Loneliness and social isolation can have a serious impact on oneâ€™s mental health, leading to increased stress, lower self-esteem, panic attacks, and drug or alcohol addictions. Older adults and international students are disproportionately affected by loneliness. This thesis investigates Socialoscope, a smartphone app that passively detects loneliness in smartphone users based on the userâ€™s day-to-day social interactions, communication and smartphone activity sensed by the smartphoneâ€™s built-in sensors. Statistical analysis is used to determine smartphone features most correlated with loneliness. A previously established relationship between loneliness and personality type is explored. The most correlated features are used to synthesize machine learning classifiers that infer loneliness levels from smartphone sensor features with an accuracy of 90%. These classifiers can be used to make the Socialoscope an intelligent loneliness sensing Android app. The results show that, of the five Big-Five Personality Traits, emotional stability and extraversion personality traits are strongly correlated with the sensor features such as number of messages, number of outgoing calls, number of late night browser searches, number of long incoming or outgoing calls and number of auto-joined trusted Wi-Fi SSIDs. Moreover, the classifier accuracy while classifying loneliness levels is significantly improved to 98% by taking these personality traits into consideration. Socialoscope can be integrated into the healthcare system as an early warning indicator of patients requiring intervention or utilized for personal self-reflection.
Worcester Polytechnic Institute
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Pulekar, Gauri Anil, "Socialoscope: Sensing User Loneliness and Its Interactions with Personality Traits" (2016). Masters Theses (All Theses, All Years). 394.
mobile sensing, sensors, loneliness, personality, social isolation, healthcare