Unobtrusive Social Network Data Through Email

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outlook archived iles nd tores ha t ata nto n SQL-

database.

Communication via mail an e ivided nto wo

types f relationships: he human-computer nteraction;

an d he omputer-computer nteraction. eople re

usually os t amiliar ith he uman-computer

interaction, where they sit at a computer, write an email,

an d push send ; or they login to their email account an d

read messages ontained n the inbox . he omputer-

computer nteraction, s ctually n utomated xchange between wo omputers, ften ith everal ther

computers erving s ntermediaries n he elivery

process. A message en t rom ne omputer s eceived

by he arget omputer(s), n ts lectronic orm, via client oftware program that ultimately opies he mail

message from its host server. The email message is stored

on esignated entral erver ntil he eceiver picks

up he message rom he erver. hi s rocess s he

electronic ersion of picking p ackage t he os t

office. The lectronic mail an be elivered to he post office repository fo r yo u to physically pick up , or directly

to your ersonal mail ox or ou o pick p. Once target omputer icks p he essage, he uman- computer nteraction llows he human to ead, rint, r

store he lectronic essage ia heir mail lient

software.

There are everal different ways n which mail ca n be elivered hrough he omputer-computer nteraction

in he orld-wide lectronic mail rchitecture. he

message can be delivered to the equivalent of post office lobby-box, alled n MAP erver. he mail an e

delivered o ersonal mailbox, alled ost ffice Protocol POP) erver. he mail an lso e outed through a Microsoft Exchange (MSEx) server. here are

many echnical ifferences between hese mail ervers,

bu t heir purpose s he ame. owever, he rincipal

difference between n MAP nd POP mail erver s

the torage eature of the erver. An MAP erver will

allow ou or your mail lient oftware) o ersist, or

store, your email physically on that server. A PO P server only serves as a temporary holding station fo r a message

that is removed once it ha s been retrieved by your email client oftware. n SEx erver s icrosoft proprietary ystem ha t s widely se d hroughout he

DoD. While it ha s some additional security features, it is more ifficult o xtract mail etwork ata rom his

system ecause f he ropriety ata ormat ha t

Microsoft institutes. n IMAP server is designed to store

the essage ve n fter he mail as een nitially

retrieved. t hould be noted that the POP protocol alls

for an email to be removed from the incoming mail bo x

once t as een etrieved, owever om e oftware

extensions o llow or ead-only ccess o he POP inbox, esulting n he message emaining n he nbox

when etrieved nd s herefore managed by he lient

software level. The popular Yahoo mail service

implements this feature fo r paying customers.

Once a target computer receives an email, the human- computer interaction involves the computer displaying the

message sing lient oftware. mail messages t he computer-computer nteraction evel re os t ften

formatted in a world-wide standard format called MBOX. MBOX llows or ifferent mail lient oftware

programs o ccess he mail rom he erver without

confusion. Th e MBOX ormat pecifies wo ections of the mail, he header section an d the body section. he

header ection ncludes he rom:, o: , C: , CC:, Subject:, nd ate:, nformation. he ody ection

contains he message ex t nd ny ttachments o he

email.

The MSEx erver oe s ot tore messages n he

MBOX format. icrosoft's proprietary tandards reate

technical nd icensing urdles n ccessing mail ata

directly rom the erver n ny manner ther than using Microsoft oftware. Unfortunately, he MSEx ormat s widely se d hroughout oD , aking mail ata

extraction more difficult. here are three approaches that we have discovered for extracting email content from an

MSEx ormat. ne pproach s ustom lient-side

visual asic atch McCulloh, t. l. , 007). nother

client-side pproach nvolves sing NetMap, which s plug-in fo r Microsoft Excel 2007 that extracts email data from proprietary .pst ile nto n Excel ormat. he

data an hen e manipulated r aved o ther ile

formats. he third approach involves parsing header data

from erver og ile. hese pproaches ill e

discussed in more detail in this paper. nalysis of dyad counts ill e se d o ompare he erformance f client-side ata ollection ith entralized ata collection.

