CSCML_2018_Final.pdf

1. Fake News Measurement Using Topic Authenticity Aviad Elyashar, Jorge Bendahan, and Rami Puzis 1 Elyashar, BGU, Israel CSCML 2018, BGU, Beer-Sheva, Israel 2018 International Symposium On Cyber Security Cryptography and Machine Learning (CSCML 2018)

2. THE PROBLEM • Today, people tend to consume news from social media, rather than traditional news. 2 Elyashar, BGU, Israel CSCML 2018, BGU, Beer-Sheva, Israel

3. THE PROBLEM • Today, people tend to consume news from social media, rather than traditional news. • The nature of online news publication has changed, to the point that traditional fact checking and vetting are sometimes incomplete due to the flood of material from content aggregators 3 Elyashar, BGU, Israel CSCML 2018, BGU, Beer-Sheva, Israel

4. THE PROBLEM • Today, people tend to consume news from social media, rather than traditional news. • The nature of online news publication has changed, to the point that traditional fact checking and vetting are sometimes incomplete due to the flood of material from content aggregators. • Therefore, fake news are widely spreading. • Fake news detection on social media has recently become an emerging research that is attracting tremendous attention. 4 Elyashar, BGU, Israel CSCML 2018, BGU, Beer-Sheva, Israel Israel Hayom newspaper, 18.6.2018, Page 27

5. FAKE NEWS DEFINITION • Particular news articles, which are intentionally written false and could mislead readers to believe false information (Allcot & Gentzkov, 2017). • Satire news as fake news (Rubin et al. 2016) • Deceptive news as fake news, which includes serious fabrications, hoaxes and satires (Rubin et al., 2015). 5 Elyashar, BGU, Israel CSCML 2018, BGU, Beer-Sheva, Israel

6. DANGERS ASSOCIATED WITH FAKE NEWS • Breaking the authenticity balance of the news ecosystem 6 Elyashar, BGU, Israel CSCML 2018, BGU, Beer-Sheva, Israel

7. DANGERS ASSOCIATED WITH FAKE NEWS • Breaking the authenticity balance of the news ecosystem • Reputational damage caused by spreading rumors. 7 Elyashar, BGU, Israel CSCML 2018, BGU, Beer-Sheva, Israel

8. DANGERS ASSOCIATED WITH FAKE NEWS • Breaking the authenticity balance of the news ecosystem • Reputational damage caused by spreading rumors. • Fake perception • Fake news is usually manipulated to convey political messages or influence. 8 Elyashar, BGU, Israel CSCML 2018, BGU, Beer-Sheva, Israel

9. DANGERS ASSOCIATED WITH FAKE NEWS • Breaking the authenticity balance of the news ecosystem • Reputational damage caused by spreading rumors. • Fake perception • Fake news is usually manipulated to convey political messages or influence. • Spreading disinformation and propaganda 9 Elyashar, BGU, Israel CSCML 2018, BGU, Beer-Sheva, Israel

10. TOPIC AUTHENTICITY Measuring fake news in online social media based on estimating the distribution of fake news promoters among the accounts that contributed to the given online discussion. Elyashar, BGU, Israel CSCML 2018, BGU, Beer-Sheva, Israel 10

11. TOPIC AUTHENTICITY 0.1 0.2 0.8 11 Elyashar, BGU, Israel CSCML 2018, BGU, Beer-Sheva, Israel Aviad Elyashar, Jorge Bendahan, Rami Puzis, and Maria-Amparo Sanmateu, “Measurement of Online Discussion Authenticity within Online Social Media”, 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) 2017.

12. TOPIC AUTHENTICITY 12 Elyashar, BGU, Israel CSCML 2018, BGU, Beer-Sheva, Israel Aviad Elyashar, Jorge Bendahan, Rami Puzis “Is the News Deceptive? Fake News Detection Using Topic Authenticity”, SOTICS 2017.

13. QUESTIONS? 13 Elyashar, BGU, Israel CSCML 2018, BGU, Beer-Sheva, Israel

14. METHODOLOGY 14 Elyashar, BGU, Israel CSCML 2018, BGU, Beer-Sheva, Israel DATA COLLECTION TOPIC EXTRACTION TOPIC AUTHENTICITY SIMILARITY FUNCTIONS ACCOUNT AUTHENTICITY ACCOUNT LABELING BUYING CROWDTURFING SERVICES CLUSTERING MANUAL LABELING Aviad Elyashar, Jorge Bendahan, Rami Puzis, and Maria-Amparo Sanmateu, “Measurement of Online Discussion Authenticity within Online Social Media”, 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) 2017.

15. DATA COLLECTION • We collected Twitter data over a period of five months (April – September 2016), crawled with the help of third party service. • Virtual TV Manually Labeled Accounts dataset (key phrases such as: ‘Online TV,’ ‘Internet TV,’ ‘Smart TV’). • Virtual TV Verified Abuser dataset • Cyber Security dataset • Arabic Honeypot dataset (Morstatter et al. 2016) • Kaggle Twitter Propaganda dataset (November 2015 – May 2016 ) 15 Elyashar, BGU, Israel CSCML 2018, BGU, Beer-Sheva, Israel

16. TOPIC EXTRACTION • Using the topic detection algorithm of Latent Dirichlet allocation (LDA), we succeeded to identify online discussions. Topic 3 Topic 2 Topic 1 kill Iraq Syria airstrike Mosul Assad civilans ISIS Allepo 16 Elyashar, BGU, Israel CSCML 2018, BGU, Beer-Sheva, Israel Aviad Elyashar, Jorge Bendahan, Rami Puzis “Is the News Deceptive? Fake News Detection Using Topic Authenticity”, SOTICS 2017.

17. ACCOUNT SIMILARITY FUNCTIONS • We defined several similarity functions: • Common-posts: shows which accounts spread the same content across the online social network. • Bag-of-words: measures the similarity between the vocabularies used by two accounts. • Topic-distribution: measures the similarity between two topic vectors that represent which topic is more related to the given account. • Profie-properties: accounts can be compare based on the features extracted from their online social media profiles. • Behavioral-properties: compares the account behavior feature vectors where each account is represented by vectors of features describing the account behavioral characteristics. Elyashar, BGU, Israel CSCML 2018, BGU, Beer-Sheva, Israel 17

18. EVALUATION 18 Elyashar, BGU, Israel CSCML 2018, BGU, Beer-Sheva, Israel

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