ScienceDirect

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Procedia Computer Science 124 (2017) 100–107

1877-0509

2018 The Authors. Published by Elsevier B.V.

Peer-review under responsibility of the scientific committee of the 4th Information Systems International Conference 2017

10.1016/j.procs.2017.12.135

4th Information Systems International Conference 2017, ISICO 2017, 6-8 November 2017, Bali,

Indonesia

Teenstagram TimeFrame

: A Visualization for Instagram Time Dataset

from Teen Users (Case Study in Surabaya, Indonesia)

Irmasari Hafidz*, Alvin Rahman Kautsar, Tetha Valianta, Nur Aini Rakhmawati

Department of Information Systems, Institut Teknologi Sepuluh Nopember, Kampus ITS, Surabaya, 60111, Indonesia

Abstract

The aim of this study is to create Teenstagram, a visualization for online pattern activity using Instagram dataset from teen users (junior

high school, 7

- 9

grade) in Surabaya, Indonesia. First, an offline workshop about ethics using Internet and social media for 18 junior

high schools in Surabaya were conducted about three weeks, from 3

until 26

October 2016. Second, we create Teenstagram, by

building a web application to visualize and analyze the pattern activity from teen users using Instagram. We get the 290 Instagram

users account from 579 students who fill in the survey from the first stage of the research. We employ K-Modes using R to cluster the

dataset with six categorical features; online type activity (like, comment follow), days in the week (Monday – Sunday), hour (00-23),

student activity (study time, rest time, school time), type of school (public and private activity), and sex (male, female). We propose a

tool for analyzing Instagram dataset for online time activity, this result reveals the time pattern from the teen users using social media

(e.g. Instagram) and what are the characteristics of each pattern has.

Peer-review under responsibility of the scientific committee of the 4th Information Systems International Conference 2017.

Keywords:

Instagram; K-Modes; R; Teen; Online Behavior; Social Media; Visualization; Categorical Attributes

Introduction

The internet and social media are now an inseparable way of life from human being, from six month old-baby [1] until

senior users age 65 years old and older [2]. An article from TIME reported that baby as early as 6-month year old now

mostly have access to mobile devices; 65% their parents uses these mobile devices to calm their kids and 29% put their

children to sleep. The American Association of Pediatrics (AAP) stated in the article that the excessive used of Internet

* Corresponding author. Tel.:+6231-5999-944; Fax.: +62-31-5964-965.

E-mail address:

irma@is.its.ac.id

Irmasari HafIDz et al. / Procedia Computer Science 124 (2017) 100–107

101

later could contribute to school trouble, attention problems and obesity [3]. AAP also warns that Internet and cell-phone

use can be a platform that lead to risky behavior.

According to survey conducted by APJII (Association of Indonesian Internet Service Providers), from 132.7 million

user of internet in Indonesia, 18.4 percent or 24.4 million are teen users [4]. According to this survey, there are two

platforms in social media which popular among the users (all age), Facebook (71,6 million users) and Instagram (19,9

million users). Even though Mark Zuckerberg still has its charm, teen users are now swifting from Facebook to Snapchat

and Instagram, one article stated that “

Teenagers really do think Facebook is less cool than Instagram and Snapchat

” [5].

This paper study about how teen users use Instagram in their daily life. We create a web application called

Teenstagram. For this paper, we propose a framework for online pattern activity using Instagram dataset from teen users.

We visit 18 junior high school in Surabaya, Indonesia and deliver questionnaires, which later become the main input for

the analysis for clustering and visualization for the Instagram time and online activity.

Literature Review

In this section, we describe Instagram and related literature about social media and Internet usage. Then, we explain

the K-Modes algorithm that is used to cluster the dataset and create pattern analysis from online time activity of Instagram

users.

2.1.

Instagram

Instagram is an online social media platform founded by Kevin Systrom (CEO, co-founder) and Mike Krieger (CTO,

co-founder), that allows its users to capture and share variety moments into photos or video easily [6]. By using Instagram

APIs, we need to understand and read the company legal terms that available on Instagram sites [7]. Right now, there are

36 General Terms (as per July 2017), this statement can be renewable any time in the future.

Studies shown many benefit for Internet usage among life-learning ability [8], for example greater skills to do Math.

But there are consequences that could damage health risk in children and pre-school [9, 10] as well as teen users [11].

Not only increase health problem, but the excessive media usage could also damage personal and create more

psychological problems [12]. For example, deficient social skills and loneliness are important factors that could lead to

impulsive and high or strong Internet behaviors, which resulted in negatives life outcomes, such as: less activity in direct

significant relationship and distract the school activity [12].

2.2.