3. METHOD This study involves monitoring the email traffic of 24

mid-career Army officers in a one-year graduate program

administered jointly by Columbia University an d the U.S. Military cademy USMA). ac h f he fficers

participating ere sked o ign onsent orm n

accordance ith he nstitutional eview oard IRB),

approved y he SMA uman ubjects esearch Review oard llowing heir ata o e ollected or

research purposes.

As part of this study, the participants permitted us to place a custom developed program (McCulloh et al , 2007) that works in conjunction with their MSEx Outlook email

accounts. This program llowed us o ollect mail ata

from the sent items folder found on participants' personal

computers. he nformation ncluded ll f he eader

information associated with an email. W e did no t view or



include the body of th e email in the study. We were also able o ollect imilar mail header nformation irectly

from he og iles maintained y he Directorate Of Information Management DOIM). he ata ollected

from the custom program is referred to as the Client-Side

Method, while data collected from the DOIM log files is

referred o s he entralized ethod. e id ot

investigate NetMap s n pproach s t as dentical underlying mail ata-sourcing apabilites nd

functionality only with a different, al beit a more elegant,

user interface. he email data collected from all methods was analyzed using a dynamic network analysis approach

(Carley, 2003).

3 1 Client Side Method A lient ide Visual Basic or Applications VBA)

program was installed on the personal computers (PC) of all participants, n the session window of their Microsoft

Outlook. Details of this data collection scheme to include

the visual basic code are outlined in detail by McCulloh, et al. (2007). t is designed to overcome the difficulty in pulling information from a subject's sent mail folder in a

proprietary Outlook Exchange system. This patch is easy

to mplement n Visual Basic nd works harmoniously with Microsoft Outlook. The principal nvestigator could

then compile the data from all participants into on e master

file an d ensure anonymity of the names.

On e of the chief advantages in managing a client-side

patch s he ow-level ontrol n athering ata. researcher oe s ot have o btain ermissions rom network administrator to collect email data. hey merely

need the consent of the monitored individuals, who must login o heir Outlook or he lient-side atch o e

installed. urthermore, he rogram esigners an pick an d choose which data to import from the local client. If,

for example, we wanted to include message content, then

that could have been an option. W e could have also just as

easily gathered incoming email traffic, as opposed to only monitoring utgoing mail. hi s ould rovide urther

insight into reas uc h as whether a user classifies mail

as junk mail, whether they delete n ncoming message, or even if they flag a particular message as important.

Managing he ata ollection rom he ndividual

participant required minimal effort. Once fully developed an d nstalled, he isual asic atch s ittle o o

overhead on the part of the user to manage. Furthermore,

these articipants elt more omfortable nowing ha t

they av e om e egree f ontrol n ow he ata s

collected. While his ould mpede he ata ollection

process, the subjects felt more comfortable knowing what

was actually monitoring their email. nitially, most of the

participants' mail were en t to ther students r people

affiliated with heir raduate rogram. ithin wo o three weeks, the participants began to email family

members an d friends. W e suppose that this represents an

increased level of trust. n the beginning, participants felt

that their email needed to appear strictly business related.

Gradually, s he y ncrementally en t ersonal mail

messages while they were at work without an y negative

consequences, they began to feel comfortable an d appear

to av e eturned nto ormal adence f mail

communication. os t of the participants ne w ow o remove he atch when heir participation n he project

ended. everal articipants aid hey el t ore

comfortable nowing ha t he oftware ending he principal investigator information was on their computer,

an d that Big Brother was not pulling their information

from somewhere else.

3 2 Centralized Method As n lternative method, we eveloped oftware

application which nalyzes mail ata athered irectly

from a centralized mail xchange erver. This oftware

gathered ata ve r ive month eriod nd xtracted those email messages which were sent an d received from the participants in this study. he server log files contain the mail eader nformation. hi s nformation as

parsed into the same format as the client-side method.

With this method of data collection, he participants

were ot ware of the precise im e ha t he ollection

process tarted. hey did provide consent in accordance

with the RB, owever, we were ot required o nform them f he xact ate he n ollection ould egin.

There as o ignificant bservable hange n he

participants' pattern of communication. he entralized

method was completely unobtrusive.

