Weitere ähnliche Inhalte Ähnlich wie HCI and Smartphone Data at Scale (20) Kürzlich hochgeladen (20) HCI and Smartphone Data at Scale3. ©2013CarnegieMellonUniversity:3
Smartphones are Intimate
Mobile phones and millennials (Pew 2012):
• 75% use in bed before going to sleep
• 83% sleep with their mobile phones
• 90% check first thing in the morning
• Half use them while eating
• A third use them in the bathroom (!)
• A fifth check them every ten minutes
7. ©2013CarnegieMellonUniversity:7
Three Threads of Research
• Augmented Social Graph
– Create richer computational models
of our social relationships with others
• Urban Analytics
– Create viz and models of cities
based on geotagged social media
• CrowdScanning Apps
– Crowdsourcing and other techniques
to analyze privacy behaviors of apps
10. ©2013CarnegieMellonUniversity:10
Why Better Models?
• Secure invitations
– Who is this person friending me?
• Communication triage
• Better info finding (weak ties)
• Configuration of privacy policies
– Tie strength strongly correlated with what
personal info people willing to share
(Wiese et al, Ubicomp 2011)
• Early detection of depression
– Less communication with strong ties,
less mobility, lots of fast food, insomnia
15. ©2013CarnegieMellonUniversity:15
User Study on Relationships
• 40 Participants
– 13 male and 27 female (age 19-50)
– 55% student, 35% employed, 10% unemp
• Data collection
– Phone: Contact list, call & SMS logs
– Facebook: Friend list from Facebook
backup
– Self-report for 70 contacts: Demographics,
group, closeness (1 – 5 = feel very close)
16. ©2013CarnegieMellonUniversity:16
Life Facets
• Can classify life facets
{work, social, home}
at 90.1%
– If at least one comm.
– Just contact list,
call log, SMS log
• Correlations
Min et al, Mining Smartphone Data to
Classify Life-Facets of Social
Relationships, CSCW 2013
17. ©2013CarnegieMellonUniversity:17
Ongoing Work: Tie Strength
• However, tie strength much harder to
predict, 74.6% for {low, med, high}
– We thought this would be easy…
– Other modes of communication
• Skype, IM, email, face-to-face
– Stage of relationship / maintenance comm.
18. ©2013CarnegieMellonUniversity:18
Three Threads of Research
• Augmented Social Graph
– Create richer computational models
of our social relationships with others
• Urban Analytics
– Create viz and models of cities
based on geotagged social media
• CrowdScanning Apps
– Crowdsourcing and other techniques
to analyze privacy behaviors of apps
19. ©2013CarnegieMellonUniversity:19
The Problem
• Today’s methods for gathering data about
cities are slow, expensive, and limited
– Ex. Travel Behavioral Inventory for traffic
flows every 10-20 years and 100s of people
– US Census 2010 cost $13 billion
– Quality of life surveys (sociology, city govts)
go door-to-door and interview people
• Some approaches today:
– Call Data Records, but granularity
– Deploy a custom app, but scale and utility
21. ©2013CarnegieMellonUniversity:21
Livehoods, Our First Urban
Analytics Tool
• The character of an urban area is defined
not just by the types of places found
there, but also by the people that make
it part of their daily life
Cranshaw et al, The Livehoods Project: Utilizing Social Media to
Understand the Dynamics of a City, ICWSM 2012.
24. ©2013CarnegieMellonUniversity:24
The Image of a Neighborhood
What you’re imagining probably looks a lot more like this.
Every citizen has had long associations with some
part of his city, and his image is soaked in
memories and meanings.
---Kevin Lynch, The Image of a City
50. ©2013CarnegieMellonUniversity:50
Three Threads of Research
• Augmented Social Graph
– Create richer computational models
of our social relationships with others
• Urban Analytics
– Create viz and models of cities
based on geotagged social media
• CrowdScanning Apps
– Crowdsourcing and other techniques
to analyze privacy behaviors of apps
56. ©2013CarnegieMellonUniversity:56
Privacy as Expectations
• Apply this same idea of mental models
for privacy
– Compare what people expect an app
to do vs what an app actually does
– Emphasize the biggest gaps,
misconceptions that many people had
App Behavior
(What an app
actually does)
User Expectations
(What people think
the app does)
57. ©2013CarnegieMellonUniversity:57
Crowdsourcing Privacy
• Idea 2: use crowdsourcing to do this
(crowdsource privacy)
• Few people read privacy policies
– We want to install the app
– Reading policies not part of main task
– Complexity of these policies (the pain!!!)
– Clear cost (time) for unclear benefit
• Crowdsourcing can mitigate these
problems
58. ©2013CarnegieMellonUniversity:58
10% users were surprised this app
wrote contents to their SD card.
25% users were surprised this app
sent their approximate location to
dictionary.com for searching nearby
words.
