1. Gender based analysis Udayan Kumar Computer and Information Science and Engineering (CISE) Department, University Of Florida, Gainesville, FL

2. Inference of User Behavior 04/01/09 2 Problem Statement: 1. How can we classify users into groups based on gender, given publicly available traces ? 2. Can we analyze User behavior and preferences using the above classification ? Our study is based on WLAN trace analysis* We present a systematic technique to group WLAN users. As a case study we applied it to gender.

3. Information in WLAN traces 04/01/09 3 Trace we used : Number of Users ~4000 Trace Period = Feb 2006, Oct 2006, Feb 2007 Do not provide :- User information like name, gender, major. MAC (anonymized) ‏ AP associationsStart timeDuration

4.  How can we classify users in groups like major and gender? Methodology 04/01/09 4 visitors Males Females University Campus Fraternity Sorority traces  Fraternities house males  Sororites house females

5. Filtering 04/01/09 5 How can we distinguish visitors from residents? Definition: Session of visitors would be significantly less than that of regular users. This definition can be used in two ways based on Individual behavior Group behavior

6. 04/01/09 6 In this graph the “Knee” bend is a important feature We classify those below the knee as visitors and exclude them from further studies. (order of magnitude difference in sessions) ‏ Knee Feb 2006No. of Males No. of Females Before filtering777687 After filtering452463 Similar trend is seen in other trace samples as well

7. Individual behavior

8. Name based UF traces have username (UFID) UF phonebook can be searched using usernames Finally SSN office provides a list of most popular male and female baby names Can classify more than 25% of the users

9. Verification of filtering (Sororities) ‏ 04/01/09 9 Before Filtering Month(a)Month(b) ‏ # of Users(a)# of users(b) ‏ Common% users Feb2006Mar-Apr2006991115571762.08 Oct2006Nov20061264130584464.67 Feb2007Mar-Apr20071169132782161.87 After Filtering Month(a)Month(b) ‏ Female(a)Female(b) ‏ Common% Common Feb2006Mar-Apr200646347442990.51 Oct2006Nov200649345643287.63 Feb2007Mar-Apr200743945840588.43

10. Time Evolution* 04/01/09 10 *Udayan Kumar, Nikhil Yadav and Ahmed Helmy, “Analyzing Gender-gaps in Mobile Student Societies”, CRAWDAD Workshop poster 3 days4 days5 days 6 days 7 days 14 days

11. Using this info 04/01/09 11 Problem Statement: How can we classify users into groups based on gender, given publicly available traces ? Can we analyze User behavior and preferences using the above classification ? We analyze the traces with the following metrics Major (Buildings most frequented) ‏ Online activity (Session duration) ‏ Device preference (Apple or PC) ‏

12. WLAN User Distribution* 12 Engineering and economics have more males, while social science has more females. *Udayan Kumar, Nikhil Yadav and Ahmed Helmy, “Gender-based Grouping of Mobile Student Societies”, The International Workshop on Mobile Device and Urban Sensing (MODUS), IPSN 2008 Workshop

13. Average session duration 13 Are there trends in the average online times of users ? Males have shorter sessions than females. *Udayan Kumar, Nikhil Yadav and Ahmed Helmy, “Gender-based Grouping of Mobile Student Societies”, The International Workshop on Mobile Device and Urban Sensing (MODUS), IPSN 2008 Workshop

14. 14 Device preferences: Which device vendors do different genders prefer? Females seem to prefer Apple over Intel Preference Statistical significance test says with 90% confidence that there is bias within genders for brands/vendors *Udayan Kumar, Nikhil Yadav and Ahmed Helmy, “Gender-based Grouping of Mobile Student Societies”, The International Workshop on Mobile Device and Urban Sensing (MODUS), IPSN 2008 Workshop

15. What can we do with it ? (Applications) ‏ 04/01/09 15 Profiling : identify gender, preference, major for a user Social Science: extent of WLAN adoption amongst genders can point out socio-economic and socio-cultural gaps between genders. Directed Ads: If an advertiser of male product want to target its users, he can find from the traces regions of campus frequented by men. Application Customization: Based on preference of the genders Designing network protocols for DTN (Delay Tolerant Networks)‏

16. What now ? ( Future Work ) ‏ 04/01/09 16

17. Visualization: all females 04/01/09 17

18. All females + Law school 04/01/09 18

19. All females+Law school+Apple Comp. 04/01/09 19