DEIM Forum 2016 A5-2

Estimating Purposes of Users in Social Networking Service Public Contents
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: We call as publicly active users (PAUs) those who actively post contents to publicly-visible locations in social
networking services (SNSs). In this paper, we explore posting purposes of PAUs (open-post purposes) through inspecting their
public contents. We sample PAUs from five public groups of different interests and one set of general users whose interests are
unknown. We can safely assume that each PAU is sharing the group’s interest by posting to the interest group. We manuallyclassify posting purposes based on types of activities into 19 purpose items, and five high-level categories of open-post
purposes: promoting self, promoting own activities, promoting opinion, propagating quotes, and propagating news. We further
evaluate for independence of these five OP-purposes through statistical analysis, which results into promoting self, propagatingnews quotes, and propagating sociality information. We find that PAUs have the following five purpose items: posting groupor fellow photos (83% PAUs), propagating positive social news (83% PAUs), posting self photos (77% PAUs), propagating
positive quotes (65% PAUs), and posting personal details (60% PAUs). These findings suggest that PAUs are relying more on
posting about their close people and propagating news (lifestyle, technology, entertainment) than either posting self photos or
sharing photos containing their favorite messages. However, correlation analysis reveals no relation between PAUs posting
photos of themselves and their close people, and propagating of information (news, quotes) from SNS users. We also introduce
post-frequency level of 0 to 2 to represent how much degree a PAU exhibits a particular purpose item.
Keyword:Publicly active users, user behavior analysis, purpose analysis, SNS user contents.
post openly.

1. INTRODUCTION
Uses and gratifications approach is commonly used to

Identifying OP-purposes and revealing predictors ofinvestigate motivations of SNS users [3,4]. To assess SNS

these purposes are useful for SNS providers to classify

uses and gratifications, surveys are usually conducted

users and their posts based on their latent purposes.

becausemost of uses

areprivately

performed

andgratifications are latent. Publicly active users (PAUs for
short) publicly publish contents such as video, photos, text,

2. PURPOSE MODEL CONSTRUCTION
2.1 Data Collection

and links to articles via SNS. Upon observing contents,

We use two methods to sample user IDs. In the firstSNS users often find that PAUs publish their photos tomethod, we generate random user IDs and filter out user

promotephotosIDs that are invalid. We term users with random IDs as

containing words to propagate quotes or news. These few

general users and we assume that their interests areexamples give an insight that PAUs are publishing

unknown. In the second method, we sample user IDs fromcontents because they have purposes to do so. In [2], threepublic groups. We infer users’ interest from the group thathigh-level purposes of 2012 US presidential tweets arethey are participating. We focus in public group(s) on

identified: oppose, favor, and other.

which users share one of the four topics, namely business,theiridentity.

Also,

PAUs

share

In this paper, we explore posting purposes (open-post

politics, animals, and music. Therefore, we use the SNS

purposes or OP-purposes for short) of PAUs by inspecting

search function to search for each topic in order to find thetheir SNS public contents. From identified OP-purposes,

public group(s). If one or more public group are returned,

we construct a purpose model by classifying purpose items

we roughly observe the contents of each group forbased on types of activities. After that, we refine theactiveness and homogeneity to judge whether the group’s

purpose model by statistical analysis. Contrary to [2], we

contents reflects the group’s interest. Such observation is

are identifying multiple purposes on one post. Also, wenecessary because we safely assume that users belonging

focus on what are typical purposes, which drive a PAU toto a group tend to share the common interest of the group.

Activeness means the group contains members who are
actively or continuously participating to discussions.
Homogeneity means the contents of the group are mostly
related to the group name. If no public group is returned,
we look for similar topics(s) and again search until wefind a public group, which contents are reflecting thegroup’s interest.
By the above procedure, we find five public groups as
seen in Table 1 and collect user IDs from these groups(column “Collected user IDs” in Table 1). These user IDs
are collected from groups’ posts and visible comments thatare published from newest (April 2015) to oldest (March
2015). Visible comments are comments, which do not
require a user to click a link in order to view them. Then,we extract user IDs from those posts and comments andremove duplicate user IDs.
2.2 Selecting PAUs
select users who are active in publicly posting contents via
SNS. Therefore, we apply conditions to the collected user
samples in Table 2 in order to select publicly active users(PAUs). We set the following conditions for a user to be a
PAU: i) post frequency of the PAU. We set the criteriasuch that the user should have at least one post per monthin year 2015 (January - May), the user should have at least
one post in year 2014, and the user posted at least 15 posts
between January 2014 and May 2015. ii) The language
used on PAU’s posts should be English. English language
is essential to enable collaboration of raters with different
ethnicity when analyzing PAUs’ contents. Nevertheless,we select a PAU who has a few posts written in otherlanguages when translation option is available.
Table 1 shows the counts of user IDs and PAU IDs thatare sampled from five public groups and one set of generalusers. We process 2467 user IDs and 350 PAU IDs
ourcriteria

