The document discusses methods for detecting and verifying online disinformation using natural language processing (NLP) and data analysis, focusing on the WeVerify architecture and collaborative tools. It outlines the COVID-19 misinformation classifier and various approaches over multiple years to analyze online conversations related to disinformation, highlighting the improvement of machine learning models. Additionally, it presents ongoing work related to multilingual misinformation analysis and the impact of social media on spreading disinformation.

1. DETECTING AND VERIFYING ONLINE DISINFORMATION:
HOW NLP AND DATA ANALYSIS CAN HELP
Carolina Scarton
c.scarton@sheffield.ac.uk
carolscarton
2º Workshop em Data Science
online workshop, 08/02/2021

2. WHY WEVERIFY?
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Credit: Zlatina Marinova (Ontotext)

3. WHY WEVERIFY?
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Credit: Zlatina Marinova (Ontotext)

4. WEVERIFY ARCHITECTURE
https://weverify.eu/
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5. TRULY MEDIA: COLLABORATIVE CROSS-MODAL
VERIFICATION WORKBENCH
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Credit: Zlatina Marinova (Ontotext)

6. TRULY MEDIA: COLLABORATIVE CROSS-MODAL
VERIFICATION WORKBENCH
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Credit: Zlatina Marinova (Ontotext)

7. TRULY MEDIA: COLLABORATIVE CROSS-MODAL
VERIFICATION WORKBENCH
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Credit: Zlatina Marinova (Ontotext)

8. VERIFICATION BROWSER PLUGIN
Available for Chrome, Firefox and Opera
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9. VERIFICATION BROWSER PLUGIN
USERS :
AND MANY MORE ...
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Credit: Zlatina Marinova (Ontotext)

10. VISUAL EXPLORATION OF DISINFORMATION NETWORKS
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Credit: Zlatina Marinova (Ontotext)

11. VISUAL EXPLORATION OF DISINFORMATION NETWORKS
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Credit: Zlatina Marinova (Ontotext)

12. BLOCKCHAIN DATABASE OF KNOWN FAKES
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Credit: Denis Teyssou (AFP)

13. BLOCKCHAIN DATABASE OF KNOWN FAKES
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14. BLOCKCHAIN DATABASE OF KNOWN FAKES
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15. BLOCKCHAIN DATABASE OF KNOWN FAKES
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16. BLOCKCHAIN DATABASE OF KNOWN FAKES
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17. BLOCKCHAIN DATABASE OF KNOWN FAKES
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18. BLOCKCHAIN DATABASE OF KNOWN FAKES
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19. VERACITY AND STANCE ANALYSIS OF ONLINE CONVERSATIONS
https://tweetveracity.gate.ac.uk/
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20. VERACITY AND STANCE ANALYSIS OF ONLINE CONVERSATIONS
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21. VERACITY AND STANCE ANALYSIS OF ONLINE CONVERSATIONS
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Year 1: Feature-based approach
➢ Textual + twitter metadata
information
➢ Traditional ML models
➢ Extended in WeVerify
macro-F1: 0.486

22. VERACITY AND STANCE ANALYSIS OF ONLINE CONVERSATIONS
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Year 1: Feature-based approach
➢ Textual + twitter metadata
information
➢ Traditional ML models
➢ Extended in WeVerify
macro-F1: 0.486
Year 2: Feature-based approach
➢ Textual + twitter metadata
information
➢ Traditional ML models
➢ Imbalanced data treatment
➢ New approach in WeVerify
macro-F1: 0.484

23. VERACITY AND STANCE ANALYSIS OF ONLINE CONVERSATIONS
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Year 1: Feature-based approach
➢ Textual + twitter metadata
information
➢ Traditional ML models
➢ Extended in WeVerify
macro-F1: 0.486
Year 2: Feature-based approach
➢ Textual + twitter metadata
information
➢ Traditional ML models
➢ Imbalanced data treatment
➢ New approach in WeVerify
macro-F1: 0.484
Year 2: BERT-based approach
➢ Deep-learning approach →
text only
➢ Imbalanced data treatment
➢ Platform independent
➢ New approach in WeVerify
macro-F1: 0.513

24. IMAGE/MEME OCR
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➢ The advert makes a highly misleading claim,
but this contained only in the image
○ it’s six in the next parliament, and none is a
completely new hospital
○ https://fullfact.org/election-2019/ads/
➢ There were between 800k-900k impressions of
adverts that used this image
https://www.facebook.com/ads/library/?active_status=all&ad_type=political_
and_issue_ads&country=GB&view_all_page_id=8807334278
Credit: Mark Greenwood (Sheffield)

