This document presents the objectives and methodology of the Socialoscope project. The project aims to develop a smartphone application that can passively detect user loneliness levels based on social interactions sensed from the phone. It explores using machine learning models to correlate phone sensor data with scores from validated loneliness and personality scales. The author conducted a pilot study collecting phone data and survey responses from participants. Statistical analysis identified several phone usage features significantly correlated with loneliness. Future work includes implementing feedback and tracking features in the Socialoscope app to help users and mental health professionals monitor loneliness levels.

1. Socialoscope
Sensing User Loneliness and Its Interactions with
Personality Types
Gauri Pulekar
Advisor: Prof. Emmanuel O Agu

2. Loneliness And The Hype Around It!
 Man is a social animal
 Rewarding social
contact and
relationships [33]
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Effects of Loneliness:
 Increasing levels of stress
 Anxiety, panic attacks
 Drug or alcohol addiction
 Depression

3. Why Is Loneliness Increasing At An Alarming Rate? [32]
 Increasing rates of
divorce
 Early deaths that leave
the significant other
alone
 Longer working hours
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 Older adults
 Loss of a spouse, loss of friends
 Distant family members, health
issues and age
 International students
 Distant family members, culture
shock

4. How Is Loneliness Tackled Today? How Efficient Are These
Methods? [32, 34]
Psychotherapy:
 Use of psychological
methods based on regular
personal interaction to help a
person change and overcome
problems
 Types: Group, Artistic,
Cognitive-behavioral
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Reasons for inefficiency:
 Reach less than half of those
afflicted with loneliness
worldwide
 Social stigma associated with
mental disorders
 Lack of resources
 Lack of skilled therapists
 Misdiagnosis

5. Thesis Objective
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6. Objective Of Socialoscope
 Mobile app that passively detects loneliness in smartphone users based on the
user’s social interactions sensed by the smartphone’s built-in sensors
 Phone calls, text messages, Wi-Fi, Bluetooth, web browsing and app usage
 Are certain personality types more prone to loneliness?
 Potentially large impact of ubiquitous ownership of smartphones
 Cost effective
 Global reach
 Feedback to users, help in tracking activity, therapist monitoring
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7. Hypothesis [23]
Decrease
in the
number
of calls
and
messages
‘I have
nobody
to talk to’
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High usage
of social
media, and
low usage of
calls and
messages
‘My social
relationship
s are
superficial’ Reduced
calls and
messages
incoming
from
contacts
marked as
favorite
‘I feel shut
out and
excluded by
others’
Loneliness is generally related to:
- Communications with people you feel connected with
- Proximity with people you feel connected with

8. Goal Of Socialoscope
1. Investigate what sensed smartphone features are statistically correlated with
loneliness questions on the clinically validated UCLA loneliness scale
2. Extend the list of features explored by prior work on smartphone loneliness
and personality sensing
3. Explore whether smartphone sensed loneliness is correlated with the Big-Five
personality types
4. Synthesize machine learning classifiers
5. Research, develop and evaluate the intelligent Socialoscope smartphone
application
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9. Background
&
Related Work
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10. Related Work: Sociometer & SociableSense
Sociometer: [19,20]
 Wearable IR based device
 Creates models of human
communication networks to
identity leaders and connections
 Communicates only with another
individual wearing the same
device
 Power consumption, limited
distance and obstacle hindrances
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SociableSense: [22]
 Detects the sociability levels,
strength of relations with
colleagues
 Uses Accelerometer, Bluetooth,
microphone

11. Related Work: StudentLife, Vive [25], Chittaranjan et al [28]
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StudentLife: [23]
 Correlates sensor data from smartphones
with mental wellbeing and academic
performance
 Performs activity detection, conversation
detection and sleep detection
 Based on UCLA loneliness scale
Vive: [25]
 Detects loneliness levels in older adults
 Gives encouragement messages to boost
morale
Chittaranjan et al: [28]
 Detects relationship between smartphone
usage and self-perceived personality type
 Based on Big-Five personality traits

