New insights into telephone call dynamics

New insights into telephone call dynamics

Analysis of call record data from the BT Home Online studyDavid K Hunter, School of CSEE

Ben Anderson, Department of SociologyAlexei Vernitski, Department of Mathematical Sciences

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

Transactional data in sociology

Examples

A 21st Century Sociology?

New empirical resources

• 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?• ?

DataWe have data for 400 households, collected by

BT between 1998 and 2001For each household, we have records of their

incoming and outgoing calls:Caller’s and the callee’s ID (anonymised telephone

numbers)The time the call was madeThe length of the callAnd some other data (tariffs, ISP calls, etc)

We also have demographic data for many of the households, although we have not used this yet

DataInterestingly, our

data is not network data

We are looking at isolated fragments of the network of telephone connections

These are called “ego networks”

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This area is a useful niche for developing researchAt the interface between teletraffic theory and social

network analysisMore appropriate for our ego network data

Existing software would not help muchEntire 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 analysisIn general, call dynamics, considering timing, length,

interrelationship and correlation between callsCould be integrated into stata or R

Timing and interrelationship of calls

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Grapevine calls are made in response to a telephone call that has been receivedMade to pass on information or to get more

informationBatch calls are a collection of calls made at one

sittingOften done intentionally, to make arrangements with

several people, or to pass on newsMaking a single call can prompt more calls to be

made, even if it was not originally intendedOther reasons: take advantage of cheap rate,

boredom, loneliness

Grapevine calls and batch calls

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The software is presently configured to discard any calls which:Overlap in time with the previous oneAre to an Internet Service Provider (ISP)Have zero costAre shorter than 5 secondsAre to or from telephone numbers shorter than 8 digitsAre between two different panel householdsAre between two numbers of the same panel householdAre to the same number – loopback within the same household

1,274,916 of the original 1,590,092 calls remainIt identifies “call groups” – two or more calls where each new

call begins less than 120 seconds after the previous one endsGrapevine calls occur when the first call in a group is

incoming, but the remainder outgoingBatch calls occur when all the calls in a group are outgoing

Call groups

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1,274,916 calls held in RAM – 887,019 single calls1,045,027 call groups – 158,008 groups of two or more calls

887,019 groups of 1 call – 416,718 grapevine and 470,301 batch116,107 groups of 2 calls – 19,937 grapevine and 68,792 batch27,079 groups of 3 calls – 3,470 grapevine and 15,913 batch8,699 groups of 4 calls – 819 grapevine and 5,167 batch3,153 groups of 5 calls – 242 grapevine and 1,858 batch1,301 groups of 6 calls – 96 grapevine and 787 batch653 groups of 7 calls – 35 grapevine and 418 batch389 groups of 8 calls – 19 grapevine and 241 batch211 groups of 9 calls – 8 grapevine and 137 batch112 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

Classification of call groups

Markov chain Each call group is identified by a string of one or more ‘O’s or ‘I’s followed 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 seonds elapse without another call arriving, it goes back into the

null state, “”

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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…

Markov chain states and transitions

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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 …..

Conditional transition frequencies

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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 datasetSpecific ideas for further work followIn particular, demographic data can also be considered

The software is presently a standalone C programHowever it could be developed into functions for R or

stataThe functionalities in the current software can be

combined and developed furtherSophisticated analysis of call dynamics will be possible

Current status and future directions

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Link the analysis to the demographic dataAre 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 etcMay be able to detect home businesses or

teenagersIt might not be feasible, but it’s worth investigatingWe could test the output from our method against

our existing demographic data (hypothesis testing)

Ideas for future topics

Further future topicsOn 22 occasions, one or other subscriber made and

received 200 or more calls in a dayThis could be investigated in more detail, for example, day

of week and timesExplanations could be sought for this behaviour

Calls to ISP, or cold callingDemographic data would indicate if particular types of

households exhibit this behaviourDevelop 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

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People living alone are a special caseParticularly when subscriber is physically isolated from friendsIt’s virtually certain who is making and receiving each call

Possible exception is visitorsUse of mobile phone records would also solve this anonymity problemEffect of age and gender on calling patterns would be tied to specific

individualsWould probably have to be aware of sample bias

Relatively small fraction of customersEven smaller proportion of calls between two one-person households

For example, could compare calling frequencies and durations between gendersCompare with findings by Friebel (Greece and Italy), and by Smoreda

(France)Friebel found that women make fewer but longer mobile calls on

average

One-person households

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Corroborate and extend existing results from Lacohee and AndersonThis existing study is based on self-report data (time-

use diaries)Effect of occupation on calling times

Some occupations require shift workingEffect of having children in the household on call

distribution in eveningHouseholds with children use the phone less from

17:00 to 20:00 than those without childrenThe reverse is true from 21:00 to 23:00Generate more extensive set of results from dataset

and consider influence of other demographic factors

Effect of life rhythms

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Zbigniew Smoreda, 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.

Bibliography