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Strategies and limitations in app usage
and human mobility
Marco De Nadai (http://www.marcodena.it)
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Mobility, Social interactions are conserved
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Mobility, Social interactions are conserved
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What about apps?
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Photo: Liz Hafalia, The Chronicle + NBC News
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THERE ARE 2.1M APPS IN THE
GOOGLE PLAY STORE
App usage statistics by Statista.com & hubspot.net
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PEOPLE SPEND AN INCREASINGLY
AMOUNT OF TIME ON THE PHONE […]
REACHING 3 HOURS PER DAY
THERE ARE 2.1M APPS IN THE
GOOGLE PLAY STORE
App usage statistics by Statista.com & hubspot.net
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PEOPLE SPEND AN INCREASINGLY
AMOUNT OF TIME ON THE PHONE […]
REACHING 3 HOURS PER DAY
THERE ARE 2.1M APPS IN THE
GOOGLE PLAY STORE
App usage statistics by Statista.com & hubspot.net
USER ENGAGMENT INCREASED BY 23%
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But I always use the same apps!
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ALWAYS USED
But I always use the same apps!
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ALWAYS USED
But I always use the same apps!
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ALWAYS USED
But I always use the same apps!
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ALWAYS USED
MEH... SOMETIMES
But I always use the same apps!
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ALWAYS USED
MEH... SOMETIMES
But I always use the same apps!
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ALWAYS USED
MEH... SOMETIMES
WAS THIS APP EVEN
HERE?
But I always use the same apps!
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ALWAYS USED
MEH... SOMETIMES
WAS THIS APP EVEN
HERE?
• APP USAGE CHANGES
• APPS CHANGE
But I always use the same apps!
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ALWAYS USED
MEH... SOMETIMES
WAS THIS APP EVEN
HERE?
• APP USAGE CHANGES
• APPS CHANGE
But I always use the same apps!
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Monotony
vs
Growth of app usage
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•Screen time of 90K people
•Over 8 months
•69K different apps
•12M of people’s hours spent on apps
Data
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Data (pseudo-anonymized)
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Data (pseudo-anonymized)
•Screen time
•No network-based approaches
•Unique dataset of app usage
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• !
~ ( + "
)#$#!
"
• = 1.19 ± 0.01
Characterizing App usage
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• !
~ ( + "
)#$#!
"
• = 1.19 ± 0.01
Characterizing App usage
• !
~ ()#$
• = 1.27 ± 0.01
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• !
~ ( + "
)#$#!
"
• = 1.19 ± 0.01
Characterizing App usage
• !
~ ()#$
• = 1.27 ± 0.01
• ~ %
• γ = 0.41
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• !
~ ( + "
)#$#!
"
• = 1.19 ± 0.01
Characterizing App usage
• !
~ ()#$
• = 1.27 ± 0.01
• ~ %
• γ = 0.41
• PEOPLE TIME IS FOCUSED ON FEW APPS
• PEOPLE KEEP EXPLORING NEW APPS
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The familiar apps over time: the App space
THE APP SPACE (t)
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• Apps used at least twice and for at least 10 min per week
• 20 weeks long sliding windows (1 week)
• How does the App space evolve over time?
The familiar apps over time: the App space
THE APP SPACE (t)
THE APP SPACE (t-1) THE APP SPACE (t+1)
1 WEEK 1 WEEK
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App space over time (adopted and dropped apps)
THE APP SPACE (t)
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App space over time (adopted and dropped apps)
!
THE APP SPACE (t)
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App space over time (adopted and dropped apps)
!
! = (!) = 4
THE APP SPACE (t)
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App space over time (adopted and dropped apps)
"
!
! = (!) = 4
" = (") = 5
THE APP SPACE (t)
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App space over time (adopted and dropped apps)
#
"
!
! = (!) = 4
" = (") = 5
# = (#) = 4
THE APP SPACE (t)
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App space over time (adopted and dropped apps)
!$"
=
!$"
=
"$#
=
"$# =
#
"
!
! = (!) = 4
" = (") = 5
# = (#) = 4
THE APP SPACE (t)
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• Gain: 3
= 3
− 3
()
• 97.5% of people exhibit a conserved capacity ( 4#
5$#
≤ 1)
Adoped and dropped apps
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What is the conserved capacity?
On average:
•Everytime a new app is adopted, an older app is discarded
THE APP SPACE (t)
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What is the conserved capacity?
On average:
•Everytime a new app is adopted, an older app is discarded
THE APP SPACE (t)
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What is the conserved capacity?
On average:
•Everytime a new app is adopted, an older app is discarded
THE APP SPACE (t)
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What is the conserved capacity?
On average:
•Everytime a new app is adopted, an older app is discarded
THE APP SPACE (t)
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•Local shuffle: app usage order of a user
•If the random permutated user has the same capacity => Time
•Two-sample Kolmogorov–Smirnov test
•Set p-value threshold (0.05 or 0.001?)
- Different (p-value < threshold)
- Don’t know (p-value > threshold)
Is capacity a time-constraints consequence?
!
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#
$
USER A #
$
!
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https://en.wikipedia.org/wiki/Kolmogorov%E2%80%93Smirnov_test
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•Local shuffle: app usage order of a user
No time consequence (KS: 0.55 p-value < 0.001).
•Global: app usage among users
(KS: 0.98 p-value < 0.001). It’s an individual characteristics.
Is capacity a time-constraints consequence?
!
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$
USER A #
$
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USER A !
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USER B !
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The app strategy
• EXPLORERS (% ≫ )
• KEEPERS (%
≪ )
• Explorers adopt 1 new app every 28 weeks
• Keepers always keep using the same apps
% = % / %
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The app strategy
• EXPLORERS (% ≫ )
• KEEPERS (%
≪ )
• Explorers adopt 1 new app every 28 weeks
• Keepers always keep using the same apps
% = % / %
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The app strategy
• EXPLORERS (% ≫ )
• KEEPERS (%
≪ )
• Explorers adopt 1 new app every 28 weeks
• Keepers always keep using the same apps
% = % / %
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What about age?
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What about age?
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What about age?
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What about age?
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People are focused on few apps, but keep exploring.
Individuals exhibit a conserved app capacity
•Capacity is an individual behaviour
•Capacity varies with age
•We can define app explorers and keepers
•AND MORE!
So?
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Then?
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Then?
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Then?
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Apps, Mobility, Social interactions are conserved
DUNBAR’S NUMBER OF APPS?
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Apps that stay the most
~10% of apps are always kept
~17.5% are continuosly changed
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Strategy vs age
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EXERCISEEEE
www.menti.com – Code: 2145921
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EXERCISEEEE!
shorturl.at/nwSZ8
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What did I learn?
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Individuals exhibit a conserved app capacity
•To explore dataaa
•Understand complex queries
•A bit of spark
What did I learn?
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Individuals exhibit a conserved app capacity
•To explore dataaa
•Understand complex queries
•A bit of spark
What did I learn?
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Individuals exhibit a conserved app capacity
•To explore dataaa
•Understand complex queries
•A bit of spark
What did I learn?
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Thank you!
@denadai2