12. Our Approach
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13. Our Approach
Build data gathering tools
Run study to gather data
Analyze collected data
Train machine learning classifiers
Use best classifiers to build machine learning app
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14. Mobile App
 Automatically monitor various user
activities through smartphone sensors,
communication and interactions of
users
April 4, 2016 Worcester Polytechnic Institute 14For illustration purposes

15. Feature List
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Data Type Measured By What it measures
Phone Calls
Call count If user has any phone communication channel
Call type If the user is the one calling or receiving calls, or is
trying to avoid calls
Call from/to If user has any phone communication with favorite
contacts
Call duration If user has any prolonged phone communications,
or keeps them to the minimum

16. Feature List
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Data
Type
Measured By
SMS SMS count
SMS character count
SMS from/to
SMS type
App No of launches
App duration
App category
Emails Number of emails
Data Type Measured By
Bluetooth No of unique BT IDs
No of times saved BT IDs are seen
Duration of availability
Wi-Fi No of SSIDs
Duration of SSIS connectivity
Type: Public/Home/Work
Browser Browser favorites
Browsing time of day
Browsing duration

17. Pilot Study
 Android app automatically sensing and
recording smartphone data
 Using Funf in a Box [26]
 Implemented social media usage,
starred contacts, browser usage, app
usage
 Uploaded to a Dropbox account daily
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18. Pilot Study
 2 weeks
 SONA Participant Pool, Publicity,
StudentLife, MechTurk
 Target: 30+ users, Current: 9
Demographics:
 6 males, 3 females
 9 international students
 9 graduate students
 Age range: 23 – 28 years
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19. Personality Detection
 A one-time personality detection survey
 Based on Big-Five Personality Traits [25]
 Extraversion, Agreeableness, Conscientiousness, Neuroticism, Openness to experience
 Provided using WPI Qualtrics
 50 questions
 I am the life of the party
 I feel comfortable around people
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20. Big-Five Personality Traits [25]
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21. Loneliness Detection
 A daily user survey which would take user’s input on his projection of his
loneliness levels
 Based on the UCLA Loneliness Levels [21]
 Scale to measure one’s subjective feelings of loneliness and social isolation
 Provided using WPI Qualtrics
 20 questions
 How often do you feel that you lack companionship?
 How often do you feel outgoing and friendly?
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22. UCLA Loneliness Scale - Version 3 [21]
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23. Analysis
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24. Analysis Steps
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Pre-
Processing
Compute
loneliness
scores,
personality
scores,
decryption
Feature
extraction
Features are
extracted from
the sensed
data, averages
and moving
averages are
computed.
Statistical
analysis
• Correlation
based feature
selection
• Good feature
subsets contain
features highly
correlated with
classification,
yet uncorrelated
with each other.
[37]
Synthesize
machine
learning
classifiers
• Most
correlated
features
• Weka Machine
Learning
library
• Various types
of classifiers
are compared
Develop the
Socialoscope
Intelligent
Smartphone
app
Synthesized
classifiers are
added to an
Android sensing
app
Message log, Call
log, Bluetooth,
Contacts, Browser
Usage

25. Statistical Analysis
Inferential Statistics
Feature
Values
Personality
Scores
Loneliness
Scores
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 Correlation based feature selection (CFS)
 Evaluates subsets of features on the basis of
the following hypothesis:
"Good feature subsets contain features
highly correlated with the classification, yet
uncorrelated to each other” [37]

26. Statistical Analysis
Correlation
Coefficient
Standard
Error of
Correlation
Coefficient
T-Score
Degree Of
Freedom
P-Value Results CSV
Filter and
Sort
Correlated
Features
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27. Analysis Steps
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Pre-
Processing
Compute
loneliness
scores,
personality
scores,
decryption
Feature
extraction
Features are
extracted from
the sensed
data, averages
and moving
averages are
computed.
Statistical
analysis
• Correlation
based feature
selection
• Good feature
subsets contain
features highly
correlated with
classification,
yet uncorrelated
with each other.
[37]
Synthesize
machine
learning
classifiers
• Most
correlated
features
• Weka Machine
Learning
library
• Various types
of classifiers
are compared
Develop the
Socialoscope
Intelligent
Smartphone
app
Synthesized
classifiers are
added to an
Android sensing
app
Message log, Call
log, Bluetooth,
Contacts, Browser
Usage

