1. © 2014 Networking for Information Communications and Energy Lab. How do I viralize a YouTube video and tip a Groupon deal Prof. Hongseok Kim Sogang University, EE Networked Life: 20 Questions and Answers (M. Chiang, Princeton University)

2. 2 YouTube  Started in Feb. 2005, Acquired by Google in 2006.  By 2008, second largest search engine.  By 2011, 40% of Internet video watched on YouTube 1 Billion videos run each day  Recommendation by co-visitation counts and YouTube graph  Social media marketing advice

3. 3 YouTube recommendation  Co-visitation count »This gives rise to a set of related videos for each video a link relationship among the videos, and thus a graph with the videos as nodes. »From the set of videos in k hops from a given video, together with explicit ratings by users and matching of keywords in the video title, tags, and summary. »YouTube generates a top n recommendation page.  Watch count »Widely watched videos to become even more widely watched, possibly becoming viral.

4. 4 What does “Viral” mean ?  The notion of “viral” means that the rate-of-adoption curve should exhibit three features High peak Large volume A short time to rise to the peak

5. 5 Tipping  Groupon was formed in 2008, and after two years was generating over $300 million annual revenue from more than 500 cities.  In a daily deal operation, a supplier of some goods or services announces a special discount, which must have a large enough number of users signed up within a 24-hour period.  If the number of users exceeds the target threshold, the deal is tipped, and each user has 3 months to redeem the coupon.  The power of crowds »Need a threshold number of people within 24 hours.

6. 6 Pop quiz

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8. 8 Influence power  Intrinsic value Music, A particular product you like  Network effect »Decision depends on what others do −The fact that others like a product gives you information −The value of the service or product depends on the number of people who use it, an example of positive externality.

9. 9 Pop quiz

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11. 11 Information cascade  YouTube experiment run by Salganik, Dodds, and Watts in 2005. »Each of the 14,341 participants rated each of the S=48 songs from 1 to 5 stars by conditions as: »When the current download numbers were shown, social influence was present. And when the order of the songs followed the download numbers, this influence became even stronger. −Experimental data showed to be always larger under social influence than under the independent condition. −A heavier social influence also increased the unpredictability of a song’s popularity.

12. 12 Information cascade  On YouTube.. »If many others before us watched video, we are more likely to watch it too.  Sequential decision making

13. 13 Information cascade

14. 14 Sequential decision making

15. 15 Sequential decision making

16. 16 Sequential decision making

17. 17 Sequential decision making

18. 18 Sequential decision making  In summary »once an odd-numbered user and then an even-numbered user show the same public action in a row, the next user will just follow, no matter what her private signal is. Thus, information cascade starts. »The probability of no cascade after two people have received their private signals and made their public actions is equal to the probability that first two private signals are different. »The probability of cascade of 1’s (up cascade) and that of a cascade of 0’s (down cascade) are the same.

19. 19 Pop quiz

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22. 22 Emperor’s New Clothes Effect

23. 23 Example 1

24. 24 Example 1

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26. 26 Tipping  It has to do with the fundamental idea of positive feedback. More people adopted certain product, more people will adopt in the next stage  Each person decides to flip from one state to another based on whether her utility function is sufficiently high.  Utility function depends on p, the product adoption percentage in the rest of the population.  A probability of person adopting the product as a function of p  We have an influence function f that maps p at time slot t to p at the next time slot t+1.  Adoption percentage (p), Influence function (f(p))

27. 27 Tipping

28. 28 Tipping

29. 29 Example 2

30. 30 Pop Quiz

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