AbstractMotive: Man is a social animal, who needs rewarding socialcontact and relationships to make him feel comfortable.When this need is not met, he feels isolated, leading tothoughts of not fitting in, feeling empty and isolated.Objective: This research investigates Socialoscope, asmartphone app that passively detects the loneliness insmartphone users based on the user’s day-to-day socialinteractions and smartphone activity sensed by thesmartphone’s built-in sensors.

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

feeling of being unloved” - Mother Teresa• Loneliness, is not the same as being alone. One can be

alone and very happy at the same time. Being alone, canbe experienced as a positive emotion.

• Effects of Loneliness: Increasing levels of stress,lowering self-esteem, anxiety, panic attacks, drug oralcohol addiction and depression, weakening ofimmune system, increased sleep problems, increasedblood pressure, irregular heartbeat, increased chancesof stroke and cardiovascular disease.

• Hindrances in tackling loneliness: Social stigma, lackof resources, lack of skilled therapists, misdiagnosis.

• Most susceptible population: Older adults andinternational 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 byincluding new internet and social media features.

• Explore whether smartphone sensed loneliness iscorrelated with the Big-Five personality types [2].

• Synthesize machine learning classifiers that detectlonely smartphone users, while factoring in differencesin personality types.

• Research, develop and evaluate the intelligentsmartphone app, which detects lonely users, whilefactoring in differences in personality types.

Socialoscope: Sensing User Loneliness and Its Interactions with Personality

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

Features Sensed and Tracked

Big-Five Personality Types [2]

Conclusions• Research and develop Socialoscope, an Android app that passively monitors

the social interactions of smartphone users in terms of calls, messages, socialmedia, Bluetooth and Wi-Fi devices, emails, browsing and thereby detectsloneliness levels factoring in the users’ personality type.

• Useful to old adults who face challenges in monitoring themselves and usingsmartphones.

• Useful to busy users who do not have want to invest their time and energy inmonitoring their social wellness.

• Directly impacts smartphone social health monitoring.

Analysis • Feature extraction: Smartphone features will be extracted from the sensed

data and analyzed for statistical correlations with loneliness and personalitytypes.

• Statistical analysis: The correlation coefficient and p-value of each featurewith questions from the UCLA loneliness questionnaire [1] and Big-Fivepersonality questionnaire [2] will be computed.

• Synthesize classifiers: The most correlated features will be used to buildmachine learning classifiers that can detect the level of loneliness ofsmartphone users.

• Developing the app: Machine learning classifiers will be used to develop anintelligent Android app that can detect loneliness levels based on themonitored data.

• User Acceptance: The app will be evaluated in a user study where users willbe surveyed to assess the app’s usability, acceptance and functionality.

Approach• Passively monitor various user activities.• Pilot study consisting of 20 subjects whose smartphone activity will be

automatically sensed and uploaded to Dropbox account for two weeks whileloneliness and personality questionnaires are administered simultaneously.

• One-time Big-Five personality questionnaire [2] will be filled out by subjectswhich will help determine their personality type.

• Daily prompts to declare their level of loneliness will be given to subjectsusing questions based on the UCLA Loneliness Levels [1].

References1. D Russell (1996), “UCLA Loneliness Scale (Version 3): Reliability, Validity, and Factor Structure”, in Journal of

Personality Assessment, 66(1):20-40.2. 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