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New insights into telephone call dynamics. Analysis of call record data from the BT Home Online study David K Hunter, School of CSEE Ben Anderson, Department of Sociology Alexei Vernitski , Department of Mathematical Sciences. Transactional data in sociology. Transactional data:

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new insights into telephone call dynamics

New insights into telephone call dynamics

Analysis of call record data from the BT Home Online study

David K Hunter, School of CSEE

Ben Anderson, Department of Sociology

Alexei Vernitski, Department of Mathematical Sciences

slide2

Transactional data in sociology

  • Transactional data:
    • Generated by everyday life
    • Automatically captured as part of \'business as usual\'
    • N = millions
      • Billions of data points
  • Literature commentary:
    • Surveillance, Computer Science
    • Social Science
      • Savage & Burrows, 2007
      • doi:10.1177/0038038507080443
      • 101 citations (Google Scholar)
      • http://www.youtube.com/watch?v=ARLARDwLJhw
a 21 st century sociology
A 21st Century Sociology?
  • Re-assessing old questions
      • Networks, place, space and social relationships (capital)
      • Consumption, leisure and class?
      • Public performance of self?
  • Imagining new questions?
      • Software & social stratification?
      • ?

New empirical resources

slide5
Data
  • We have data for 400 households, collected by BT between 1998 and 2001
  • For each household, we have records of their incoming and outgoing calls:
    • Caller’s and the callee’s ID (anonymisedtelephone numbers)
    • The time the call was made
    • The length of the call
    • And some other data (tariffs, ISP calls, etc)
  • We also have demographic data for many of the households, although we have not used this yet
slide6
Data
  • Interestingly, our data is not network data
  • We are looking at isolated fragments of the network of telephone connections
  • These are called “ego networks”
timing and interrelationship of calls
Timing and interrelationship of calls
  • This area is a useful niche for developing research
    • At the interface between teletraffic theory and social network analysis
    • More appropriate for our ego network data
  • Existing software would not help much
  • Entire dataset (400 ego networks) read into RAM
    • Storage format in RAM is tailored to our dataset and to the general analysis of call dynamics
  • Library of C functions is being developed with general applicability to this kind of analysis
    • In general, call dynamics, considering timing, length, interrelationship and correlation between calls
    • Could be integrated into stataor R
grapevine calls and batch calls
Grapevine calls and batch calls
  • Grapevine calls are made in response to a telephone call that has been received
    • Made to pass on information or to get more information
  • Batch calls are a collection of calls made at one sitting
    • Often done intentionally, to make arrangements with several people, or to pass on news
    • Making a single call can prompt more calls to be made, even if it was not originally intended
    • Other reasons: take advantage of cheap rate, boredom, loneliness
call groups
Call groups
  • The software is presently configured to discard any calls which:
    • Overlap in time with the previous one
    • Are to an Internet Service Provider (ISP)
    • Have zero cost
    • Are shorter than 5 seconds
    • Are to or from telephone numbers shorter than 8 digits
    • Are between two different panel households
    • Are between two numbers of the same panel household
    • Are to the same number – loopback within the same household
  • 1,274,916 of the original 1,590,092 calls remain
  • It identifies “call groups” – two or more calls where each new call begins less than 120 seconds after the previous one ends
  • Grapevine calls occur when the first call in a group is incoming, but the remainder outgoing
  • Batch calls occur when all the calls in a group are outgoing
classification of call groups
Classification of call groups
  • 1,274,916 calls held in RAM – 887,019 single calls