25. COVID-19 CLASSIFIER
➢ Classification of misinformation into 10 different categories (Brennen et al., 2020):
• Public authority
• Community spread and impact
• Medical advice, self-treatments and virus effects
• Prominent actors
• Conspiracy theory
• Virus transmission
• Virus origins and properties
• Public preparedness
• Vaccines, medical treatment, and tests
• Cannot determine
Scott Brennen, Felix Simon, Philip Howard, and Rasmus Kleis Nielsen. 2020. Types, sources, and claims of covid-19 misinformation. Technical report, Reuters Institute
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26. COVID-19 CLASSIFIER
➢ IFCN dataset:
• Claim → rephrased by a fact-checker
• Explanation → why a claim is false
• Source link → page of the debunk
• Date → of publication in the IFCN Poynter website
• Type of media
• Original website
https://www.poynter.org/ifcn-covid-19-misinformation/
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27. COVID-19 CLASSIFIER
➢ Data annotation:
• 27 volunteers (English claims only)
• Assign most relevant class + a confidence score
• After removing low quality annotations → 1,293 debunks annotated (Cohen's k= 0.70)
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Credit: Xingyi Song (Sheffield)

28. COVID-19 CLASSIFIER
➢ Large-scale analysis:
• Start with Conspiracy theories
• Then Community spread and Virus origin in mid February
• Public authority action dominate in March
• Community Spread and Prominent actors are top topics in the later period
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Credit: Xingyi Song (Sheffield)

29. COVID-19 CLASSIFIER
➢ Large-scale analysis:
• 50% text
• ~ 50% of Virus origin and Public preparedness → videos
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Credit: Xingyi Song (Sheffield)

30. COVID-19 CLASSIFIER
➢ Large-scale analysis:
• Most spread in social media → Public Authority action and Community spread
• Instagram and Tiktok → Virus origin
• News, Youtube, Blog and TV → Conspiracy theory
• Messages apps → General medical advise
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Credit: Xingyi Song (Sheffield)

31. COVID-19 CLASSIFIER
➢ Large-scale analysis:
• Community spread → highest in misleading claims
• ~ 50% of No evidence → General advise
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Credit: Xingyi Song (Sheffield)

32. ONGOING AND FUTURE WORK
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33. COVID-19 SOCIAL MEDIA ANALYSIS
➢ Matching debunks (IFCN dataset) with tweets → finding misinformation
"[The coronavirus is] ‘new’ yet it was lab-created and patented in 2015"
https://www.poynter.org/?ifcn_misinformation=the-coronavirus-is-new-yet-it-was-lab-created-and-patented-in-2015
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34. COVID-19 SOCIAL MEDIA ANALYSIS
➢ Matching debunks (IFCN dataset) with tweets → finding debunks
"An alkaline diet could prevent COVID-19"
https://www.poynter.org/?ifcn_misinformation=alkaline-diet-could-prevent-covid-19
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35. DIGITAL COMPANION ASSISTANT
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36. MULTILINGUAL MISINFORMATION ANALYSIS
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37. MULTILINGUAL MISINFORMATION ANALYSIS
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A diet rich in alkaline foods can eliminate the coronavirus
2020/03/01: Spain https://maldita.es/malditaciencia/2020/03/01/cuerpo-alcalino-no-previene-coronavirus-tec-monterrey/ (WhatsApp)
2020/04/02: Indonesia https://cekfakta.tempo.co/fakta/715/fakta-atau-hoaks-benarkah-virus-corona-bisa-dibunuh-dengan-konsumsi-makanan-alkali
(WhatsApp)
2020/04/02: US
https://leadstories.com/hoax-alert/2020/04/Fact-Check-Alkaline-Diet-Does-NOT-Prevent-You-From-Getting-Coronavirus.html(Facebook)
2020/04/06: Venezuela https://efectococuyo.com/cocuyo-chequea/la-dieta-alcalina-ayuda-a-prevenir-la-covid-19/ (WhatsApp)
2020/06/16: Spain https://www.efe.com/efe/espana/efeverifica/consumir-alimentos-alcalinos-no-protege-del-coronavirus/50001435-4272618
(Facebook and Twitter)
2020/09/12: Turkey https://teyit.org/yeni-koronavirusun-alkali-beslenerek-yok-edilebilecegi-iddiasi (Facebook)
2020/12/09: Brazil https://www.aosfatos.org/noticias/dieta-rica-em-alimentos-alcalinos-nao-e-capaz-de-eliminar-o-coronavirus/ (Facebook)

38. GATECLOUD WEVERIFY SERVICES
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39. THANK YOU FOR YOUR ATTENTION!
www.weverify.eu
@WeVerify