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Cell Phone Behavior Gender Identification Study

This study explores automatic gender identification using cell phone calling behavior data. Evaluating popular approaches, the research aims to understand the association between phone usage and gender and to propose efficient classification methods. The dataset consists of 2 million calls from 10,000 phone numbers, including encrypted caller and callee numbers, call timestamps, durations, and locations. Various behavioral, social, and mobility variables are analyzed to create ranked distribution charts contrasting behavior with gender. Classification techniques such as SVM, Random Forests, and a semi-supervised approach combining K-means, labeling, and KNN are investigated.

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Cell Phone Behavior Gender Identification Study

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  1. Automatic Gender Identification using Cell Phone Calling Behavior Presented by David

  2. Motivation • Existing gender classification • Based on voice • Based on image • Violate user privacy • Purpose of this work • understanding how phone usage associated with gender id • evaluating common approaches for gender id

  3. Call Data Records • Dataset • 2 M calls • 10 K phone numbers • Features • encrypted cell phone numbers of caller and callee • the date and time of the call • the duration of the call • The initial and final location of the caller while making the call

  4. Variables • Behavioral Variables • Number of Calls • Average Duration of Calls • Expenses • Social Variables • In Degree • Out Degree • Degree • Mobility Variables • Talk Distance • Route Distance

  5. Behavior vs. Gender • Ranked distributions charts

  6. Behavior vs. Gender (Cont.) • Ranked distributions charts • Selected features • number of incoming calls • Number of outgoing calls • average duration of incoming calls • average duration of outgoing calls • Expenses • Degree

  7. Gender Classification • SVM • Random Forests

  8. Gender Classification (Cont.) • Semi-supervised (K-means + Labeling + KNN)

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