Socialoscope GRIE Finals Poster
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Poster for Socialoscope GRIE finals
![Results
11111
Our Approach
Abstract
This 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.
References
1. 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
Hypothesis
Loneliness can be inferred from communications and proximity
with people one feels connected to.](https://image.slidesharecdn.com/socialoscopegriegauripulekarfinals-160411132117/85/Socialoscope-GRIE-Finals-Poster-1-320.jpg)
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Socialoscope GRIE Finals Poster
- 1. Results 11111 Our Approach Abstract This 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. References 1. 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 Hypothesis Loneliness can be inferred from communications and proximity with people one feels connected to.