onvalidity.

Weobservethat

proportion of general PAUs is smaller compared with thatof PAUs from public groups (see Table 1). Upon
processing random IDs for general PAUs, we found thefollowing: i) most of sampled general users have notsatisfied the conditions to be PAUs, and ii) some IDs arefor public pages and groups.

Group’sinterest

GeneralUnknown
Public group names

952
565

115
249

46 (40.0)49 (19.7)

Political-enthusiast
Trade

175

172

72 (41.9)

544

250

74 (29.6)

Trade

517

23

9 (39.1)

All
5
9746
2467
350 (14.2)
Note: a Number of users whose pages are checked for meeting theconditions to be PAUs. b Proportion of processed users inpercentage that are PAUs .

2.3 Constructing the Purpose ModelIn this section, we explain about how we construct the
purpose model from purpose items based on types ofactivities. Then, we will explain about how the purpose
model is refined using statistical approach. Table 2 shows

2.3.1 Constructing Purpose Model ManuallyFirst we discuss listing up candidate open-post purposes(OP-purposes).

We

adopt

the

following

steps:(1)

Identifying candidate OP-purposes from public contents,(2) filtering out OP-purposes that are rarely observed, (3)refining candidate OP-purpose documented in Step 1 toremoveduplicatedclusteringoforsimilarOP-purposes,

similarpurpose

items

into

and

(4)

high-level

categories.
(1) Identifying candidate OP-purposes from public
contents. We identify OP-purposes of first 185 PAUs in
Table 2. For each PAU, we write an outline of any
OP-purposes that can be identified from his/her publicly
observed contents, dated between January 2014 and May
2015. There exist posts that are difficult to interpret theirrespective OP-purposes. For example, posts with words
such as “Good night!!”, “Hello friends!”, “Noooo”, orposts with pictures of places (parks, beaches, etc.), andobjects (buildings, sculpture, etc.). These posts are
therefore not counted.
(2) Filtering out OP-purposes that are rarely observed.
This step is necessary for statistical analysis, to avoid
OP-purposes having only a few PAUs. We remove
OP-purposes in Step 1 that are identified in not greaterthan 20 PAUs.
(3) Refining candidate OP-purposes documented in Step
1 to remove duplicated or similar OP-purposes. We

Table 1. Data set summary
Source of data

Pet
Music

the description of PAU sets that are used in this section.

To identify OP-purposes consistently, it is necessary tomatchedHiking With Dogs
Jazzmasters&Ja
guars
Constitutional
Patriots
Like For Like
PromoteYour
Business
PromoteMy
Business

Collect
ed user
IDs
6993

Process
ed user
IDs a
1658

PAUs IDs(%) b
100 (6.0)

manually classify OP-purposes based on types of activities
into 19 purpose items. As an example, Table 3 shows 2 out
of 19 purpose items.
(4) Clustering similar purpose items into high-levelcategories. We identified the following five high-level
categories: promoting self, promoting opinion, promoting
professional activities, propagating quotes, and propagatingnews. Promoting self is associated with a PAU’s intention
to expose his/her personal life via SNS within the contexts
ofthemselves,families(includingpets),

friends,

non-professional activities, and possessions. Promoting
own activities is associated with a PAU’s intention to
exposehis/hersocialandprofessional

activity

commitments via SNS. Promoting opinion is associatedFigure 1. Purpose model constructed from identified

with a PAU’s intention to express his/her thoughts on

purpose items

news or encountered situations via SNS. Propagating
quotes is associated with a PAU’s intention to share words
from

other

sources,which

reflect

his/her

taste.

