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Strategies and limitations in app usage
and human mobility
Marco De Nadai, Antonio Lima, Angelo Cardoso, Bruno Lepri and Nuria Oliver
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Photo: Liz Hafalia, The Chronicle + NBC News
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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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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
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)
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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
• ~ 9
• γ = 0.41
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• "
~ ( + '
))*),
-
• = 1.19 ± 0.01
Characterizing App usage
• "
~ ())*
• = 1.27 ± 0.01
• ~ 9
• 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)
<
< = (<) = 4
THE APP SPACE (t)
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App space over time (adopted and dropped apps)
B
<
< = (<) = 4
B = (B) = 5
THE APP SPACE (t)
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App space over time (adopted and dropped apps)
D
B
<
< = (<) = 4
B = (B) = 5
D = (D) = 4
THE APP SPACE (t)
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App space over time (adopted and dropped apps)
<)B
=
<)B
=
B)D
=
B)D =
D
B
<
< = (<) = 4
B = (B) = 5
D = (D) = 4
THE APP SPACE (t)
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• Gain: M
= M
− M
()
• 97.5% of people exhibit a conserved capacity ( OP
QRP
≤ 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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•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?
<
B
D
T
USER A D
T
<
B
<
B
D
T
USER A <
B
D
T
<
B
D
T
USER B <
B
D
T
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The app strategy
• EXPLORERS (M ≫ )
• KEEPERS (M
≪ )
• Explorers adopt 1 new app every 28 weeks
• Keepers always keep using the same apps
M = M / M
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What about age?
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What about age?
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What about age?
16
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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Strategies and limitations in app
usage and human mobility
https://arxiv.org/abs/1904.09350
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Apps, Mobility, Social interactions are conserved
DUNBAR’S NUMBER OF APPS?
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ANGELO CARDOSO BRUNO LEPRI
ANTONIO LIMA
Thank you!
@denadai2
NURIA OLIVER
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• Alessandretti, Laura, et al. "Evidence for a conserved quantity in human mobility."
Nature human behaviour 2.7 (2018): 485.
• Alessandretti, Laura, Sune Lehmann, and Andrea Baronchelli. "Understanding the
interplay between social and spatial behaviour." EPJ Data Science 7.1 (2018): 36.
• Pappalardo, Luca, et al. "Returners and explorers dichotomy in human mobility."
Nature communications 6 (2015): 8166.
• Miritello, Giovanna, et al. "Limited communication capacity unveils strategies for
human interaction." Scientific reports 3 (2013): 1950.
References
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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