K-Modes Algorithm

K-Modes algorithm is the modification of K-Means clustering from MacQueen in 1967 [13], the algorithm replaces

the means (average of points on a cluster) with the modes. K-Modes simply records or count which characteristic or

values mostly found (the modes) on a specific feature or attribute. Similar with K-means, the algorithm will ask the initial

number of K (number of cluster defined) and will record the distance between instances and the center of a cluster K.

A study from Huang [14] used K-Modes for 34 categorical features from half million Soybean dataset (500000 records)

resulted in higher scalability because the algorithm needs many less iterations to converge. We employ the package from

R, using packages called

kmodes

and calculate the number of

withindiff

or the distance calculated by simple-matching

algorithm within cluster for each cluster [15]. The similarity measurement defined as the calculation of the distance point.

The K-Modes algorithm is frequency based and update the centroid of cluster K based on iterative object cluster

assignment [16].

Research Methodologies

We conduct two research stages. First, we conduct an offline survey and workshop about ethics using Internet and

social media for 18 junior high school in Surabaya. During the workshop we also delivered questionnaires through the

student or participants. The offline survey to 18 junior high school had been conducted in three weeks from October 3

– 26

, 2016. We delivered questionnaires during this workshop and consent form about how many social media account

they have, how much time do they spent online on a day, etc. We successfully delivered 590 offline questionnaires

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Irmasari HafIDz et al. / Procedia Computer Science 124 (2017) 100–107

through the students and validated 290 Instagram account from the feedback. We validate the student names with each

Instagram account manually, and add sex features (annotated to each account whether she/he is female/ male). This is

important for the second stage of research since sex is an important feature for the analysis. Secondly, we create a website

application to show the pattern and statistical distribution from Instagram dataset. We use Instagram API and crawl the

account for about one month from April, 23

to May 18

2017. The research stages is shown in Fig. 1. Table 1 shows

the timeline for our two research stages. Table 2 shows the six features used for the analysis and value from each features.

Fig. 1. Research methodology for teenstagram; time frame visualization.

Table 1. Research stages conducted in

Teenstagram

Research

Stages

Period

Type of

Research Stage

Note

stage -

Offline Survey

October 3

–

, 2016

Offline Survey

An offline visit and survey to 18 junior high school (7

– 9

grade) in Surabaya,

Indonesia. This survey is conducted by undergraduate students & lecturer from

Dept. of Information Systems for a course named

Etika Profesi

(Ethics in

Information Systems). We delivered 590 offline questionnaires & validate 290

Instagram user account. This survey has another result, a book titled

Cendekia

Dengan Smartphone

[17].

stage -

Offline Survey

May, 9

– 17

2017

Offline Survey

(feature: type

of activity)

We conduct an offline interview to 54 students from 18 junior high school (3

students from each schools). We ask a survey for an added feature: type of activity

(3 types). The only question being asked is usually at what time of the day will

you (1) go to school, (2) to study or (3) take a rest or sleep.

stage -

Crawling

Instagram

dataset (using

its API)

April, 23

May 18

2017

Crawling

Instagram

users log

activity &

framework for

Teenstagram

visualization

We crawl the 290 annotated Instagram account (added sex feature: male, female)

and resulted in 200209 online activities from those account. From this stage we

also got the log activity, from page You and Following from Instagram feature.

Three types of activities an Instagram user can do: like, comment & follow, as

well as each time dataset; when he/she liking, commenting or following someone

else’s post in Instagram. We used a

cronjob

and scheduled it in every hour to

crawl the Instagram dataset and put them into MySQL database. The output from

this stage is shown in Appendix A.1.

There are six features from our datasets; they are online type activity (like, comment follow), days in the week (Monday

– Sunday), hour (00-23), type of activity (study time, rest time, school time), type of school (public and private activity),

and sex (male, female). We convert all categorical in number (integer data type), the values of each features are explained

in Table 2.

Table 2. Six categorical features from

Teenstagram

dataset.

Feature Name

Type of Feature

Values

Type of school

Categorical

Public school = 1, Private School = 2

MySQL

Teenstagram:

TimeFrame Visualization

Package:

K-Modes

Irmasari HafIDz et al. / Procedia Computer Science 124 (2017) 100–107

103

Feature Name

Type of Feature

Values

Type of online

activity*

Categorical

Like = 1, Comment = 2, Follow = 3

Days in the Week

Categorical

Sunday = 1, Monday = 2, Tuesday = 3,

Wednesday = 4, Friday = 6, Saturday = 7

Hour*

Categorical

00:00 – 00:59 = 0, 01:00 – 01:59 = 1, … ,

22:00 – 22:59 = 22, 23:00 – 23:59 = 23

Type of activity

Categorical

School time = 1, Study time = 2, Rest/ sleeping

time = 3

Sex

Categorical

Girl/ Female = 0, Boy = Male = 1

3.1.