Propagating news is associated with a PAU’s intention toshare information about events that happens in a society.
The hierarchical structure of purposes is shown in
Figure 1, whose purpose symbols are defined in Table 4.
In Figure 1, a promoting purpose is a purpose of a PAU
posting for apparently promoting or advertising his/her
own agenda. On the other hand, a non-promoting purposeis the complement of the promoting purpose such that
promotional/advertising purpose is not explicit. Public
postings can convey promoting purposes in various forms,
either implicitly or explicitly. Implicit promoting purpose
can be exemplified as propagating others’ posts and news
that match with the PAU’s purpose, as well as, a PAU
sharing a snapshot, which contains details of upcoming
music

performanceof

his/herfavorite

Table 2 Summary of research sets and their descriptions
PAU
Sec(s) a Description
setsFirst b PAUs out of 350 PAUs.
PAUs who are randomly selected out of
350 PAUs. In Step 2, rater1 crosscheckpublic contents of the first 100 PAUs,
while rater 2 crosscheck that of theremaining 100 PAUs.
150
2.3.2;
These are remaining PAUs afterStep 3
selecting 200 PAUs above.
350
These are all PAUs that match ourcriteria for validity in Section 2.2.
Note : a Sec(s); Section(s). b 54 are general PAUs, 46 from
“Hiking With Dogs”, 41 from “Jazzmasters&Jaguars”,
18 from “Constitutional Patriots”, 17 from “Like For
Like Promote Your Business”, and 9 from “Promote My
Business”
185
200

Table 3. Purpose identification table
Symbol Purpose item

PAUs’
observed
content
constitute purpose items

Me

Posting self
photosPhotos of PAU either at home orvisiting several places (restaurant,mall, beach, holiday, etc.)+veN

Propagating
positive social
news

Social news (lifestyle, technology,
entertainment, sports) shared by
PAU to provide useful self-education,awareness, or enthusiasm.

musician.

However, in this study we label a post with the promoting
purpose when the PAU publicly posts contents that are
directly linked to him/her, or share such contents fromother sources (public page, website, other SNSs). One
public post can be labeled with multiple purposes. For
example, a PAU posts his/her comments on a shared news
article. In this case, the post is labeled as both promoting(explicitly

expressing

thoughts

on

2.3.1
2.3.2;
Steps
2-3

news)

and

that

Note: To identify PAU’s photos, at least one photo should havedescription about PAU. News is shared from other users, otherSNSs, or websites as articles, videos, photos, and links.

non-promoting (propagating news). However, if the PAU
shares another’s post or content but not giving his/her own

2.3.2 Post-frequency Levels on Purpose Items

comment, we regard that the PAU’s intension is implicit

Now we introduce the post-frequency level of 0 to 2 toand we label the post as non-promoting only.

represent how much degree a PAU exhibits a particularpurpose item, as follows:(1) Assigning the post-frequency level of 0 to 2 to a
PAU on each purpose item, as follows: i) level 0 if public
contents that constitute the purpose item are not observed,ii) level 1 if public contents that constitute the purpose
item are observed between 1 to 5 times, and iii) level 2 if
public contents that constitute the purpose item areobserved at least 6 times. If no more than five posts arewith Gr (r = .393, p < .01) and Detail (r = .250, p < .01), Gr

observed per PAU, then only levels 0 and 1 are used.

is positively correlated with Detail (r = .336, p < .01), and(2) Crosschecking by raters. Here, raters are colleagues+veN is positively correlated with Detail (r = .198, p < .01)

(not authors of this paper) with expertise in the socialand +veQ (r = .286, p < .01). From these results, we can

network field. We use two raters, for assuring accuracy of

say that it is not surprising to find that PAUs who are

our initial inspection of public contents. Raters are also

actively posting photos of themselves, are also posting

instructed to give comments where necessary. Table 2

about their close people and writing details about theirshows details of PAUs that are used in this step, andpersonal lives via SNS. However, it is surprising to findcrosschecked by raters. Using the table (purpose itemthat there is no relationship between PAUs posting photos

table for short) that describes guidelines of each purposeof themselves and their close people, and their tendency ofitem as a guideline and post-frequency levels in Step 1, we

propagating news (technology, entertainment, lifestyle)select 200 PAUs and first inspect each PAU’s publicfrom SNS users. Indicating that, PAUs who are actively

contents that match with each purpose item. After

propagating news are not necessarily more/less active in

inspection,

posting photos that are directly linked to them.

werecordpost-frequency

level

of

each

purpose item for each PAU. Then, raters are given

In addition, we perform descriptive statistics on number

instructions to repeat the procedure above. We calculateof purpose items having post-frequency levels of 0 to 2 for

Cohen’s Kappa coefficient to measure agreement oneach PAU. We find that PAUs are likely to have morerecording post-frequency levels of purpose items between

purpose items with the post-frequency level of 0 (Mean =the first author and each rater. We find that agreement

11.1, SD = 3.1) than those with post-frequency levels of 1

between raters and the first author is above moderate.