3 3 Dyad Analysis It was not clear at the beginning of this investigation

whether mail ommunication within omogenous

group of people would appear random, if it would remain relatively consistent from week to week, or if there were

identifiable factors ha t would ffect changes n network

structure. To investigate the structure of the network, we

computed the dyad count. he dyad count, defined as the communication between tw o nodes (Wasserman, & Faust,

1994) istinguishes hree ifferent ypes f communication:

symmetric,

utual,

nd

ull.

n

n asymmetric dyad, on e node talks to another, bu t does no t

receive a response. This type of communication could be

an example of a group that ha s members who are sending

ou t nformation. mutual ya d ignifies wo odes communicating ith ach ther. his yp e f

communication might occur n group ha t ollaborates

equally, r ne n which ubordinates erify or larify

directives. inally, ull ya d ccurs when wo odes which re art f he etwork o ot ave ny communication ctivity. n ya d ount, we onduct



census nd abulate he umber f ull, mutual, nd

asymmetric yads. ith 4 embers n ur tudy

comprising network, here xists 76 ombinations of

possible undirected pairs. ach of the 276 dyads could be

either null, mutual, r asymmetric. he dyad counts re

compared or data collected with the lient-side method,

centralized method, nd with alendar f ignificant

events.

4. RESULTS

There were ignificant ifferences n he lient-side

an d centralized methods of data collection. he data from

both methods as oded s meta-network Carley, 2002). onsidering ha t he articipants re andom

sample f mid-career Army fficers ha t ll ulfill he same ole f tudent n he rganization, e ight

hypothesize ha t he mail elationships ormed n he network are random. Given that there are 24 nodes in the

network, here xist 24 3 = 52 ossible yads. e can test the hypothesis:

H0: Graph ~ Binomial(552, 0.5)

HA: Graph ̂ Binomial(552, 0.5),

using the test statistic z =( / - E(/)) / Sqrt( V(/) ), where /

is the number of directed links in the graph. hi s reduces

to z = / - 276 ) / 11.75, where / is the su m of the mutual

an d asymmetric dyad counts. Under the null hypothesis,

this number follows standard normal distribution. he

p-value was ignificant at the 0.05 evel fo r most weeks,

providing vidence o eject he ypothesis ha t mail

communication atterns re andom inomial ith probability arameter f .5 . eek ith corresponding p-value that was no t significant at the 0.05

level ca n be identified in Table by the 95% confidence

interval on the Binomial parameter/? that includes 0.5.

A onfidence nterval n he robability f communication an e onstructed or ac h eek

according to the expression given by ,

P±z„ PII552

where ̂

is

he

maximum

ikelihood

stimate

f

he unknown arameter n he ssumed inomial

distribution nd qual o 52. able hows he

mutual, asymmetric, an d null dyad counts recorded using

the lient-side nd entralized methods. he ight most

column of Table hows the 95 % confidence interval on the random probability of communication. confidence

interval that spans .5 will correspond to a significant p-

value in the random binomial hypothesis test above. or

each week in Table , tw o values re hown fo r each of

the ya d ounts: Mutual, symmetric, nd Null. The

numbers n the top of each cell in Table orrespond to

the lient-side ata ollection method. he umbers n

the ottom f ach ell n able orrespond o he

centralized data collection method. he data presented in

Table orresponds to the time period beginning with the first week of the pring emester nd nding with he

week efore pring reak. he tudents oo k heir

comprehensive xa m ollowing pring reak nd he n

began to transition to their military duties t West Point.

Therefore, his ata epresents easonable im e eriod

for comparison of the client-side an d centralized methods of data collection.

