1. Using a model of Social Dynamics to Predict Popularity of News Kristina Lerman, Tad Hogg USC Information Sciences Institute, Institute for Molecular Manufacturing WWW 2010 2010. 07. 09 Summarized and Presented by Park,Sung Eun,IDS Lab., Seoul National University

2. Copyright  2008 by CEBT Contents  Introduction  Details of Digg  The model developed in earlier works Dynamical model of social voting Model parameters and solutions  The model-based prediction of eventual popularity of newly submitted stories  Discussion Compare the results to the other prediction  Conclusion 2

3. Copyright  2008 by CEBT Introduction  Inequality of popularity A handful stories become very popular, accumulating thousands of votes, while most others can only muster a few hundred votes  Unpredictability of popularity A similar set of stories submitted to Digg on another day will end with radically different numbers of votes.  Popularity is difficult to predict, even to experts Quality – inherent feature of content Social influence – i.e., knowing about the choices of others Other factors? 3

4. Copyright  2008 by CEBT Social News Portal Digg  Life cycle of Digg User submits story to Upcoming Stories Others vote on the story If story gets many votes quickly -> promoted to Front page Friends Interface shows stories friends submitted or voted on  Digg dataset Scraping web pages in Digg’s Technology section in May and June 2006. At least 4 observations of these stories Within one hour interval 4

5. Copyright  2008 by CEBT Stochastic user model  visibility: does user see the story? user interface – browse upcoming stories – browse front page stories – recommended by friends  interest: does user like the story? 5

6. Copyright  2008 by CEBT Dynamic model of social voting  rate a story gets votes : how the number of votes a story receives changes over time. Visibility – ν f : on front page list – ν u : on upcoming list – ν friends : via friends list r : the probability a user seeing the story will vote on it. 6

7. Copyright  2008 by CEBT on Front Page and Upcoming Page P(t) is linearly increase : p=1.5 denotes half way down the first page. Threshold – h is a threshold that makes article appear in front page. – if x>0 V is a rate general users come to Digg. C is a fraction viewing upcoming pages 7

8. Copyright  2008 by CEBT Via Friends Interface  The friends interface allows the user to see the stories her friends have submitted/voted for.  w= rate a voters’ fans come to Digg  s(t) : the number of fans of voters on the story by time t who have not yet seen the story  : The average number of additional fans from an extra vote when the story has N vote votes 8 The decreasing number of users who can see the story via Friends interface The increasing number of users who can see the story via Friends interface

9. Copyright  2008 by CEBT The model : promotion to front page  Estimating r : the value that minimizes the root-mean-square(RMS) difference between the observed votes and the model predictions. 9

10. Copyright  2008 by CEBT Model Parameters and Solutions The model’s prediction of whether a story is promoted is correct for 95% stories. -0.13 correlation between S and r for promoted stories 10

11. Copyright  2008 by CEBT Model-based Prediction  Estimating story quality Estimating r from the full voting history for the promoted stories – the value that minimizes the root-mean-square(RMS) difference between the observed votes and the model predictions by the model at the end of the data sample or two days after submission, whichever was earlier 11

12. Copyright  2008 by CEBT Model-based Prediction  Predicting final number of votes Estimating r values from the early voting history of each story – After the 4 observations for each promoted story 12 87% correlation 49% correlation

13. Copyright  2008 by CEBT Model-based Prediction  Comparing to direct extrapolation Comparing Model : growth in votes is well-predicted from the number of votes shortly after promotion. – Does not consider visibility – Use this model with 4 observations and show a lower correlation of 75%  Comparing to social influence only prediction Social Influence : stories that initially receive many fan votes ultimately go on to accumulate fewer votes than stories Comparing model : Social influence-based model – A decision tree classifier : number of fan votes it received within the first 10 votes, number of submitters’ fan, and a boolean attribute indicating whether the story was successful. 10% improvement over the social influence-based model 13

14. Copyright  2008 by CEBT Discussion  Why is it important to predict final popularity with initial social dynamics? The usage? – Recommendations? – Finding interestingness and recommend from it may be more useful. 14

15. Thank you Q&A 15