(Mean = 4.4, SD = 2.4) and 2 (Mean = 3.5, SD = 1.7).

(3) After collecting feedbacks from raters, we inspectagain public contents of 200 PAUs (in Step 2), which have

Table 4. Distribution of PAUs’ post-frequency levels fordisagreement between the first author and raters, andeach purpose item (N=350)

resolve into unique post-frequency levels. We also inspect

Symb
ol
Me
Gr
Anim
Detail
Favor
Soc
Caree
Mark
SocO
RelO
PolO
+veQ
-veQ
FunQ
PolN
+veN
-veN
RelN
FunN

public contents of 150 PAUs (Table 3) and resolve thepurpose item post-frequency levels in the same manner. In
total, our data set consists of 350 PAUs and 19 purpose
items.
2.3.3 Purpose analysis
Table

4

shows

the

resultsofSection2.3.2

ondetermining post-frequency levels of 0 – 2 for eachpurpose item over 350 PAUs. We observe that the five
most frequent purpose items (with lowest frequencies atlevel 0) are posting group or fellow photos (Gr), posting self
photos(Me),propagating

positivesocialnews(+veN),

propagating positive quotes (+veQ), and posting personal
details (Detail). These purpose items also have highfrequencies at level 2 in comparison with other purpose
items. These tendencies suggest that PAUs are relying

Purpose item
Posting self photos
Posting about close people
Posting pet photos
Posting personal details
Requesting favor
Promoting social activities
Promoting career activities
Promoting marketing activities
Promoting social opinion
Promoting religious opinion
Promoting political opinion
Propagating positive quotes
Propagating negative quotes
Propagating funny quotes
Propagating political news
Propagating positive social news
Propagating negative social news
Propagating religious news
Propagating funny news

Leve
l0
82
60
198
141
265
307
276
209
187
306
289
122
204
241
254
60
165
269
243

Leve
l1
141
107
90
80
80
33
21
61
118
32
37
129
103
95
49
117
113
58
77

Lev
el 2
127
183
62
129
5
10
53
80
45
12
24
99
43
14
47
173
72
23
30

2.3.4 Refining the Purpose Model through Statistical
Analysismore on posting about their close people and propagating

Now we evaluate independence of the five high-levelnews (lifestyle, technology, entertainment) than either

OP-purposes in Figure 1 through factor analysis, to refine

posting self photos or sharing photos containing their

the model. We adopt from [1] a benchmark of item loading

favorite messages (quotes). Also, as Detail has 129 PAUs

greater than 0.5 to identify a purpose item as a marker forat level 2, writing details about their personal lives via

the factor. After examining factor loadings, we retain

SNS is popular among PAUs.

three factors (Table 5) corresponding to three high-level

Furthermore, we conduct correlation analysis on the top
5 purpose items and find that Me is positively correlated

OP-purposes: promoting self, propagating news quotes, andpropagating sociality information, shown in Figure 2.

Promoting self is associated with a PAU’s intention to
expose his/her personal life without posting self photos
via SNS. An item posting self photos is discarded due to its
low factor loading. Propagating sociality information isassociated with a PAUs’ intention to share words that areeitherencouraging,educating,orentertaining.

Propagating news quotes is associated with a PAU’s
intention to share news (political, religious, violence, etc.)
and his/her words that either support or oppose people’s
opinions. Three purpose items promoting social opinion,
promoting political opinion, and promoting religious opinion
loaded on propagating news quotes factor. However, weFigure 2. Purpose model constructed through factor

discard them because they are classified under promoting

analysis

purpose, and high-level OP-purpose promoting opinion in
Figure1.