Table 1 . Recorded directed links using client-side an d

central methods. Week Mutual Asymmetric N u l l C o n f i d e n c e

1 3 J a n 2 8 0

5 4

44

8 9

232

1 3 3

0 . 0 6 , 0 . 1 0

0 . 2 2 , 0 . 3 0

2 0 J a n 2 8 6

2 8

8 8

8 3

1 8 2

0

0 . 1 4 , 0 . 2 0

0 . 5 0 , 0 . 5 9

2 7 J a n 2 8 0

1 1 8

7 8

9 2

1 9 8

6 6

0 . 1 1 , 0 . 1 7

0 . 3 4 , 0 . 4 2

3 Feb 2 8 8

2 2

1 6 2

8 1

1 0 6

0

0 . 2 7 , 0 . 3 5

0 . 4 7 , 0 . 5 5

Feb2 8 0

1 1 2

1 4 8

1 0 0

1 2 8

6 4

0 . 2 3 , 0 . 3 1

0 . 3 4 , 0 . 4 2

1 7 Feb 2 8 6

23

1 1 4

7 9

1 5 6

0

0 . 1 8 , 0 . 2 5

0 . 5 2 , 0 . 6 0

24 Feb 2 8 26

2 4

1 0 8

9 2

1 4 2

0

0 . 2 1 , 0 . 2 8

0 . 4 9 , 0 . 5 8

2 Ma r 2 8 8 4

32

1 9 2

5 1

0

0

0 . 4 6 , 0 . 5 4

0 . 5 8 , 0 . 6 6

9 Ma r 2 8 26

2 4

1 4 3

7 3

1 0 7

0

0 . 2 7 , 0 . 3 4

0 . 4 6 , 0 . 5 4

Client-side dyad counts are above central dyad counts.

It ca n be

een in the Confidence

olumn of Table

that here s tatistically ignificant ifference n he probability of communication between the client-side an d

central ata ollection ethods or ll eeks, y

observing ha t he 5% onfidence ntervals o ot

overlap. n ll ases, he lient-side ethodunderestimates he probability of communication n he

network. he general pattern of the probability parameter

is correlated at a value of 0.69, which is low considering

they re stimates n he am e roup f ndividuals

during he am e week. he lient-side ata ollection

method is therefore biased.

The ya d ount nalysis an rovide dditional

insight nto he rganizational ynamics f he participants by comparing their probability of interaction

to ignificant vents n heir cademic alendar. e

restrict ur nvestigation o ata ollected sing he

centralized method ince it s omplete. he entralized

method aptures ll ata en t r eceived hrough he

central erver. he maximum likelihood stimate of the parameter, , n he inomial istribution f yads s

plotted over time an d displayed in Figure 1 .



Maximum Likelihood Estimate of inomial Parameter

Figure 1 . ML E of parameter » using centralized method.

Th e lowest ML E of p s hown in the irst week of

the emester, when he participants were just eturning

from Christmas leave. hi s was followed by an increase

in ommunication s he roup begins to plan or group

academic ssignments, arpooling, nd ther

administrative ssues. he ow points n the ML E of p

occur uring he weeks of 27 anuary nd 0 February

when major roup cademic rojects r resentations were ue . his s onsistent with he indings f

McCulloh, et. l. 2007) who observed a similar decrease in mail ommunication uring imes of group ctivity.

They hypothesized that during times of increased face-to-

face ommunication, people ommunicate verbally nd have es s im e nd eed or mail ommunication.

Furthermore, uring hese imes f ncreased ubgroup

activity, people have es s im e o write nd espond o

emails rom ndividuals utside f heir mmediate

subgroup. ollowing he roup ssignments ue uring the week of 10 February, the next major academic vent

was the comprehensive exam following Spring break.

A similar dyad analysis or the client-side method is

shown n igure . he haracteristic ip n mail communication orresponding o roup ctivity s ot

clear. areful eview f the participants' cademic calendar oes ot eveal ny ctivities r vents hat

would explain the behavior of the plot in Figure 2. hi s

further suggests the importance of centralized email data collection.

Maximum Likelihood Estimate of inomial Parameter

Figure 2. ML E of parameter » using client-side method.

Th e client-side method of ata collection is not completely without merit. It ca n still be seen in Figure 2

that the first week ha s the lowest ML E ofp. here is also a dip in the plot for the group assignment for the week of

27 January. he identification of the week of 10 February

is missed owever. hi s uggests hat ven lient-side

data ca n provide some insight into group behavior. his

may e n ppropriate method o se when omplete

centralized data is unavailable. entralized data may be

unavailable or easons of security, rivacy, amage, r

other technical difficulties. n these situations, the client-

side method may till provide valuable nformation n social network behavior.