K-Modes Clustering

We have 200209 records of log activity from Instagram dataset. From the initial account, there are 108 boy or male’s

account (37%) and 182 girl or female’s account (63%) out of 290 Instagram accounts; 63 students are from private school,

whereas the rest of it, 227 students (78%) go to public schools. There are five original datasets from Instagram API, they

are:

date, hour-minutes-seconds, time-zone, id-account-instagram, type of online activity

. This is shown in Fig.4 in

Appendix A.1. The features that being used in our clustering are type of online activity* and hour*, together with the

other four features. The other four features: type of school, days in the week, type of activity and sex are being annotated

using code.

3.2.

K-Modes Result

We define the K in K-Modes from K=3 to 7. We employ packages called

kmodes

in R and set the iterations = 10. We

also set the weighted = FALSE or in other words, we simply used the usual simple-matching distance between objects.

Table 3 shows the size of each cluster from K=3 to 7. Table 4 and 5 shows the

withindiff

and

total-withindiff

function of

kmodes

package from K=3 to 7.

Table 3. Size of K=3 to 7 using

kmodes

R package from

Teenstagram

dataset.

size K

K = 3

16507

11193

10308

K = 4

16488

9658

1619

10243

K = 5

15703

7953

5507

6383

2456

K = 6

9682

11878

6543

1334

3340

5231

K = 7

16649

6871

6174

3515

957

1186

2656

Table 4.

Withindiff

of K=3 to 7 using

kmodes

R package from

Teenstagram

dataset.

Withindiff

from K

K = 3

37416

19628

16443

K = 4

34939

15117

3668

16158

K = 5

27852

13292

7157

11441

4090

K = 6

15079

19855

10430

2821

3400

7515

K = 7

28604

11200

9082

4083

1433

2403

1759

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Irmasari HafIDz et al. / Procedia Computer Science 124 (2017) 100–107

Table 5.

Total-Withindiff

of K=3 to 7 using

kmodes

R package from

Teenstagram

dataset.

Total-Withindiff

from K

Average

K = 3

1.718061

1.630039

1.471007

1.871813

K = 4

1.557426

1.671578

1.59407

1.161593

1.881838

K = 5

1.773674

1.671319

1.299619

2.114693

1.497388

1.640131

K = 6

2.119056

1.565231

2.265596

1.790734

1.017964

2.026138

1.565393

K = 7

2.266675

1.753596

1.595169

1.577468

1.665309

1.436628

0.662274

1.452357

We choose K= 3 and run the algorithm again with the iteration = 15 and parameter weighted = FALSE. We then

analyze the pattern of three clusters. Fig. 2 shows the result from R code, what are the modes from each features from

cluster K=3. Table 6 and 7 shows the result and analysis of clusters pattern among three clusters (K=1,2,3).

Fig. 2. Codes from Modes from K=3 from Teenstagram dataset using

kmodes

package in R, iteration = 15, weighted = FALSE.

Table 6. Result of each modes from

Teenstagram

dataset, K=1 to 3 using

kmodes

R package, iteration = 15, weighted = FALSE.

Cluster

Type of Online

Activity

Days in The Week

Hour

Type of

Activity

Type of School

Sex

Table 7. Analysis of each mode from

Teenstagram

dataset, K=1 to 3 using

kmodes

R package, iteration = 15, weighted = FALSE.

K=1

K=2

K=3

Cluster 1 has the characteristic of the

modes of each features or attribute as

follows:

Type of Online Activity:

Days in the Week: Tuesday

Hour: 15:00 (Time zone:

Jakarta/Asia)

Type of activity: school time

Type of school: Public school

Sex: Girl or Female

Cluster 2 has the characteristic of the

modes of each features or attribute as

follows:

Type of Online Activity:

Days in the Week: Wednesday

Hour: 14:00 (Time zone:

Jakarta/Asia)

Type of activity: rest or sleep

time

Type of school: Public school

Sex: Girl or Female

Cluster 3 has the characteristic of the

modes of each features or attribute as

follows:

Type of Online Activity:

Days in the Week: Sunday

Hour: 08:00 (Time zone:

Jakarta/Asia)

Type of activity: rest or

sleep time

Type of school: Public

school

Sex: Girl or Female

3.3.

