1. CS 315 – Web Search and Data Mining

2. Overview The power of crowdsourcing Predicting flu outbreaks Predicting “the present” through Google Insights! Predicting movie success! Predicting elections! Predicting elections? What can (and cannot) be predicted How (not to) predict

3. Tracking Seasonal Flu through the CDC.gov Map taken on April 18 -> Based on reports from Hospitals Takes a couple of weeks to record

4. google.org/fl utrends/us Map taken on April 18 -> Based on keywords being searched It is updated immediately Data can be downloaded, studied

5. Why does it work so well? “close relationship between how many people search for flu-related topics and how many people actually have flu symptoms”

6. Google Trends predicts flu outbreak!

7. Observing the crowd It makes sense: People search about things they want to be informed about, including flu symptoms Another example: Which day of the week there are the most queries with the term “hangover” in?

8. Observing the crowd It makes sense: People search about things they want to be informed about, including flu symptoms Another example: Which day of the week there are the most queries with the term “hangover” in? “Civil war” what do you expect to see?

9. Predicting “the future" Sample data Not identical when repeated Preserve privacy Normalized data Peak at 100% You can disambiguate Apple in computer & electronics Apple in food & drink Downloadable Must be logged in Geography Category Time window

10. 10 Autoregressive: value at time t depends on Value at time t-1 Seasonal adjustment: value at time t depends on Value at time t-12 Transfer function: value at time t depends on other contemporaneous or lagging variables Seasonal autoregressive transfer model: Value at time t depends on Value at time t-12 (seasonality)‏ Value at time t-1 (recent behavior)‏ Other lagging or contemporaneous variables (such as Google Trends data)‏ Typical question of interest How much more accurate forecasts can you get from additional variables over and above the accuracy you get with the history of the time series itself? Basic Econometrics Forecasting Models

11. Method: Fit other data as best you can, then add Trends data, improve prediction Model: Y t = 446.1 + 0.864 * Y t - 1 – 4.340 * us378.1 + 4.198 * us96.2 – 0.001 * AvgP t – 1 Y t : New house sold at t-th month AvgP t – 1 : Average Sales Price of New One-Family Houses Sold at (t-1)-th month us378.1 : Google Trend of vertical id = 378 (Rental Listings & Referrals ) at t-th month 1 st week us96.2 : Google Trend of vertical id = 96 (Real Estate Agent) at t-th month 2 nd week Analysis and Forecasting July 2008 Actual = 515K Predicted = 442.98K Z-score = 2.53 August 2008 Prediction = 417.52K

12. Google Trends “can predict the present”

13. Predicted with Google Trends Home sales Movie box-office success Product sales (e.g., video games) Travel to Hong Kong Unemployment rates …Consumer behavior, in general? (Goel paper) Is there anything that could NOT be predicted with Google Trends? Is Twitter chat volume as good?

14. Twitter Predicts Movie Box-Office Sales!

15. Movie buzz creates tweets… The rate at which movie tweets are generated can be used to build a powerful model for predicting movie box-office revenue, (better than “gold-standard” Hollywood Stock Exch.) Tweet-rate(movie) = tweets(movie)/hour Predictions (linear regression): 7-days before release data thent: #theaters playing HSX index

16. Twitter monitors Poll Sentiment (!) For more information, see “oconnor – tweets to polls AAPOR panel.ppt”

17. Smoothed (15 days) comparisons SentimentRatio(”jobs”)

18. US Presidential elections not predicted 2008 elections SR(“obama”) and SR(“mccain”) sentiment do not correlate But, “obama” and “mccain” volume: r =.79,.74 (!) Simple indicator of election news? 2009 job approval SR(“obama”): r =.72 Looks easier: simple decline

19. In the meantime, in Germany…

20. Twitter can Predict Elections (?!) For more info, see “icwsm2010_Tumasjan-Predicting elections with Twitter.pdf”

21. Not so fast, speedy… It seems that they forgot the party with the biggest tweet share…

22. Maybe Google Trends can predict US Elections…

23. Can Google Trends predict elections? 2008 US Congressional Elections Data Collection 2010 US Congressional Elections Data Collection The Competitors for Prediction:

24. US congressional elections 2008 & 2010 20082010 Total Races413441 House Races381408 Senate Races3233 Highly contested61125 Democrats237200 Republicans177241 “landslide win”DemocratsRepublicans

26. Prediction of All races (unfair to Google-trends)

27. Prediction of races where one candidate had no G-trends visibility

28. Prediction of races where both candidates had G-trends visibility

29. What about the one success case?

30. Conclusions Google Trends: bad predictor of election results Google Trends: Good Predictor of election defeat! But what about other Social Media? What do YOU think?

31. High G-trends may be bad news! Liberal activists openly collaborate to Google-bomb search results of political opponents in 2006 Conservative activists launch a Tweeter-bomb in Jan. 2010 Liberal activists try again unsuccessfully in 2010