Gender based analysis

Udayan Kumar

Computer and Information Science and Engineering (CISE) Department, University Of Florida, Gainesville, FL

Inference of User Behavior

04/01/092

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.

Information in WLAN traces

04/01/093

Trace we used :Number of Users ~4000Trace Period = Feb 2006, Oct 2006, Feb

2007

Do not provide :- User information like name, gender, major.

MAC (anonymized)

AP associations

Start time Duration

How can we classify users in groups like major and gender?

Methodology

04/01/094

visitorsvisitors

MalesFemales

University Campus

FraternitySorority

tracestraces

Fraternities house males

Sororites house females

Filtering

04/01/095

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 onIndividual behaviorGroup behavior

Group behavior

04/01/096

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 2006 No. of Males

No. of Females

Before filtering

777 687

After filtering

452 463

Similar trend is seen in other trace samples as well

Individual behavior

Name based UF traces have username (UFID)UF phonebook can be searched using

usernamesFinally SSN office provides a list of most

popular male and female baby names Can classify more than 25% of the users

Verification of filtering (Sororities)

04/01/099

Before Filtering

Month(a)

Month(b) # of Users(a)

# of users(b)

Common % users

Feb2006

Mar-Apr2006

991 1155 717 62.08

Oct2006

Nov2006 1264 1305 844 64.67

Feb2007

Mar-Apr2007

1169 1327 821 61.87

After Filtering

Month(a)

Month(b) Female(a) Female(b) Common % CommonFeb200

6Mar-Apr2006

463 474 429 90.51

Oct2006

Nov2006 493 456 432 87.63

Feb2007

Mar-Apr2007

439 458 405 88.43

Time Evolution*

04/01/0910

*Udayan Kumar, Nikhil Yadav and Ahmed Helmy, “Analyzing Gender-gaps in Mobile Student Societies”, CRAWDAD Workshop poster

3 days 4 days 5 days

6 days 7 days 14 days

Using this info

04/01/0911

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)

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

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

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

What can we do with it ? (Applications)

04/01/0915

• 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)

What now ? (Future Work)

04/01/0916

Visualization: all females

04/01/0917

All females + Law school

04/01/0918

All females+Law school+Apple Comp.

04/01/0919