Analysis of K-Modes Algorithm with K=3

From Table 6, we can draw the preliminary analysis from K=3 using K-Modes algorithm. There are three types of

clusters among the teen. From the Cluster 1 and 2, the majority of each attributes mostly the same, they tend to follow

ctx <- kmodes(tx, 3, iter.max = 15, weighted = FALSE)

Irmasari HafIDz et al. / Procedia Computer Science 124 (2017) 100–107

105

people and they are from Public schools. The different values are for attribute the Days in the Week (Cluster 1= Tuesday,

Cluster 2=Wednesday), Type of activity (Cluster 1= school time, Cluster 2=rest or sleep time). The hour in the day from

both Cluster 1 and Cluster 2 are almost the same, it is during 14:00 – 15:00. Surprisingly from all the clusters, the girls

or females have the highest number of engagement compare to the boys or males. In Cluster 3, the type of activity that

has the highest modes are like and it is on Sunday, which is clearly a rest time for all students and it is mostly happen

during 8:00 in the morning.

3.4.

Visualization for Teenstagram TimeFrame

We build a web application using Xampp 3.2.2 and PHP 5.6.28. The database being used is MySQL. The web page

of the Teenstagram TimeFrame Visualization is available at http://bit.ly/2xKv5l1. We visualize the distribution of

between features. Figure 3 shows the TimeFrame Visualization of Instagram towards the day of the week. This chart

reveals that there is still activity from 21:00 until 06:00 in the morning, the time when all pupils should sleep or take the

rest.

Fig. 3. TimeFrame Visualization of Teenstagram; The Number of Activities (the sum of activities from like, comment and follow) vs. Hour during the

day. The green streamgraph shows the activities from all day in the week. The grey streamgraph shows the activities from school day of the week.

This paper only discuss about the Time Frame tab (the blue box, from Fig. 3, Fig.5, Fig.6,.). The other three tab consist of TeenStagram, Caption

Frame & Processing Data Caption (the orange box) are described in this link https://goo.gl/tzbNQD.

4. Conclusion

Our experiments and visualization shows that the students are indeed using their rest and sleep time for Internet and

browsing through Instagram dataset. The online activity of 290 accounts reveals what are the exact time, the students go

online and the analysis of everyday activity from Monday through Sunday. From the visualization (Fig. 3, Fig.5, Fig. 6

and the rest of it can be seen in the web app), it shows that females students are more active than males students. Even

though, the number of female’s account is larger than the male’s account from the beginning of the studies (see 3.1), the

gap of online activity between male’s (7%) and female’s (93%) is large. There are studies about digital and internet

exposure that lead the users especially children, pre-school that will affect their sleeping habit resulted in fewer minutes

sleep per night [9] and later bedtime media use and increase view of violent content [10]. Not only for child and pre-

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Irmasari HafIDz et al. / Procedia Computer Science 124 (2017) 100–107

school students, this phenomenon is also happening to the young adults or adolescents people; studies found that these

young adults with higher social media use will have greater risks of sleep disturbances [11]. Although other study stated

that Internet promotes greater ability to learn, for example in Math [12] by using an app and increase creativity in children,

the Internet and social media usage shows an alarming pattern that should be taken into account, especially for parents

and teachers in school. There are lots of improvements for future research such as the validation from the number of K

being chosen in K-Modes algorithm. Other algorithms can be taken into account, for example: Fuzzy K-Modes or K-

Prototype to compare and seek what parameters need to be change in order to increase the performance of the chosen

algorithm.

Acknowledgements

This research is being conducted and was supported by funding from Lembaga Penelitian dan Pengabdian kepada

Masyarakat, Institut Teknologi Sepuluh Nopember (LPPM - ITS) and Kementrian Riset, Teknologi, dan Pendidikan

Tinggi (or Ministry of Higher Education Indonesia) with the scheme of

Penelitian Dosen Pemula

and the grant number

Surat Perjanjian Penelitian No: 817/PKS/ITS/2017

Appendix A. Teenstagram TimeFrame Visualization

The Appendix A has many section and figures which provides graphs and tables from Teenstagram TimeFrame

Visualization study. The other data and graph from this study is available at: https://goo.gl/tzbNQD.

A.1. Instagram dataset - Crawling Output

Fig. 4. Crawling output of time dataset from Teenstagram.

A.2. Teenstagram Visualization: Boy and Girl vs. Hour during the Day

Fig. 5. TimeFrame visualization of Teenstagram; The number of activities (the sum of activities from like, comment and follow) vs. hour during the

day. The orange streamgraph shows the activities only from boys or male students. The graph shows that boys activity peaked at 14:00 during the day

and has the maximum number of activity of approximately at 110 activities.

Irmasari HafIDz et al. / Procedia Computer Science 124 (2017) 100–107

107

Fig. 6. TimeFrame Visualization of Teenstagram; The Number of Activities (the sum of activities from like, comment and follow) vs. Hour during the

day. The blue graph shows for girls and has the scale of the max number activity almost reach 4000 and peaked at 15:00.

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Cendekia Dengan Smartphone

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602-73429-1-0

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