5 CONCLUSION We found that the primary advantage to utilizing a server-

side method to gather data is the improved data integrity. Every user with n mail ccount must oth end nd

receive ata rom hat ccount's ssociated mail erver.

Therefore, o nsure that ll data s gathered t must be

collected t ts ource. ll ata ontained within he

centralized server is available for collection, such as from, to, cc, bcc, subject, time of receipt at the mail server, etc. Copying data directly rom he erver llows he ocial

network nalyst o ccurately tudy ll mail

communications within tudy group or those utilizing their given email address.

Implementing erver ased pplication lso

precludes he ubjects nvolved n he tudy rom corrupting an d inserting bias into the data. With a client-

side application, users had the ability to turn off, remove or disrupt the xecution of the program used to monitor

email. With a server-side collection technique, the clients

are completely unaware or knowledgeable about when or what s ollected. W e ound hat while t akes more

overhead o nitiate he etrieval of email raffic rom mail erver, here s urprisingly ittle overhead n he

part f erver dministrator o ctually ssist he

research ffort n athering ata. ince og iles re

typically tored in ommon location on the erver, the

administrator need only make these files available. When

operated cross network, he/she an asily op y these log iles o ommon ocation rom where he erver-

based data collection program ca n import the data.

By resenting wo ethods or athering nd

analyzing email data, we have shown both advantages an d disadvantages or he ocial etwork nalyst. hese

strengths an d limitations must be considered by an y social network analyst when studying email traffic. Even though

gathering ata t ts ource oes rovide etter ata

integrity, uch ata ollection means re ot lways

feasible. n hese ases, mail ata ollected n decentralized manner ca n still provide insightful analysis of the underlying social network.



We advise a practitioner to be highly sensitive to the

privacy mplications f his rocess, specially n he

public nd rivate ectors. eople within he military

typically o ot maintain he xpectation f mail nd

internet rivacy. his ay ot e rue n ther

populations. are ust lso e xercised ith

interpreting he esults of these ypes of social networks.

It is important that trained social network analysts provide

proper interpretation of the organizational behavior, while

respecting the privacy of individual dentities. evealing

the position an individual maintains n the social network

of an organization may lead to an overall decrease in trust

and dversely ffect he eadership limate within he

organization. hen sed roperly, owever, ocial

network nalysis an rovide ealth f aluable

information o he rganization. everal ommands

within he rmy ave lready mplemented ocial

network data collection from email. hese methods have

been utomated n he oftware package ORA, which s

maintained by CASOS at Carnegie Mellon University and

can be freely downloaded by the military and academia.

Future esearch n his rea will ikely xplore he

impact of cellular phone communication and blackberries

on ocial etworks within he military. his ine f

research will urther upport he fficacy f Netcentric

Operations within he Army. ocused esearch nto he

usage f cell phones, lackberries, -mail, nd ace-to-

face ommunication uring major roup ctivities will

provide greater insight into social network data collection.

Understanding the desired channels of communication for

military eaders, may ignificantly ontribute o haping

the communication echnologies hat he DoD nvests n.

This ine of research may also provide data or real-time monitoring of organizational change. t will certainly be

valuable in enhancing command and control systems used

by the military.

ACKNOWLEDGEMENTS This research is part of the IkeNet project in the U.S. Military Academy

Network Science Center an d the Dynamics Networks project in CASOS

(Center for Computational Analysis of Social an d Organizational Systems, http://www.casos.cs.cmu.edu) at Carnegie Mellon University. This work wa s supported in part by:

• Th e Army Research Institute for th e Behavioral an d Social Sciences, Army Project No . 611102B74F/ Grant No . CM U - W91WAW07C0063

• Th e Army Research Labs Grant No . DAAD 19-01-2-0009

• Th e views an d conclusions contained in this document are those of the authors an d should not be interpreted as representing the official policies, either expressed or implied, of the National Science Foundation or th e U.S. government.

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