DEIM Forum 2014 A5-3

Predicting Facebook Users’ Interactive Motivations from Observable Factors
ンヴンギ

バシリサ†

岩井原瑞穂‡†早稲田大学大学院情報生産システム研究科〒808-0135 福岡県北九州市若松区ひびきの ２－７
E-mail:†basilisam@akane.waseda.jp,‡iwaihara@waseda.jp

Abstract: Motivations to use social networking service (SNS) can be classified into inward, outward, and neutral. Inward
motive is to interact with existing SNS friends, outward motive is to acquire new friends via the SNS, and neutral motive is the
motive which cannot classify whether a user has inward or outward motive. Predicting users’ interactive (inward, outward, andneutral) motivations of using Facebook is important for its users, providers, application developers, and marketers for target
advertising and friendship recommendation. We have conducted a survey via a questionnaire, to study relationship between
motivations and observable factors that are visible from the public. In this paper, we discuss constructing prediction models;
based on binary logistic regressions, to predict motivations from observable factors such as profile photo, number of friends,
contact openness, non-contact openness, gender, and age. Results show that our prediction models (inward and outward) havehigh accuracy and c statistics, indicating that the models can predict latent motives well and have good discrimination power.
We also report that our prediction models perform better than a random selection model. Furthermore, using dimensional reduction technique important predictors for optimum prediction models were identified.
Keyword: Accuracy, c-statistics, dimensional reduction, f1-score, Facebook, kappa, motivations of using SNS.

1. INTRODUCTION

SNSs through prediction models. The models estimate

It is known that not a small fraction of Facebook users

likelihood of having inward , neutral, and outward motives

are disclosing private information to a large group of

from only observable factors. Here, observable factors

people in spite of their risk awareness[6]. This is partly

refer to user-generated data visible from the public level .

due to their motivation(s) behind , for example, communi-

We focus on interactive motives where lurking is not con-cating with new friends[6], which cause users to compro-

sidered, because a user with lurking motives do not re-mise their long-term risk awareness, and communicating

quire any information in his/her profile page. While a user

for short-term benefits.

with interactive motives require disclosing identifying

Motivations of using SNSs (motivations for short), par-information in order to facilitate communications. We

ticularly Facebook; have been widely studied[1,2,5,7,8].

classify motives into three groups, which are inward,

However, all previous works related to motivations relied

outward, and neutral motives. Inward motive is to interact

sorely on users’ responses from questionnaires becausewith existing SNS friends. Outward motive is to acquire a

motivations are latent and subjective factors. The survey

new friend via the SNS. Neutral motive is a motive toward

process can be very laborious in terms of time and cost,

activities a user performs on the SNS, where the activities

especially when sample data of diverse demography is

do not indicate explicit distinction between new and e x-

required. In an SNS a user ’s profile data can be observed

isting friends. In this paper, we will focus on inward andby other SNS user(s), however reason(s) behind the user

outward motives. With our prediction models, researchers

information disclosure is quiet unknown to other SNS

with a data set that contain only observable factors canusers. If motivations can be detected from observed user ’s

estimate motives for further detailed analysis. SNS users

profile, then it will be known whether the user uninten-(providers, end users, marketers, and application devel-tionally or intentionally publish his/her data. Using con-opers) can use the models to improve users experience,

structed prediction model, it is possible to predict user ’stargeted advertising and proper friendship recommenda-

motivations provided that a data set consists of users’

tions.

response on motivations and behaviors is available fortraining purpose.
In this paper, we discuss estimating motives of using

Contribution: To the best of our knowledge, we are thefirst to derive prediction models that estimate motives ofusing an SNS from observable factors. The models wepropose in this paper have several advantages: i) The

lyze a vast collection of SNS data that are accessible frommodels are simple and intuitive, only requiring user ’s SNS

the public. The requiring factors of our prediction model sprofile data that are visible from the public, ii) using

are easily translatable between different SNSs, which

dimensional reduction technique optimum predictor var i-enable comparison of user motivations between SNSs.