  • 1,045,027 call groups – 158,008 groups of two or more calls
  • 887,019 groups of 1 call – 416,718 grapevine and 470,301 batch
  • 116,107 groups of 2 calls – 19,937 grapevine and 68,792 batch
  • 27,079 groups of 3 calls – 3,470 grapevine and 15,913 batch
  • 8,699 groups of 4 calls –819 grapevine and 5,167 batch
  • 3,153 groups of 5 calls –242 grapevine and 1,858 batch
  • 1,301 groups of 6 calls –96 grapevine and 787 batch
  • 653 groups of 7 calls –35 grapevine and 418 batch
  • 389 groups of 8 calls –19 grapevine and 241 batch
  • 211 groups of 9 calls –8 grapevine and 137 batch
  • 112 groups of 10 calls –6 grapevine and 63 batch
  • … and so on …
  • Group of 301 calls – repeated calls to 0845 756 000, an unlisted ISP number
markov chain
Markov chain
  • Each call group is identified by a string of one or more ‘O’s or ‘I’sfollowed by a ‘G’
    • An incoming call followed by two outgoing calls = “IOOG”
  • Each state (other than the null state) is identified by a string of one or more characters which is called the identifier
    • Each character is either ‘I’ or ‘O’
  • The null state “” is entered when the subscriber is idle for more than 120 seconds
    • It has two possible outgoing transitions – into state “I” or state “O”
  • Every other state (represented by a string S) has three possible outgoing transitions:
    • To the null state
    • To state S+”I”
    • To state S+”O”
  • Call group of “IOOG”:
    • The Markov chain starts off in the null state, “”
    • After the first call arrives, it goes into state “I”.
    • When the second call arrives, it goes into state “IO”
    • When the third call arrives, it goes into state “IOO”
    • When more than 120 seondselapse without another call arriving, it goes back into the null state, “”
markov chain states and transitions
Markov chain states and transitions
  • 1058 states and 1,444 distinct transitions in the Markov chain
  • state \'\' (1,045,027 calls): freq out = 585519, in = 459508, gap = 0
  • state \'O\' (585,519 calls): freq out = 98358, in = 16860, gap = 470301
  • state \'I\' (459,508 calls): freq out = 26326, in = 16464, gap = 416718
  • state \'OO\' (98,358 calls): freq out = 26259, in = 3307, gap = 68792
  • state \'OI\' (168,60 calls): freq out = 2371, in = 915, gap = 13574
  • state \'IO\' (26,326 calls): freq out = 5055, in = 1334, gap = 19937
  • state \'II\' (16,464 calls): freq out = 1321, in = 1339, gap = 13804
  • state \'OOO\' (26,259 calls): freq out = 9449, in = 897, gap = 15913
  • state \'OOI\' (3,307 calls): freq out = 618, in = 223, gap = 2466
  • state \'OIO\' (2,371 calls): freq out = 600, in = 192, gap = 1579
  • state \'OII\' (915 calls, total 57571.96 sec): freq out = 136, in = 88, gap = 691
  • state \'IOO\' (5,055 calls): freq out = 1342, in = 243, gap = 3470
  • state \'IOI\' (1,334 calls): freq out = 220, in = 110, gap = 1004
  • state \'IIO\' (1,321 calls): freq out = 276, in = 101, gap = 944
  • state \'III\' (1,339 calls): freq out = 105, in = 222, gap = 1012
  • and so on…
conditional transition frequencies
Conditional transition frequencies
  • 1058 states ending \'\' (2319943 calls): freq out = 771231, in = 503685, gap = 1045027
  • …..
  • 573 states ending \'O\' (771231 calls): freq out = 153845, in = 23929, gap = 593457
  • 484 states ending \'I\' (503685 calls): freq out = 31867, in = 20248, gap = 451570
  • 1 state ending \'O\' and of length 1 (585519 calls): freq out = 98358, in = 16860, gap = 470301
  • 1 state ending \'I\' and of length 1 (459508 calls): freq out = 26326, in = 16464, gap = 416718
  • 2 states ending \'O\' and of length 2 (124684 calls): freq out = 31314, in = 4641, gap = 88729
  • 2 states ending \'I\' and of length 2 (33324 calls): freq out = 3692, in = 2254, gap = 27378
  • …..
  • 472 states ending \'OO\' (153845 calls): freq out = 48990, in = 5274, gap = 99581
  • 108 states ending \'OI\' (23929 calls): freq out = 3821, in = 1437, gap = 18671
  • 100 states ending \'IO\' (31867 calls): freq out = 6497, in = 1795, gap = 23575
  • 375 states ending \'II\' (20248 calls): freq out = 1720, in = 2347, gap = 16181
  • 1 state ending \'OO\' and of length 2 (98358 calls): freq out = 26259, in = 3307, gap = 68792
