[Data Meetup] Data Science in Journalism - Tanbih, QCRI and MIT
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Check out our Data Science Meetup devoted to Data Science in Journalism Dr. Preslav Nakov, Principal Scientist at QCRI, presented the #Tanbih news aggregator, which makes people aware of what they are reading. The aggregator features media profiles that show the general factuality of reporting, the degree of propagandistic content, the hyper-partisanship, the leading political ideology, the general frame of reporting, the stance with respect to various claims and topics, as well as the audience reach and the audience bias in social media. This is part of the Tanbih project, which is developed in collaboration with MIT. Special thanks to our partners from #Ontotext, #Telelink and #Leanplum! #DSS #DataMeetup
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[Data Meetup] Data Science in Journalism - Tanbih, QCRI and MIT
- 1. Tanbih: a News Aggregator that Makes People Aware of Propaganda and Bias in the News Preslav Nakov Qatar Computing Research Institute, HBKU Data Science Society August 22, 2019
- 2. “Fake News”: A Weapon of Mass Deception
- 3. QCRI/MIT-CSAIL Annual Meeting – March 2018 3 3
- 4. QCRI/MIT-CSAIL Annual Meeting – March 2018 4 4
- 5. QCRI/MIT-CSAIL Annual Meeting – March 2018 5 5
- 6. QCRI/MIT-CSAIL Annual Meeting – March 2018 6 6
- 7. QCRI/MIT-CSAIL Annual Meeting – March 2018 7 7
- 8. 8
- 9. QCRI/MIT-CSAIL Annual Meeting – March 2018 9 9
- 10. QCRI/MIT-CSAIL Annual Meeting – March 2018 10 10
- 11. QCRI/MIT-CSAIL Annual Meeting – March 2018 11 11 “Propaganda becomes ineffective the moment we are aware of it.” Joseph Goebbels (1897-1945)
- 15. Limit the effect of disinformation. Tanbih: News Aggregator 15 Show stance, bias, propaganda in the news. Promote different viewpoints, engage users. Highlights: • Disinformation-aware news aggregator • Media profiles: can fact-check the news before they were even written • Fine-grained propaganda analysis: adversarial attacks are very hard • Focus on MENA: media, events, languages
- 16. The Tanbih Team at QCRI(present & past) 16 Haewoon Kwak Jisun An Social Computing Arabic Language Technologies Alberto Barrón-Cedeño Giovanni Da San Martino Preslav Nakov Hamdy Mubarak Yifan Zhang Kareem Darwish Ahmed Abdelali Banu Akdenizli (NU-Q) Steering Committee Salvatore Romeo Shaden Shaar Israa Jaradat
- 17. Jim Glass Mitra Mohtarami MIT Collaborators (present & past) 17 Ramy Baly (QCRI-MIT) QCRI-MIT project: Arabic Speech and Language Processing for Cross-Language Information Search and Fact Verification Abdelrhman Saleh Seunghak Yu (QCRI-MIT) Regina Barzilay Darsh Shah Yujia Bao
- 18. Ivan Koychev Georgi KaradzhovMomchil Hardalov Sofia University Collaborators(present & past) Todor Mihaylov Tsvetomila Mihaylova Pepa Gencheva Slavena VasilevaDimitar AleksandrovGeorgi Georgiev Spas Kyuchukov Yoan DinkovDimitrina Zlatkova Todor Staykovski Dimitar Kerezov Peter StefanovKristiyan Mitov Atanas Atanasov Daniel Kopev Daniel Shaprin Stanislav Trifonov 18
- 19. Collaborations
- 20. Media Bias and Factuality of Reporting
- 21. Previous Work Focused on Fact-Checking Claims, Rumors, Articles 21
- 22. But Can We Check All News and All Claims in the World? 22
- 23. Instead: We Go After the Source http://www.angrypatriotmovement.com/ 23
- 24. Thus, We Can Detect “Fake News” Before They Were even Written! 24
- 26. Extra Slides
- 28. 28
- 29. 29
- 30. 30 Our Analysis of the Previous Medium (in Tanbih)
- 31. 31 In Media Bias/Fact Check
- 32. Propaganda
- 33. Highly-biased and fake news use propaganda techniques to convey their message 33
- 34. 34 Fine-Grained Propaganda Detection Hack The News Datathon (Jan 2019): 250 participants from 50 countries NLP4IF@EMNLP’2019 (upcoming): shared task SemEval’2019 Task 11 (upcoming): shared task New Dataset • 18 techniques • 350k words • 400 man hours • 7.3k instances 34
- 35. 35
- 37. Quiz What is the main problem with “Fake News”? a. they are false b. they are weaponized c. they are viral d. they can be personalized