5. RUMOUR STANCE CLASSIFICATION
➢ What is being said about a rumour?

6. RUMOUR STANCE CLASSIFICATION
➢ What is being said about a rumour?

7. RUMOUR STANCE CLASSIFICATION
➢ Stance of replies can help in predicting veracity (Mendoza et al., 2010;
Kumar and Carley, 2019) → specially denies (Zubiaga et al., 2016)

8. RUMOUR STANCE CLASSIFICATION
➢ Stance of replies can help in predicting veracity (Mendoza et al., 2010;
Kumar and Carley, 2019) → specially denies (Zubiaga et al., 2016)
➢ However,
• four-class classification problem
• support, deny, query, comment
• Highly imbalanced problem
• Support and denies
• most important minority classes
• hardest to recognise correctly
• Different from traditional stance classification task

9. Analysing Memes and Adverts: Example Election Ad
9
http://demos.gate.ac.uk/weverify/ocr/
https://www.facebook.com/ads/library/?active_status=all&ad_t
ype=political_and_issue_ads&country=GB&view_all_page_id
=8807334278
● Misleading claim made in the ad image only
● 800k-900k impressions
● Misleading
○ it’s six in the next parliament, and none is a
completely new hospital
● https://fullfact.org/election-2019/ads/

10. Rumour Veracity and Stance as a Service
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https://cloud.gate.ac.uk

11. 11
Disinformation: Same image, different narratives
Credit: Denis Teyssou (AFP)

12. 12
Google getting confused
Database of
Known Fakes can
help!

13. 13
Database of Debunks and Known Fakes

14. 14
Database of Debunks and Known Fakes (2)

15. 15
Database of Debunks and Known Fakes (2)
➢ Automatic language detection; search by language
➢ LT for semantic enrichment (search by country, organisation, person, etc)
➢ Machine translation of the debunks (if requested by the users)
• Under development, based on MT from the European Language Grid

16. 16
LT in Action: The INVID/WeVerify Browser Plugin

17. 17
LT in Action: The Verification Assistant

18. 18
COVID-19 Misinformation Classifier
Further details:
https://arxiv.org/abs/2006.03354

19. 19
Exploration and visualisation of COVID-19 misinfo
Joint work with First Draft:
https://datadeficits.firstdraftnews.org/
https://firstdraftnews.org/long-form-article/data-deficits/

20. 20
Exploration and visualisation of COVID-19 misinfo
Joint work with First Draft:
https://datadeficits.firstdraftnews.org/
https://firstdraftnews.org/long-form-article/data-deficits/

21. THANK YOU FOR YOUR ATTENTION!
www.weverify.eu
@WeVerify
Thanks to Carolina Scarton (USFD)
Denis Teyssou (AFP) and,
Zlatina Marinova (ONTOTEXT)
for the slides (and work done!)