  • 1 state ending \'OI\' and of length 2 (16860 calls): freq out = 2371, in = 915, gap = 13574
  • 1 state ending \'IO\' and of length 2 (26326 calls): freq out = 5055, in = 1334, gap = 19937
  • 1 state ending \'II\' and of length 2 (16464 calls): freq out = 1321, in = 1339, gap = 13804
  • …..
current status and future directions
Current status and future directions
  • The work thus far is a proof-of-principle investigation of what is possible
  • It has only scratched the surface of what can be done with the dataset
    • Specific ideas for further work follow
    • In particular, demographic data can also be considered
  • The software is presently a standalone C program
    • However it could be developed into functions for R or stata
  • The functionalities in the current software can be combined and developed further
    • Sophisticated analysis of call dynamics will be possible
ideas for future topics
Ideas for future topics
  • Link the analysis to the demographic data
    • Are people living alone more likely to make batch calls or grapevine calls?
    • How do age and gender of the household inhabitants affect the dynamics of calling patterns?
  • Determine whether it’s possible to devise a useful method to estimate the number of people in a house, or even age, gender etc
    • May be able to detect home businesses or teenagers
    • It might not be feasible, but it’s worth investigating
    • We could test the output from our method against our existing demographic data (hypothesis testing)
further future topics
Further future topics
  • On 22 occasions, one or other subscriber made and received 200 or more calls in a day
    • This could be investigated in more detail, for example, day of week and times
    • Explanations could be sought for this behaviour
      • Calls to ISP, or cold calling
    • Demographic data would indicate if particular types of households exhibit this behaviour
  • Develop more sophisticated Markov chain model which considers whether same phone number occurs more than once in a call group
  • Study the dynamics of call timings and duration over a long period between two specific numbers
one person households
One-person households
  • People living alone are a special case
    • Particularly when subscriber is physically isolated from friends
    • It’s virtually certain who is making and receiving each call
      • Possible exception is visitors
    • Use of mobile phone records would also solve this anonymity problem
    • Effect of age and gender on calling patterns would be tied to specific individuals
  • Would probably have to be aware of sample bias
    • Relatively small fraction of customers
    • Even smaller proportion of calls between two one-person households
  • For example, could compare calling frequencies and durations between genders
    • Compare with findings by Friebel (Greece and Italy), and by Smoreda (France)
    • Friebel found that women make fewer but longer mobile calls on average
effect of life rhythms
Effect of life rhythms
  • Corroborate and extend existing results from Lacohee and Anderson
    • This existing study is based on self-report data (time-use diaries)
  • Effect of occupation on calling times
    • Some occupations require shift working
  • Effect of having children in the household on call distribution in evening
    • Households with children use the phone less from 17:00 to 20:00 than those without children
    • The reverse is true from 21:00 to 23:00
    • Generate more extensive set of results from dataset and consider influence of other demographic factors
bibliography
Bibliography
  • ZbigniewSmoreda, Christian Licoppe, “Gender-Specific Use of the Domestic Telephone”, Social Psychology Quarterly, vol 63, no 3, 2000, pp238-252.
  • Hazel Lacohee,Ben Anderson, “Interacting with the Telephone”, Journal of Human Computer Studies, vol 54, no 5, May 2001, pp665-699.
  • Guido Friebel, Paul Seabright, “Do Women Have Longer Conversations? Telephone Evidence of Gendered Communication Strategies”, Journal of Economic Psychology, vol 32, 2011, pp348-356.
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