New insights into telephone call dynamics
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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

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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


New insights into telephone call dynamics

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


New insights into telephone call dynamics

Examples


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


    New insights into telephone call dynamics

    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


    New insights into telephone call dynamics

    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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