ables were identified with only little degradation in theperformance of the models and, iii) the models are flexible
to the changes in requiring data sets and they can be ap-

3. PREDICTIONMODELMETHODOLODY

and PREDICTOR VARIABLES

plied to different SNSs that have similar attributes. In thisBinary logistic regression is one of prediction method-

paper, due to space constraint, we will discuss the firstologies that are widely used in clinical fields to predic t thetwo advantages.

outcome of a future patient (predicted probability) gi ven

The rest of the paper is organized a s follows. In Section

the patient characteristics[3], and also used in psycholo g-

2, we review related work. In Section 3, describe ourical analysis. In this paper, we adopt binary logistic r e-

prediction problem. The construction of our prediction

gression to find whether an SNS user is {hi gh, medium,models are presented in Section 4. In Section 5, we willlow} in {outward, inward} motives, through combination

present our experiment results. In Section 6, we will pr e-of observable factors. We focus on finding which combi-

sent a summary in the form of discussion, and Section 7 isnations of factors are high in accuracy and practicallya conclusion and future work.

useful.

2. RELATED WORK

profile attribute of a user. If a user opens his/her attribute

We set a threshold termed as a scope threshold for each
Motivations of using SNSs have been studied as a sep-value beyond the scope threshold, we regard that he/shearate variable[2,5] or related to other factors[1,7,8] be-

opened this attribute. Facebook has five levels of disclo-yond our work. [2,5] found that, Facebook users use thesure scopes which are 1) only me, 2) selected individuals,site more to communicate with their friends than to ac-3) friends 4) friends of friends, and 5) everyone. For ourquire new friends. In our previous work[6], we investi-data, we set its scope threshold at between 3) friends andgated factors causing private information disclosure. We4) friends of friends. This threshold is intended to illumi-

found that motivation to acquire new friends is amongnate whether the user has an intention to reveal info r-

factors that cause private information to be open , even inmation to friends of friends and beyond, or just to sharespite of the risk awareness. Other studies about motiva-

with his/her direct friends.

tions were related to factors such as trust and Internet

We model profiles of SNS users as follows. An SNS

privacy concern[1], Facebook features[7], demography,

user j has a profile pagetime spent on Facebook, Facebook usage and per sonal

i={1,2,3,….,I}, and each item x i has a non-negative value,

network metric[8].

representing openness of x i . For most of items, we use

consisting of

items

xi,

In spite of the abundance of researches related to mo-binary number {0,1} as values except for items like num-

tivations as reviewed in the above, the current approach of

ber of friends and age where we use continuous values .

using surveys to obtain users’ motivations responses has

Depending on user j’s privacy settings, each item is either

several challenges: i) the questionnaire-based approachvisible from the public (value 1) or not (value 0). Not that

suffers from low response rate i.e. the ratio of users who

the latter case includes the situation where user j only

responded to total number of users who received invitation

shares the items with friends and friends of friends, orrequest to participate in a survey. Compensation is there-

simply does not supply item x i to the SNS. However it isfore awarded to users in order to motivate them to parti c-difficult to separate these non-public cases and therefore

ipate in the survey. ii) Analysis technique such as logistic

we only focus on whether user j opens item x i to the public

regression requires at least 10 users per predictor variable.

or not. Let X = [x 1 ,x 2 ,…,x I ] be the vector of items values

The more the number of predictor variables, t he more theof user j. Let Z∈{0,1}be one of the six dependent variables

minimum number of required users and therefore the moreon whether the user is {high, low} in {outward , neutral,

duration is required to obtain enough responses. iii) Since

inward}motive. Given observable factors X j of user j, wemost of SNSs adopt privacy control, only permitted co n-model the predicted probability P j (Z=1|X j ) of user j’s

tents are visible from the public. By incorporating only

motive satisfying Z=1 as follows:

observable factors in our prediction models, we can ana-

𝑃𝑗 =

1

(1)

