Checking Out Checking In: Observations on Foursquare Usage Patterns

627b1a10da6bd579fd7f2ea8c73774b8?s=47 Matt J Williams
September 09, 2011
29

Checking Out Checking In: Observations on Foursquare Usage Patterns

Research talk.
Venue: International Workshop on Finding Patterns of Human Behaviours in Network Data and Mobility Data (NEMO), Athens, Greece.

627b1a10da6bd579fd7f2ea8c73774b8?s=128

Matt J Williams

September 09, 2011
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Transcript

  1. 1.

    Checking out checking in: Observations on Foursquare usage patterns International

    Workshop on Finding Patterns of Human Behaviours in Network Data and Mobility Data - NEMO 9 Sept 2011 Martin Chorley, Gualtiero Colombo, Matthew Williams, Stuart Allen, Roger Whitaker Cardiff University
  2. 2.

    Motivation • Areas of study: • Presence of routine (regularity)

    in mobility and encounters • Relationship between personality traits and mobility behaviour • Heterogeneity in individuals’ behaviours
  3. 3.

    Motivation • Appropriate datasets hard to find! • In addition

    to the mobility trace, we want: • social graph • profiles of individuals • properties of the places individuals visit • ...and comprehensive coverage of a geographic region!
  4. 4.

    About Foursquare • “Location-based online social network” • Users ‘check-in’

    to their current venue • Venues are user-contributed • Points, “mayorships”, and discounts to incentivise participation
  5. 9.

    Collected data Foursquare dataset Foursquare dataset Foursquare dataset Foursquare dataset

    City population Collection area # users (>= 1 visit) # venues (>= 1 visit) # checkins Cardiff 341,000 7.0 x 9.0 km 1,701 1,234 13,299 Cambridge 130,000 5.0 x 3.5 km 1,196 852 6,464 Collection period: Mon 21st March – Fri 13th May 53 continuous days
  6. 11.
  7. 13.

    User activity 1 10 100 Number of Checkins 1 10

    100 1000 Number of Users Cardiff Cambridge • Users with exactly one checkin: • Cambridge: 31% • Cardiff: 52% • Top 1% of users responsible for 15% of all checkins
  8. 14.

    Venue popularity 1 10 100 Number of Checkins 1 10

    100 Number of Venues Cardiff Cambridge
  9. 16.

    Checkins over time Cambridge Cardiff W T F S S

    M T W T F S S M T W T F S S M T W T F S S M T W T F S S M T W T F S S M T W T F S S M T W T F S S M T W T F S S M T W T F S S M T missing data
  10. 17.

    Checkins per day Checkins per day Checkins per day Weekday

    Weekend Cardiff 246.2 255.5 Cambridge 123.5 126.0
  11. 18.

    Weekday checkins: Cardiff vs Cambridge !" #!" !!'!!" !$'!!" !%'!!"

    !&'!!" !('!!" !#'!!" !)'!!" !*'!!" !+'!!" !,'!! -;<<4=4:">"?91@4/:1A4:" B; G/4.D"HID0;;/:" J J;"-.D4=;/6" L7 !" #!" $!!" $#!" !!'!!" !$'!!" !%'!!" !&'!!" !('!!" !#'!!" !)'!!" !*'!!" !+'!!" !,'!!" $!'!!" $$'!!" $%'!!" $&'!!" $('!!" $#'! -;<<4=4:">"?91@4/:1A4:" B;;0" C G/4.D"HID0;;/:" J1=7D<1K4"LM;D:" N J;"-.D4=;/6" L7;M:" P/ !" #!" $!!" $#!" %!!" %#!" !!'!!" !$'!!" !%'!!" !&'!!" !('!!" !#'!!" !)'!!" !*'!!" !+'!!" !,'!!" $!'!!" $$'!!" $%'!!" $&'!!" $('!!" $#'!!" $)'!!" $*'!!" $+'!!" $,'!!" %!'!!" %$' -;<<4=4:">"?91@4/:1A4:" B;;0" C/D:">"E9D4/D.19F49D" G/4.D"HID0;;/:" J1=7D<1K4"LM;D:" N;F4:"O"3;/5"O"HD74/:" J;"-.D4=;/6" L7;M:" P/.@4<"LM;D:" % of all checkins in a day % of all checkins in a day Cardiff Cambridge
  12. 19.

    Weekend checkins: Cardiff vs Cambridge !" #!" !!'!!" !$'!!" !%'!!"

    !&'!!" !('!!" !#'!!" !)'!!" !*'!!" !+'!!" !,'!! -;<<4=4:">"?91@4/:1A4:" B; G/4.D"HID0;;/:" J J;"-.D4=;/6" L7 !" #!" $!!" $#!" !!'!!" !$'!!" !%'!!" !&'!!" !('!!" !#'!!" !)'!!" !*'!!" !+'!!" !,'!!" $!'!!" $$'!!" $%'!!" $&'!!" $('!!" $#'! -;<<4=4:">"?91@4/:1A4:" B;;0" C G/4.D"HID0;;/:" J1=7D<1K4"LM;D:" N J;"-.D4=;/6" L7;M:" P/ !" #!" $!!" $#!" %!!" %#!" !!'!!" !$'!!" !%'!!" !&'!!" !('!!" !#'!!" !)'!!" !*'!!" !+'!!" !,'!!" $!'!!" $$'!!" $%'!!" $&'!!" $('!!" $#'!!" $)'!!" $*'!!" $+'!!" $,'!!" %!'!!" %$' -;<<4=4:">"?91@4/:1A4:" B;;0" C/D:">"E9D4/D.19F49D" G/4.D"HID0;;/:" J1=7D<1K4"LM;D:" N;F4:"O"3;/5"O"HD74/:" J;"-.D4=;/6" L7;M:" P/.@4<"LM;D:" Cardiff Cambridge % of all checkins in a day % of all checkins in a day
  13. 21.

    User similarity • We treat the fraction of checkins in

    venues of a particular category as the user’s interest in that category • Interest profile: vector of user’s interest levels for each category Coll&Uni Food Arts&Ent Outdoors Nightlife Home &Work Shops Travel User A 0.51 0.13 0.00 0.00 0.34 0.00 0.02 0.00 User B 0.57 0.05 0.10 0.00 0.20 0.00 0.03 0.05 User C 0.00 0.19 0.43 0.00 0.05 0.32 0.00 0.01 ... • Use proportional similarity metric to compute similarity between two of users’ interest profiles
  14. 23.

    The bad news... • Relies on self reporting • Users

    very W.E.I.R.D • Users have differing usage styles
  15. 24.

    Summary • Foursquare offers a rich source of data –

    location visits, social graph, venues, users • Stronger presence of routine on weekdays, but weekend checkins less structured • Friends are more similar in the types of places they visit
  16. 25.

    Ongoing and future research • Individual checkin patterns: regularity, predictability,

    heterogeneity • User co-location patterns • Relationship between personality traits and visiting behaviour
  17. 26.

    Thanks for listening! Questions? Matt Williams www.mattjw.net @voxmjw www.gplus.to/mattjw Supported

    by... { M.J.Chorley, G.Colombo, M.J.Williams, Stuart.M.Allen, R.M.Whitaker } @cs.cardiff.ac.uk www.recognition-project.eu