Socialoscope PEDS talk

SocialoscopeSensing User Loneliness and Its Interactions with Personality Types

Gauri PulekarAdvisor: Prof. Emmanuel O Agu

Worcester Polytechnic Institute 2

Loneliness And The Hype Around It!

Man is a social animal

Rewarding social contact and relationships [33]

April 4, 2016

Effects of Loneliness: Increasing levels of stress

Anxiety, panic attacks

Drug or alcohol addiction

Depression


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


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


Thesis Objective

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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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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 relationships 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


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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Background & Related Work

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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


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


Our Approach

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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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Mobile App

Automatically monitor various user activities through smartphone sensors, communication and interactions of users

April 4, 2016 For illustration purposes


Feature List

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Data Type Measured By What it measures

Phone Calls

Call count If user has any phone communication channelCall type If the user is the one calling or receiving calls, or is

trying to avoid callsCall from/to If user has any phone communication with favorite

contactsCall duration If user has any prolonged phone communications,

or keeps them to the minimum


Feature List

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Data Type

Measured By

SMS SMS count SMS character count SMS from/toSMS 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/WorkBrowser Browser favorites

Browsing time of day Browsing duration


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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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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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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Big-Five Personality Traits [25]

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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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UCLA Loneliness Scale - Version 3 [21]

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Analysis

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Analysis Steps

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Pre-ProcessingCompute loneliness scores, personality scores, decryption

Feature extractionFeatures 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 appMessage log, Call log, Bluetooth, Contacts, Browser Usage


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]


Statistical Analysis

Correlation

Coefficient

Standard Error of

Correlation

Coefficient

T-Score Degree Of Freedom P-Value Results

CSVFilter and

SortCorrelated Features

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Analysis Steps

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Pre-ProcessingCompute loneliness scores, personality scores, decryption

Feature extractionFeatures 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 appMessage log, Call log, Bluetooth, Contacts, Browser Usage


Results

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Results

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Results

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Results

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Results

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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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Research Progress&Future Work

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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

April 4, 2016 Worcester Polytechnic Institute 35


App Implementation

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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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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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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.

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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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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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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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Questions?

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Thank you.

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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

April 4, 2016