Evaluating Event Credibility on Twitter

1. Evaluating Event Credibility on Twitter Presented by YananXie College of Computer Science, Zhejiang University 2012

2. Motivation Since its launch in 2006, Twitter has gained huge popularity. 4% of the massive number of tweets constitute news. Recent surveys show that just ~8% people trust Twitter, while just ~12% trust their friends’ Twitter streams. A lot of rumors have been spread using Twitter in the recent past and have resulted into a lot of damage

3. Problem Definition Example: A bull jumped into the crowds in a Spanish arena injuring 30 people.

4. Problem Definition

5. Classification-Based Approach

6. Classification-Based Approach

7. Classification-Based Approach

8. Classification-Based Approach

9. Classification-Based Approach Lacks: With high probability, credible users provide credible tweets; Average credibility of tweets is higher for credible events than that for non- credible events; and Events sharing many common words and topics should have similar credibility scores.

10. Basic Credibility Analysis

11. Basic Credibility Analysis

12. Basic Credibility Analysis

13. Basic Credibility Analysis

14. Basic Credibility Analysis

15. Performing Event Graph Optimization similar events get similar credibility scores, and change in event credibility vector is controlled.

16. Experiments

17. Experiments

18. Experiments

19. Experiments

20. Experiments

21. Thank you!