1. YOU ARE WHAT YOU EAT (AND DRINK): IDENTIFYING CULTURAL BOUNDARIES BY ANALYZING FOOD AND DRINK HABITS IN FOURSQUARE Presenter: LEUNG Pak Him

2. METHODS USED TO ANALYZE CROSS- CULTURAL DIFFERENCES Traditional method Surveys New method in this paper Foursquare check-ins

3. PROCEDURES 1) Map food and drink related check-ins2) Identify particular individual preferences 3) Show how to analyze this information assess the cultural distance 4) Apply a simple k-means clustering technique to draw boundaries

4. CULTURAL BOUNDARIES Homophily Social Influence Cultural Boundaries

5. TRADICTIONAL METHOD CONSTRAINTS

6. BIGGEST CHALLENGE IN THE ANALYSIS Problem: No appropriate empirical data to use Problem: No appropriate empirical data to use Solution: data collected from questionnaires filled during face-to-face interviews Solution: data collected from questionnaires filled during face-to-face interviews

7. CONSTRAINTS IN USING SURVEY DATA 1) costly and do not scale up 2) provide only static information

8. NEW METHOD

9. REQUIREMENTS FOR USING NEW METHOD 1) Associate a user to its location 2) Extract a finite set of preferences 3) Map users’ actions into the preferences

10. MAPPING PREFERENCES

11. DATA DESCRIPTION Eight main venue categories Eight main venue categories Sub-categories Sub-categories Spans a single week of April 2012 Spans a single week of April 2012 Grouped relevant subcategories into three classes

12. FREQUENCY OF CHECK-INS OF THE THREE ANALYZED CLASSES ClassDrinkFast FoodSlow Food Check-ins279,650410,592394,042 Unique venue106,152193,541198,565 Unique users162,891230,846231,651 No. of subcategories212753

13. MAPPING FOURSQUARE DATA INTO USER PREFERENCES m =101 features m =101 features F = a vector of 101 attributes with binary representation F = a vector of 101 attributes with binary representation Finite set of preferences Finite set of preferences Map users’ action Map users’ action Associate a user with a location Associate a user with a location

14. CULTURAL SIMILARITIES

15. EXAMPLE NETWORKS IN THE PAPER

16. ANALYSIS OF THE EXAMPLE NETWORKS % of people satisfying “s” +1 : people living in the same region tend to be similar -1 : people living in the same region tend to be different

17. SPATIAL CORRELATIONS Goal : Define a set of features that are able to characterize the cultural preferences of a given geographical area Goal : Define a set of features that are able to characterize the cultural preferences of a given geographical area 3) Calculate Pearson’s correlation for different area vectors

18. CORRELATION MATRICES BETWEEN COUNTRIES

19. CORRELATION MATRICES BETWEEN CITIES

20. WITHIN BORDER ANALYSIS

21. CORRELATION MATRICES

22. TEMPORAL ANALYSIS 1) Count the number of check-ins per hour2) Group days into weekdays and weekends3) Normalize the combined number

23. RESULT - 1

24. IDENTIFYING CULTURAL BOUNDARIES

25. CLUSTERING REGIONS 2) Apply the Principal Component Analysis3) Apply k-means algorithm

26. RESULT

27. Q & A