socialoscope grie finals poster

Results

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Our Approach

AbstractThis research investigates Socialoscope, a smartphone app that passively detects loneliness in smartphone users based on the user’s day-to-day social interactions and smartphone activity sensed by the smartphone’s built-in sensors.

Background• “The most terrible poverty is loneliness, and the feeling

of being unloved” - Mother Teresa• Effects of Loneliness: Increasing levels of stress, anxiety,

panic attacks, drug or alcohol addiction and depression.• Hindrances in Tackling Loneliness: Social stigma, lack of

resources, lack of skilled therapists, misdiagnosis.• Susceptible Groups: Old adults, international students.[3]

Key Contributions• Correlation of smartphone features with questions from

the clinically validated UCLA loneliness scale. [1]

• Extend the list of features explored by prior work on smartphone loneliness and personality sensing.

• Explore whether smartphone sensed loneliness is correlated with the Big-Five personality traits. [2]

• Synthesize machine learning classifiers. • Research, develop and evaluate the intelligent

smartphone app, which detects lonely users, while factoring in differences in personality types.

Socialoscope: Mobile Sensing User Loneliness and Its Interactions with Personality

Gauri Pulekar and Prof. Emmanuel Agu (Advisor)Computer Science Dept., Worcester Polytechnic Institute

Features Tracked

Big-Five Personality Traits[2]

Data Gathering• Pilot study consisting of 9 subjects for two weeks.• Android app using Funf-in-a-Box was distributed which automatically sensed smartphone

activity and uploaded it to Dropbox account.• Loneliness and personality questionnaires were administered simultaneously.

Analysis • Based on Correlation based Feature Selection (CFS).• Correlation coefficient and p-value of each feature with UCLA

loneliness score[1] and Big-Five personality scores[2] is computed. • The most correlated features are used to build machine learning

classifiers that can detect the level of loneliness of smartphone users.

References1. D Russel, “UCLA Loneliness Scale (Version 3): Reliability, Validity, and Factor Structure”, in Journal of Personality Assessment.2. G Chittaranjan, J BlomDaniel, and Gatica-Perez (2011), “Who’s Who with Big-Five: Analyzing and Classifying Personality Traits

with Smartphones”, in Proc ISWC 2011, Washington, DC, USA.3. A Ong, B Uchino and E Wethington, “Loneliness and the health of older people” in Gerontology. 4. “Funf Sensing Framework”, https://code.google.com/p/funf-open-sensing-framework/source/checkout

HypothesisLoneliness can be inferred from communications and proximity with people one feels connected to.