𝐼

1+𝑒 −(𝛽0 +∑𝑖=1 𝛽𝑖 𝑥𝑖)

each motive, which we use to build two classifiers C1 andC2. Classifier C1 separates {in ward, outward}-high andwhere  0 is a constant factor and  i is a coefficient of{inward, outward}-medium, while classifier C2 separates

predictor variable x i . Equation 1 is derived from the fo l-{inward, outward}-medium and {inward, outward}-low.

lowing logistic regression formula

The criteria for selecting two thresholds will be discussed

ln (

𝑃𝑗

1−𝑃𝑗

) = 𝛽0 + ∑𝑖=𝐼
𝑖=1 𝛽𝑖 𝑥𝑖 .

(2)

in Section 5.
We use the following observable factors as predictor

The constant factor and coefficients of predictor vari a-variables to predict motives: Age, Gender (we encode

bles are computed by using maximum likeli hood estima-

female = 0 and male =1), number of friends in logarithmiction, where both predictor variables and motive values areform (LogFriends), ProfilePhoto, Contact information

required as a training data set.

which is the number of open contact attributes of a user,

We conducted a survey on Facebook users with a ques-

i.e.

k

∈

{1,2,3,….,m}where

m

is

the

numberoftionnaire[6], in which users’ motives were asked among

non-contact attributes and k is openness, and Non-contact

other questions. The question consists of 16 items, each

openness which is the number of open non-contact attrib-

anchored using five point Likert scale ( from 1= strongly

utes of a user, i.e. k ∈ {1,2,3,….,n}where n is the number

disagree to 5= strongly agree ). We label user j’s response rof non-contact attributes and k is openness. We use theon a motive m based on the Likert scale as follows: (a) j isfollowing for Contact openness attributes: Address, IM,

low on m when r is 1 or 2, (b) j is medium on m when r is 3,

Email, and MobilePhone. We use the following forand (c) j is high on m when r is 4 or 5.

Non-contact

openness

attributes:

RelationshipStatus,

Applying a dimension reduction technique called fac-

Family, CurrentLocation, Hometown, Work, BirthdayFull,

tor analysis, we further grouped these motives into three

and Birthday Partial. However, any profile attribute(s),independent motives (inward, neutral, and outward ) as

which fall(s) under contact or not contact information

shown in Table 1. Inward, neutral, and outward motives ofcategory, can be included in Contact and Non-contact

user j are calculated as the mean value of the element

openness, respectively.

motives in each motivation. Due to space constraint, we
will present results for inward and outward motives only.
Later we discuss thresholds to determine whether user j iseither high, medium, or low in these motives.

4. PREDICTION MODEL CONSTRUCTION
4.1 Data Collection Method
A total of 276 participants with age ranging from 20 to
59, from 17 current countries of residence agreed to par-Motivations of using SNS items and scales2

Mean

SD

Neutral scale (Cronbach’s alpha =0.86 )Commented on photo/posts

3.03
3.13

0.89
1.10

.833

2.87
2.88
3.11
2.64
3.54

1.15
1.15
1.22
1.22
1.15

.815
.787
.707
.605
.510

Changing current status
Updating or editing profile
Posting on wall
All my friends have accountEntertainment (pass time)

Factor loadings1
NeutralOutward
InwardOutward scale (Cronbach’s alpha =0.82)
2.94
0.91
Creating group/groups
2.72
1.20
.789
Have direct encounter with Facebook friend
2.38
1.15
.782
Meeting new people and make new friends
3.20
1.29
.762
Finding about, organizing or oj ining event
3.02
1.14
.694
Interacting with group/g roups
3.38
1.17
.521
Inward scale (Cronbach’s alpha = 0.77)
4.03
0.81
Reconnect with people you have lost contact with
4.20
.974
.878
Maintaining relationship with friends
4.05
.958
.816
Contacting people far from you cheaply
3.98
1.13
.665
Keeping in touch with old friends
3.90
1.15
.629
Notes: 1Principal component factor analysis with varimax rotation, cut off point is 0.500. 2Individual
items ranged from 1=strongly disagree to 5=strongly agree,Tab.1. Summary statistics and factor analysis results
for motives.

ticipate in our survey. Females were 134 while males were142. As compensation, participants received a complimentary copy of possible social network risks and re commendations for safe practice in their email.