Joinson[1]

also

discarded

item(s)with

3. OBSERVABLE FEATURES

acceptable factor loading(s) that do not represent a factor.
If we adopt our refined purpose model, the following
purpose items will be excluded due to not fit to be in thethree factors above: promoting social opinion (SocO),
promoting religious opinion (RelO), promoting political opinion

In this section, we will discuss the observable featuresof PAUs, which are numerical indicators extracted from
PAU’s public posts and profile items. We investigate
possibilities of utilizing observable features as predictors(PolO), promoting social activities (Soc), promoting careerof his/her purposes of posting contents in public. In theactivities (Caree), promoting marketing activities (Mark), andfollowing, we denote by OF the set of observable features,

posting self photos (Me). However, these purpose items arewhere X is divided into non-overlapping subsets PI

clearly visible in postings and explaining PAUs behaviors(profilewell. So we retain these purpose items and adopt theinterests), and PPT (profile photo types).

items),

AI(activity

indicators),GI(grouporiginal purpose model in Figure 1, and we study
statistical correlations between these purpose items and

3.1 Profile Items

other observable features of PAUs.

Profile items are information elements in a PAU’s
page.

In

Table 5. Summary statistics and factor analysis ofprofileProfilePhoto,

and

this

paper,

OP-purposes(Non-contact openness) as profile items PI.

Weighted

weconsidernon-contact

We encode Gender as follows:

Gender,openness

Gender equals 0, 1, or

system missing, if the PAU is male, female, or undisclosed,
respectively. In our data set, there are 158 males, 141
females, and 51 PAUs whose gender information are notshown in their public profile pages. For ProfilePhoto, we
set ProfilePhoto = 1 if the PAU discloses his/her profile
photo to the public, and 0 otherwise.
Contact profile items are those that strongly identify
user

identities,

such

as

MobilePhone,

Websites,

BirthdayFull, BirthdayPartial (year omitted), Email, andScreenNames. For security reasons, very few users open
such contact profile items. We define that NG is the set ofthe profile items where contact profile items, Gender, andProfilePhoto are excluded.
We utilize the following weighted non-contact openness
NC

[3]

as

a

normalizedfeaturefor

openness

non-contact profile items, ranging between 0 and 1:of𝑥  ! = 𝑃  !" =!
|!"|∗!

!∈!" |𝑅! | 𝑦! .

(1)

the activity indicator (e.g. Music), and iii) SNS users do
not interact with the PAU regarding the activity indicator(e.g. Followers). Due to high variability, we convert

Here, y k , k = {1,2,3,…,K} is such that y k =1, if the k-th
non-contact profile item is disclosed to the public and 0

activity indicators into their natural logarithmic forms.
We denote by AI ⊆ OF the set of the observable

otherwise, N is the total number of PAUs, and R k equals

features consisting of the activity indicators.

the number of PAUs who opened the k-th non-contact

AI are continuous.

Values of

profile item. The feature NC gives weighting such that a
profile item has more weight if more users disclose it.

Table 6. Description and descriptive statistics of theThis weighting gives us benefits such as prediction

activity

indicators

accuracy improvement, variable reduction, and portability
between SNSs.
Figure 3 shows the non-contact profile items plus
ProfilePhoto, and their disclosure rates, which is thepercentage of the PAUs that open the item. We note that
Figure 3 shows the profile items having disclosure rates
over 10 percent and used in computing the weighted
non-contact openness NC. We exclude items whose
disclosure rates are no greater that 10 percent, becausesuch profile items are sporadic and hard to be utilized to
explain purpose items.

3.3 Group Interests
As we discuss in Section 2.1, we assume that a PAU
participating to an interest group share the interest of thegroup. We utilize the group membership information as anobservable feature. Let GI ⊆ OF denote the set ofgroup-interest features such that a binary feature takes
value 1 f the PAU is participating to its corresponding
public group, otherwise the feature takes value zero. We
note that we only check whether each PAU belongs to thegroup or not, and do not examine his/her postings in theinterest group. As shown in Table 1, our dataset samples
PAUs from five public groups, so we construct fourfeatures for these groups, namely Pet, Jazz, Trade, andFigure 3. Disclosure rates non-contact profile items andPolitical-enthusiast.

