Decomposing the Social Heartbeat of Norway Rich Ling
Kenth Engø-Monsen
IT University of Copenhagen & Telenor Group
Research and Future Studies Telenor Group
[email protected]
[email protected]
Data
Overview We report on the natural activity profile — the heartbeat — of the aggregated customer behavior during the day and during a week of the collective behavior from a random sample of mobile telephony customers. We decompose the “heartbeat” by the friend-rank measure, where communication partners are ranked according to volume (seconds) or number of voice calls, SMS, or MMS. Another way to decompose the “heartbeat” is by considering the gender of the originating and the terminating party of the communication link.
1) The “heartbeat” – the daily, reference profile
We studied a large, random, and anonymous sample (many tens of thousands) of users in Norway. In order to extract the daily “heartbeat”-profiles, data over a period of three months is used.
2) Breaking down the “heartbeat” on relationships The “heartbeat” broken down by Friend rank (e.g. link strength)
Number of Minutes
Note the extremely repetitive nature to the daily use of voice telephony
Minutes
Monday Tuesday Wednesday Thursday Friday Saturday Sunday
00
01
02
03
04
05
06
07
08
09
10
11
12
13
14
15
16
17
18
19
20
21
22
0
23
2
4
6
8 10 12 14 16 18 20 22 0
2
4
6
8 10 12 14 16 18 20 22 0
4 Thursday
Time of day
6
8 10 12 14 16 18 20 22 0
2
4
3
8 10 12 14 16 18 20 22 7 Sunday
6 Saturday 2
6
4
4) Weak vs. strong ties
Definition used: Strong ties = Friend rank 1 -> 5, and weak ties = Friend rank > 5
Number of Minutes
Number of Minutes
More male to male interaction in the mornings and female to female in the afternoon and on the weekends
4
5 Friday 1
3) Gender differences
2
0
2
4
6
00 02 04 06 08 10 12 14 16 18 20 22 00 02 04 06 08 10 12 14 16 18 20 22 00 02 04 06 08 10 12 14 16 18 20 22 00 02 04 06 08 10 12 14 16 18 20 22 4 Thursday
6 Saturday
5 Friday M-M
M-F
F-M
8 10 12 14 16 18 20 22 0
2
4
4
8 10 12 14 16 18 20 22 0
2
4
6
5
Thursday
7 Sunday
6
8 10 12 14 16 18 20 22 0
2
4
6 Saturday
Friday
F-F
Strong
6
8 10 12 14 16 18 20 22 7 Sunday
Weak
6) Zipf's law patterns in communication
5) Age segments
Is time used to talk with ties as a Zipf distribution?
Adults with children are the most “active” users of mobile voice
6 Senior - 1 Children 3 Young adult - 1 Children 4 Adult w children - 1 Children 5 Adult - 1 Children 2 Youth - 1 Children 1 Children - 1 Children
Minutes measured in percentage of stronges tie (Friend rank = 1)
Number of Minutes
100,0 %
50,0 %
Zipf's law 25,0 %
Whole Week Monday Tuesday Wednesday Thursday
12,5 %
Friday Saturday Sunday
6,3 %
3,1 % 0 3 7 9 11 13 15 17 19 21 23 6 8 10 12 14 16 18 20 22 0 2 4 6 8 10 12 14 16 18 20 22 0 2 4 7 9 11 13 15 17 19 21 23 4 Thursday
5 Friday
6 Saturday
7 Sunday
There is a regularity to our daily mobile voice interaction. On Fridays there is a blending of job and social calls, that it is in that condition that we are closest to the distribution suggested by Zipf's law. By contrast, when we have the most partitioned telephonic interaction, i.e. on Sunday when there is largely social telephonic interaction , i.e. talking with family and friends, then we are the furthest in from the distribution. Thus, Zipf's law, to the degree that it applies, speaks to the multi-dimensional interaction and not to the partitioned interaction that is common within for example the social sphere.
1
2
3
4
5
6
7
8
9
10
Tie/Relationship order (Friend rank)
Another issue is that Zipf’s law is derived from the idea that an individual will seek the most convenient method in developing speech acts (Zipf, 1949) or seeking information (Poole, 1987). The phenomena has to do with individual decision making. The difference between the findings of Zipf and Poole and those noted here may have to do with the reciprocal nature of mobile communication whereby two parties (i.e. two strong ties) are mutually seeking to reduce their own effort in the pursuit of social interaction or eventually commercial activity (as evidenced in the contrast between the profiles of weekday vs. weekend traffic).