85% users were surprised this app
sent their phone’s unique ID to
mobile ads providers.
0% users were surprised this app
could control their audio settings.
See all
90% users were surprised this app
sent their precise location to
mobile ads providers.
95% users were surprised this app
sent their approximate location
to mobile ads providers.
95% users were surprised this app
sent their phone’s unique ID to
mobile ads providers.
0% users were surprised this app
can control camera flashlight.
59. ©2013CarnegieMellonUniversity:59
Our Study on App Privacy
• Showed crowd workers screenshots and
description of app (from Google Play)
– 56 of top 100 Android Apps
• Showed permissions one at a time
– Only those related to privacy
• Expectation Condition
– Why they think the app uses permission
– How comfortable they were with it
• Purpose Condition
– We gave an explanation (based on our analysis)
– Asked how comfortable they were with it
60. ©2013CarnegieMellonUniversity:60
Results for Location Data
(N=20 per app, Expectations Condition)
App Comfort Level (-2 – 2)
Maps 1.52
GasBuddy 1.47
Weather Channel 1.45
Foursquare 0.95
TuneIn Radio 0.60
Evernote 0.15
Angry Birds -0.70
Brightest Flashlight Free -1.15
Toss It -1.2
61. ©2013CarnegieMellonUniversity:61
Showing Purpose Lowers Concerns
• All differences statistically significant
• Big increases for dictionary, Shazam,
Air Control Lite, and others (> 1.0)
App Comfort w/
Purpose
Comfort w/o
Purpose
Device ID 0.47 ( =0.30) -0.10 ( =0.41)
Contact List 0.66 ( =0.22) 0.16 ( =0.54)
Network Location 0.90 ( =0.53) 0.65 ( =0.55)
GPS Location 0.72 ( =0.62) 0.35 ( =0.73)
64. ©2013CarnegieMellonUniversity:64
Thanks!
More info at cmuchimps.org
or email jasonh@cs.cmu.edu
Special thanks to:
• Army Research Office
• National Science Foundation
• Alfred P. Sloan Foundation
• DARPA
• Google
• CMU Cylab
Join our community for researchers at:
www.reddit.com/r/pervasivecomputing
68. ©2013CarnegieMellonUniversity:68
Using Location Data to
Infer Friendships
• 2.8m location sightings of
489 users of Locaccino
friend finder in Pittsburgh
• Place entropy for inferring
social quality of a place
– #unique people seen in a place
– 0.0002 x 0.0002 lat/lon grid,
~30m x 30m
Cranshaw et al, Bridging the Gap Between Physical Location and
Online Social Networks, Ubicomp 2010
70. ©2013CarnegieMellonUniversity:70
Inferring Friendships
• 67 different machine learning features
– Location diversity (and entropy)
– Intensity and Duration
– Specificity (TF-IDF)
– Graph structure (overlap in friends)
• 92% accuracy in predicting friend/not
– Location entropy improves performance
over shallow features like #co-locations
Hinweis der Redaktion Start out with a statement that probably won’t be controversial, which is that smartphones are pervasiveAbout 40% of all mobile phones sold today are smartphones, and the number is rapidly growingWhat’s also interestingare trends in how people use these smartphoneshttp://blog.sciencecreative.com/2011/03/16/the-authentic-online-marketer/http://www.generationalinsights.com/millennials-addicted-to-their-smartphones-some-suffer-nomophobia/In fact, Millennials don’t just sleep with their smartphones. 75% use them in bed before going to sleep and 90% check them again first thing in the morning. Half use them while eating and third use them in the bathroom. A third check them every half hour. Another fifth check them every ten minutes. A quarter of them check them so frequently that they lose count.http://www.androidtapp.com/how-simple-is-your-smartphone-to-use-funny-videos/Pew Research CenterAround 83 percent of those 18- to 29-year-olds sleep with their cell phones within reach. http://persquaremile.com/category/suburbia/ Smartphones intimate part of our livesLocation,call logs,SMS,pics, moreCan capture human behavior atunprecedented fidelity and scale http://www.flickr.com/photos/robby_van_moor/478725670/ We know these relationships, but computers have an overly simplified model of our relationships, usually just “friend”Can we do better? Image adapted from Real Life Social Network, by Paul Adams Picture of “Robin Sage” to understand the dynamics, structure, and character of a city If you just looked at the geography only, you might break things down as follows… http://www-958.ibm.com/software/data/cognos/manyeyes/visualizations/b2794c5a60c611e18bfd000255111976/comments/b27c2c4060c611e18bfd000255111976 DARPAGoogleCMU CyLab Intuitively, if we are co-located in a highly public place, it’s not a very strong signal 2.8m location sightings of 489 volunteers in Pittsburgh 2.8m location sightings of 489 volunteers in Pittsburgh Livehoods useful for recommender systems, e.g. not recommending things across boundaries