ProfilePhoto
3.4 Types of profile photos
3.2 Activity Indicators

Finally, we introduce observable features on what types

An activity indicator is a numerical feature on howof profile photos are presented in PAUs’ system-generated

much degree a user is active on a specified aspect. We

profile photo albums. In Facebook, users can upload theirconsider the following nine activity indicators that are

profile photo as their icons, and past profile photos areobservable from their public pages: Friends, Followers,

accessible as an album. Based on the tendencies we

Likes, Groups, Music, Photos, ProfilePhotos, CoverPhotos,

observe on user profile photos, we classify profile photos

and Posts.

into the following three types: profile photo with human

Table 6 shows their description, disclosure rates, mean,
maximum,andminimum

values.

We

observehighface(s), profile photo containing pet face(s), and profile photowith non-human/pet (objects).

variability (range between minimum and maximum value)

We denote by PPT ⊆ OF the set of features based onin these activity indicators. The cases of minimum value

the above three profile photo types. Each feature x in PPT

of 0 of an activity indicator can be interpreted as: i) thehas value x = 1 on a PAU, if the PAU shows the

PAU hides the activity indicator value from being publicly

corresponding photo type in one or more profile photos inviewed (e.g. Friends), ii) the PAU does not post regarding

the first five profile photos, otherwise x = 0.

place, blurred photo), then raters should ignore that
3.4.1 Challenges of observing one profile photoprofile photo.

Relying on profile photos of PAUs that are currently
displayedtoobserve

the

above

types

has

severalchallenges:

In this work, we identify one profile photo type per eachphoto. For photos with multiple profile photo types, raters
are given the following instructions: i) if a PAU’s profile(i) PAUs tend to have at least one profile photo, andphoto has background of human/pet and covered with texts,

their profile photo albums contain past profile photos. In

we give priority to the texts and raters should regard theour data set, 96% of PAUs have uploaded at least two

type as profile photo with objects. However, if the face(s) are

profile photos, and as shown in Table 10, the maximum

clearly visible and not covered with texts, then we give

number of one user’s uploaded profile photos is 1146. Topriority to those face(s) that are identified. ii) If theidentify profile photo types, we examine the first five

PAU’s profile photo contains both human and pet faces,

profile photos of each PAU.

then we give priority to pet face(s) and raters should(ii) PAUs tend to upload several different types of
profile photos in their profile photo albums. For example,

regard the type as profile photo containing pet face(s). The
first author also performs the tasks in this step.

one PAU has profile photo of him/herself, with a pet, and(2) Determining the frequency level of each profile

with other people. We record frequencies of each profile

photo type. The frequency level is given as the frequencies

photo type in each album.

of the type found in the first five photos. If there exists a(iii) Profile photos that are uploaded by PAUs tend to

disagreement in the frequencies between the annotators,

have a variety of styles such as face visibility (none,

we adopt the smallest frequency as profile photo type’s

partial, complete), type of photos (colored photos, black

frequency level.

and white, altered colors, have texts), subjects (human(s),(3) Computing agreement statistics. Table 11 showspet(s), human and pet, objects), and facial expressioninter-annotator agreements (IAA), the number of times in(serious, action, posing, normal smile, making a face).

percentage that annotators record the same frequency of

Due to these varieties of styles, annotators (the firstthe profile photo type (Step 1). Also, Table 11 shows theauthor and two raters) independently examine for types ofprobability that annotators agree on the frequency level ofprofile photos for each PAU. By recent image recognition,

each profile photo type. IAA for the type profile photo with

human

be

human face(s) is surprisingly lower compared with that ofautomatically detected. However, to recognize objects inother types of profile photos. That is probably because aprofile photos and to be more accurate in identifying

profile photo might contain a small-sized face, people

human and pet face(s), manual detection of profile photo

took photo in dark place, or people put shades (flag) on

types is necessary.

photo, which make it difficult for all annotators to identify

face(s)andpetface(s)inphotoscanhuman face(s) in that photo. Also wearing costume tends
3.4.2 Manual detection of profile photo types

to change the appearance of a human face in the profile

The following steps are carried out to manually

photo. High probability values in Table 11 indicate that

identifying the profile photo types of PAUs:(1) Raters receive links of the 350 PAUs that are

the annotators mostly agree on the frequency levels ofeach profile photo type of PAUs.

described in Section 3.1, and are instructed to record the(4) Converting the frequency level of each profile photofrequency of each of profile photo type of each PAU.