Partic i-pants were recruited in six ways: invitation post ed on theForeign Student Division Board and on an international
house board, mailing lists, visiting close members who
have a Facebook account to their houses and giving them aquestionnaire to participate, posting an invitation on several Facebook group walls and giving links to members
who were interested in participating, recruited by a Jap anese company, and chatting with online Facebook mem-

Binary logistic regression requires that a dependent

bers and inviting them to participate by giving a link to avariable Z (motives) to be dichotomized. Therefore, we set

survey for those who showed interest. The link wasthresholds corresponding to several top Y% users follow-

opened for three weeks.

ing descending order of the mean of each motive, where
Y= 10 + 5g, g ={0,1,2, …..,17}. If a user is among top Y%

4.2 Survey Designusers, we regard the user as having the motive, otherwise

The survey questionnaire we prepared contained four-

the user has no motive. Then we select two thresholds forteen questions, which were FB related questions (motiva-

tions, disclosure scopes of some attributes, and the total
number of friends). The survey also contained the set of
demographic questions (gender, age, status, and current
country of residence). The survey was prepared by using
Microsoft Word to create a paper version and was also
hosted on GoogleDocs. The survey is available in English
and Japanese versions upon request from the authors.

Tab.3. Performance tests results for outward motive

To ensure content validity, pre-tested scales were reliedon where possible. All items in questions were anchored

Table 2 and Table 3 show the performance results forusing a Likert scale except for question s related to age,the inward and outward motives, respectively. We cancurrent country of residents, and the total number ofobserve that irrespective of the distributions of users forfriends. We relied on[2,4,5,9] to prepare items for motive

each motive, performance results tend to increase when atconstruction. We modified some items where need ed. For

least top50% users have a motive. The cut points for in-

the purpose of this study, we only considered interactive

ward motive are higher than outward motive, because themotivations and activities without lurking .

distribution of inward motive is skewed to the right ind icating many users are high in inward motive. While the5. EXPERIMENTS

distribution of outward motive has a single peak andThis section discusses about the performance tests re-symmetric. We can also observe from Table 1 that thesults used to determine the appropriate classifiers C1 andmean of inward motive is 4.03, while mean of outward

C2 for each motive. Each performance measure will be

motive is 2.94.

explained in the next paragraph.

We used the following conditions to select appropriate

We used the following performance tests to assessclassifiers for each motive. For both classifiers in eachthresholds in each motive. Accuracy is the proportion of

motive, we set lower limits of 0.6, 0.6, and 0.3 to accuracy,users that are correctly predicted. When any of our pre-c-statistics, and kappa, respectively, to ensure prediction

diction models is applied to a particular user, accuracy

qualities. For classifier C1 in each motive, we selected agives the expected probability that the motive will be

cut point for top Y% users based on the following cond i-correctly predicted. C-Statistics[3] is a discrimination

tions: 1) the mean value corresponding to top Y% users

measure, which refers to the ability of the prediction

should be above 3.6, 2) the value of F1 score should not bemodel to distinguish correctly the two classes of outcomes.

equal to zero (see Table 2 and Table 3). For classifier C2

In binary logistic regression, this measure corresponds toin each motive, we selected a cut point based on the fol-

the area under the receiver operating characteristic (ROC)

lowing conditions: 1) the mean value correspond to top

curve. F1 score is the performance measure for finding

Y% users should be below 3.6, 2) the value for F1 score

portion of objects out of the large set. Kappa is a statisti-

should not be equal to zero . Classifier C2 aims to predict

cal measure that shows our prediction models are better

users with low motives, therefore we computed F1 scorethan random selection. If kappa is near zero, it means thatfor bottom Y% users (bottom 40% users and below). T he

our prediction model is not much improving from random

results are shown in Figure 1 .

selection. If kappa is close to one, the prediction is almost
perfect. Diff accuracy is accuracy with predictor variables
minus accuracy with constant value only.