28. Results
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29. Results
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30. Results
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31. Results
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32. Results
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33. Results
Feature Correlation
Coefficient
Standard error of
correlation
coefficient
T-score p-Value Significance at
p < 0.05
Number of calls -0.626 0.07003 -8.939 < 0.00001 Significant
Number of messages -0.793 0.05468 -14.5025 < 0.00001 Significant
Number of browser searches 0.471 0.079 5.9620 < 0.00001 Significant
Number of auto-joined Wi-Fi
SSIDS
-0.3087 0.08541 -3.6146 0.00437 Significant
Percentage of missed calls 0.3262 0.08489 3.84262 0.000193 Significant
Difference in outgoing and
incoming messages
0.3384 0.084504 -4.00454 0.000107 Significant
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34. Research Progress
&
Future Work
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35. Research Progress
Build data gathering tools
Run study to gather data: SONA, Publicity, MechTurk,
StudentLife
Analyze collected data: Modules made to analyze data from all
sources
Train machine learning classifiers
Use best classifiers to build machine learning app
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36. App Implementation
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37. Future Work
 Feedback to users
 Encouragement messages
 Tracking
 Feedback to close ones
 Parents of international students
 Children to old adults
 Therapist, psychologists, psychiatrists
 Tracking activity
 Helps to backtrack during consultation
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38. Conclusion
 Loneliness increases at an alarming rate
 Proposed Socialoscope, a smartphone app that passively monitors users’ social
activity and loneliness
 Explore previously discovered relationships between personality and loneliness
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39. References
1. N. D. Lane, M. Mohammod, M Lin, X Yang, H Lu, S Ali, A Doryab, E Berke, T Choudhury, A T. Campbell, “BeWell: A Smartphone Application to Monitor, Model and Promote
Wellbeing", in Proc. Pervasive Health Conference 2011, May 2011, pp. 23-26.
2. H Lu, W Pan, N D. Lane, T Choudhury and A T. Campbell, “SoundSense: Scalable Sound Sensing for People-Centric Applications on Mobile Phones”. In Proc MobiSys 2009
(2009), 165-178.
3. N. Eagle, A. Pentland, and D. Lazer, "Inferring Social Network Structure Using Mobile Phone Data”, in Proc National Academy of Sciences (PNAS) Vol. 106(36), pp. 15274-
15278.
4. M C Gonzalez, C Hidalgo and A Barabasi (2008), “Understanding individual human mobility patterns”, Nature 453 (7196), 779-782.
5. D. Olguin, P. Gloor, and A. Pentland (2009), “Capturing Individual and Group Behavior Using Wearable Sensors”, in Proc AAAI Spring Symposium on Human Behavior
Modeling, Palo Alto.
6. T. Abdelzaher, Y. Anokwa, P. Boda, J. Burke, D. Estrin, L. Guibas, A. Kansal, S. Madden, and J. Reich (2007), “Mobiscopes for human spaces”, in IEEE Pervasive Computing, pp
20-29.
7. S. Avancha, A. Baxi, and D. Kotz (2012), “Privacy in mobile technology for personal healthcare”, ACM Computing Surveys.
8. N. Christakis and J. Fowler (2007), “The spread of obesity in a large social network over 32 years”, New England Journal of Medicine, 357(4):370.
9. N. Christakis and J. Fowler (2008), “The collective dynamics of smoking in a large social network”, New England Journal of Medicine, 358(21):2249.’
10. J. Fowler and N. Christakis, “Dynamic Spread of Happiness in a Large Social Network: longitudinal analysis over 20 years” in Framingham Heart Study British Medical Journal.
April 4, 2016 Worcester Polytechnic Institute 39