type in Step 2 to a binary feature. For each PAU, if theRaters are also given the following additional instructions:frequency level of the type is greater than 0, then thei) If the PAU’s profile photo contains more than one

feature has value 1, otherwise 0.

human, then raters should regard the type as profile photowith human face(s), when at least one human face in that

Table 7. Agreement statistics: inter-annotator agreement

photo is clearly identified. Also, if the PAU’s profileand probability of recording true frequency

photo contains more than one pet, then raters should

	
  

	
  

regard the type as profile photo containing pet face(s), when

Type	
  of	
  profile	
  photo	
  

IAA	
  

Containing	
  pet	
  face(s)	
  With	
  human	
  face(s)	
  With	
  objects	
  

0.89	
  
0.64	
  
0.75	
  

at least one pet face in that photo is clearly identified. ii)
If a human or pet face(s) in the PAU’s profile photo is notclearly identified (backside, half face, taken in the dark

Probability	
   of	
   agreement	
  with	
  true	  value	  Annot
Annotat Annot
ator	
  1	
   or	
  2	
  
ator	
  3	
  
0.96	
  
0.95	
  
0.97	
  
0.87	
  
0.90	
  
0.83	
  
0.91	
  
0.93	
  
0.88	
  

observable features (profile items, activity indicators,
3.4.3 Profile photo type results

photos, and groups’ interests).

Table 8 shows the results of Section 3.4.2, Step 2 ondetermining frequency levels of 0 to 5 for each profile
photo type. Number of PAUs is reduced (from N=350 to

5. REFERENCES
1. Joinson, A.: ‘Looking at’, ‘Looking up’, or ‘Keeping up

N=346) because links of four PAUs are no longer

with’

available when annotators are identifying profile photo

Proceedings of SIGCHI conference on Human Factors intypes. Profile photo with human face(s) has the lowest

Computing Systems, pp. 1027-1036. New-York: ACM

frequency level of 0, indicating that majority of PAUs

People?

Motives

and

Uses

of

Facebook.

In:

Press, 2008.

have that profile photo type. In Table 12, the distributions

2. Mohammad, S.M., Zhu, X., Kiritchenko, S., Martin, J.:of profile photo containing pet face(s) and profile photo with

Sentiment, Emotion, Purpose, and Style in Electoral

objects are positively skewed indicating that these profile

Tweets. Information Processing and Management, vol. 51,

photo types are rarely found in PAUs’ profile photopp. 480-499, 2015.

albums.

3. Mvungi, B., Iwaihara, M.: Associations between Privacy,

We further investigate the distribution of number of

Risk Awareness, and Interactive Motivations of Social

profile photo types of PAUs and find that about 45% of

Networking Service Users, and Motivation Prediction from

PAUs have at least two types of profile photos in theirObservable Features. Computers in Human Behavior, vol.

first five profile photos. Such findings reveal that PAUs

44, pp. 20-34, 2015.

are commonly changing profile photo types; therefore, we

4. Spiliotopoulos, T., Oakley, I.: Understanding Motivations

should not adopt a model, which assumes one PAU has afor Facebook Use: Usage Metrics, Network Structure, andsingle profile photo type.

Privacy. In: Proc. the SIGCHI Conference. 3287-3296,
Paris, France, 2013.

Table 8. Distribution of PAUs’ frequency levels of eachprofile photo type (N=346)
Profile photo type
Profile photo containing petface(s)Profile photo with humanface(s)Profile photo with objects

0
279

1
31

2
15

3
11

4
8

5

70

56

55

52

39

74

191

64

34

24

14

19

2

4. CONCLUSION and FUTURE WORKWe

explored

posting

purposesof

PAUsthrough

inspecting their SNS public contents. We manuallyclassified posting purposes based on types of activities
into 19 purpose items and five high-level OP-purposes.
Through statistical analysis, these five OP-purposes wererefined into three high-level OP-purposes. However, therefined purpose model excluded purpose items that are
useful in application such as determining sentiments andemotions of PAUs, among others. Also, we discussed
about observable features that will be used for estimatinglevels of the five OP-purposes for a PAU. We introduced amethod of detecting several types of profile photos of
PAUs in their albums. We found that 45% of PAUs have atleast two out of three types of profile photos. In addition,
we also introduced post-frequency level of 0 to 2 torepresent how much degree a PAU exhibits a particularpurpose item.
As future work, we plan on estimating OP-purposes from