Fig.1. F1 score for left: inward motive and right:
outward motive
Tab.2. Performance tests results for inward motive

From the above conditions on classifiers, we found that
top 70% and top 80% users with corresponding inward
motive mean values of 3.75 and 3.5 are appropriate cut

points for classifiers C1 and C2, respectively. For theoutward motive, we found that top 20% and top 7 0% users
with corresponding mean values of 3.8 and 2.4 are appropriate cut points for classifiers C1 and C2, respectively.
Furthermore, we performed dimensional test for eachselected prediction model by removing variables , andobserve changes in performance results. From this dimensional reduction test, an optimum prediction model with

Tab.6. Performance results for outward high prediction model when attribute(s) are removed

minimum number of predictors, causing little or no degradation in performance measures, is obtained. A prediction model with minimum predictors is desirable, because
the model is applicable to a wider range of data sets wherecertain attributes may be unavailable.
In Tables 4-7, each row shows the number of removed
predictor variable(s), predictor variables included in pre-Tab.7. Performance results for outward low predic-

diction model, and performance results. The results revealtion model when attribute(s) are removed

that when predictor variable(s) are removed in ascending

Below, we present the optimum prediction models selectedorder of Wald’s (without replacement), the first few mo d-in Tables 4-7, where the four prediction models consist ofels indicate certain degradations from the prediction

the combinations of {high, low} in {inward, outward}model with no attribute re moved (Row in Tables 4-7 withmotives.

the number of removed predictor = 0). Then the perfo r-

𝑃𝐼𝑛𝐻𝑖𝑔ℎ =mance results tend to either slightly increase or decrease

1
1 + 𝑒 1.193−𝑎,(3)with subsequent removals of predictors. When the pe r-

where a = .713x L ogf ri ends + .944x Prof i l ePhot o - .363x Cont a ct

formance results start to decrease, we include the predi c-- .180x NonCont act

tor variable that causes reduction in subsequent analysis.

𝑃𝐼𝑛𝐿𝑜𝑤 =Therefore the prediction models with the minimum nu m-

11+𝑒

2.392−1.403𝑥𝐿𝑜𝑔𝐹𝑟𝑖𝑒𝑛𝑑𝑠−.019𝑥𝐴𝑔𝑒+.095𝑥𝑁𝑜𝑛𝐶𝑜𝑛𝑡𝑎𝑐𝑡,(4)

ber of predictors (the dotted circle in Tables 4 -7) include
two types of predictors; significant predictor(s) and in-

𝑃𝑂𝑢𝑡𝐻𝑖𝑔ℎ =cluded predictor(s) that avoid performance degradation.

1
1+𝑒

𝑃𝑂𝑢𝑡𝐿𝑜𝑤 =8.197−.95𝑥𝐴𝑔𝑒 −1.498𝑥𝐿𝑜𝑔𝐹𝑟𝑖𝑒𝑛𝑑𝑠

1
1+𝑒 4.645−𝑐

,

,

(5)

(6)

where
c=1.256x L ogFri ends +.075x A ge +.006x NonCont act –.347x Cont act .
Tab.4. Performance results for inward high prediction model when attribute(s) are removed

6. DISCUSSIONS
We used Facebook as a case study to find prediction
models that predict latent motives of using the SNS fromobservable factors. Our prediction models can be applicable to other SNSs having the following features: (a) the
SNS has similar distribution of motives as Facebook users ,and (b) the SNS has access controls for profile attributes(especially for profile attributes, to apply the optimum
prediction model) that can be specified by privacy settings.

Tab.5. Performance results for inward low prediction
model when attribute(s) are removed

With our prediction models, it is also possible to predi ct auser motives across multiple SNSs.
The attribute for the number of friends appears as asignificant predictor in both high and low motives. With-out the number of friends as an observable factor, it istrievedNovember

15,

2008,

from

difficult to predict a user ’s motive. Age appear as signif-

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