40. References
11. K. George, D.G. Blazer, D.C. Hughes, and N. Fowler (1989), “Social Support and the Outcome of Major Depression”, in British Journal of Psychiatry, vol. 154. No. 4, pp 478.
12. George Forman (2003), “An extensive empirical study of feature selection metrics for text classification”, Journal of Machine Learning Research 3, 1289-1305.
13. “AudioRecord Android Media Recording”, http://developer.android.com/reference/android/media/AudioRecord.html
14. M Hojat, Loneliness as a function of selected personality variables, Journal of Clinical Psychology, Volume 38, Issue 1, pages 137–141, January 1982.
15. “Android Service Component”, http://developer.android.com/guide/ components/services.html
16. T Choudhury, A Pentland (2004), “Characterizing Social Networks using the Sociometer”, in Proc NAACOS 2004.
17. T Choudhury, A Pentland (2002), “The Sociometer: A Wearable Device for Understanding Human Networks”, In Proc CSCW 2012.
18. A Ghose, C Bhaumik and T Chakravarty (2013), “BlueEye - A system for Proximity Detection Using Bluetooth on Mobile Phones”, in Proc UbiComp 2013.
19. K Rachuri, C Mascolo, M Musolesi, P Rentfrow (2011), “SocialableSense: Exploring the Trade-Off ofAdaptive Sampling and Computation Offloading for Social Sensing”, in Proc
MobiCom 2011.
20. R Wang, F Chen, Z Chen, T Li, G Harari, S Tignor, X Zhou, D Ben-Zeev, and A T. Campbell (2014), “StudentLife: Assessing Mental Health, Academic Performance and Behavioural
Trends of College Students using Smartphones”, in Proc Ubicomp 2014.
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41. References
21. D Russell (1996), “UCLA Loneliness Scale (Version 3): Reliability, Validity, and Factor Structure”, in Journal of Personality Assessment, 66(1):20-40.
22. A Ong, B Uchino and E Wethington, “Loneliness and the health of older people” in Gerontology.
23. “Determining if one is a social butterfly”, Unpublished manuscript.
24. W Lane, C Manner, “The Impact of Personality Traits on Smartphone Ownership and Use”, Int’l Journal Business and Social Science, Vol. 2 No. 17.
25. 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.
26. “Funf Sensing Framework”, https://code.google.com/p/funf-open-sensing-framework/source/checkout
27. Ohmage open mobile data collection platform, http://ohmage.org
28. “5 Medical Technologies Revolutionizing Healthcare”by Forbes (2013), http://www.forbes.com/sites/stevenkotler/2013/12/19/5-medical-
technologies-revolutionizing-healthcare/2
29. “Top 10 Medical Gadgets” by Technology Personalized” (2012), http://techpp.com/2012/03/26/top-medical-gadgets
30. D Howard, Effect of Temperature on the Intracellular Growth of Histoplasma Capsulatum, J Bacteriol. 1967, Jan; 93 (1): 438-444.
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42. References
31. “Depression Toolkit”, by University of Michigan Depression Center http://www.depressiontoolkit.org/aboutyourdiagnosis/depression.asp
32. “The Loneliness of American Society” by The American Spectator, http://spectator.org/articles/59230/loneliness-american-society
33. “Campaign to End Loneliness”, http://www.campaigntoendloneliness.org
34. “Mind for Better Mental Health”, http://www.mind.org.uk/information-support/tips-for-everyday-living/loneliness/about-loneliness
35. “Psychologist Anywhere Anytime”, "http://www.psychologistanywhereanytime.com/relationships_psychologist/psychologist_loneliness.htm
36. “Thought Catalog”, http://thoughtcatalog.com/lorenzo-jensen-iii/2015/03/36-absolutely-heartbreaking-quotes-about-loneliness
37. “Feature Selection” by Wikipedia, https://en.wikipedia.org/wiki/Feature_selection
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43. Questions?
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44. Thank you.
April 4, 2016 Worcester Polytechnic Institute 44

45. Pilot Study - Security Aspects
 First level of encryption encrypts all the uploaded data, and will be decrypted by
the investigators.
 Second level of encryption is a one-way hash that cannot be decrypted by
the investigators
 Private data like message text, website URL, message text, calling number, etc.
 All the personal level information is hidden
 Thus, we will have details of how many calls you made, but now which contact or number you
called
 Anonymized using random user ids
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