A Network of Twitter User on Stunting Issue in Lampung, Indonesia

1FENI ROSALIA, 2YULIANTO, 3TINA KARTIKA, 4JENI WULANDARI,

5ALBET MAYDIANTORO

1Department of Government Science, University of Lampung, Bandar Lampung, INDONESIA

2Department of Public Administration, University of Lampung, Bandar Lampung, INDONESIA

3Department of Communication Studies, University of Lampung, Bandar Lampung, NDONESIA

4Department of Business Administration, University of Lampung, Bandar Lampung, INDONESIA

5Department of Social Science Education, University of Lampung, Bandar Lampung, INDONESIA

Abstract: - This study analyzes the network of users and hashtag Twitter messages connected to the stunting

issue in Lampung Province, Indonesia. The research process adopts social network analysis (SNA) methods.

The research data comes from as many as forty thousand tweets from the Twitter API. Data is downloaded,

processed, and analyzed with three R, R Studio, and Gephi. Research findings show that Twitter users linked to

stunting have different: (a) degree scores (13%, 12%, <10%); (b) betweenness scores (4%, 2.67%, 1.07%, and

<1%); and (c) a closeness score that divides the actors into two groups: a closeness score equal to 1 and a

closeness score below 1. The hashtags #pakhalimtahanstunting and #jokowimembangundesa have become

popular hashtags among Twitter users related to stunting issues. This study concludes that central and local

government actors encourage preventive and cross-sectoral intervention even though there are still a few

collaborations between actors and institutions involved in this process.

Keywords: social networking, big data, critically assisted discourse, stunting, Twitter.

Received: May 7, 2022. Revised: September 28, 2022. Accepted: October 30, 2022. Published: November 30, 2022.

1 Introduction

Stunting is one of the national health problems that

are a priority for the Government of Indonesia to be

addressed. This phenomenon is not only a medical

problem but also a socio-cultural, economic, and

political problem. As a socio-cultural problem, some

people still consider stunting solely as an

unavoidable destiny of God. As a financial problem,

stunting cannot be separated from the ability of

household members to provide extra nutritional

support for pregnant women. As a political issue,

the prevalence of stunting shows the low

performance of government agencies in delivering

health services to all citizens. In Indonesia,

according to the Ministry of Health of the Republic

of Indonesia, [1], the proportion of stunting in

children under five in Indonesia has decreased by

7%, from 37.2% (2013) to 30.7% (2018). However,

the trend of stunting in Lampung Province tends to

increase from year to year: 22.7% (2015), 24.8%

(2016), and 31.6% (2017). In 2020, when the

Government of Indonesia paid serious attention to

the problem of stunting, the Provincial Government

of Lampung (GoLP) began to take several policies

related to stunting prevention, starting from the

establishment of the Lampung Stunting Agency

(LSA), the Convergence of Stunting Action, and the

implementation of various specific nutrition

intervention programs. And sensitive. The

government has also set six priority districts/cities

for stunting prevention, namely South Lampung

Regency, East Lampung Regency, Central Lampung

Regency, Tanggamus Regency, North Lampung

Regency, and Pesawaran Regency.

Stunting is a form of malnutrition phenomenon.

In simple terms, malnutrition is an abnormal

physiological condition caused by inadequate,

unbalanced consumption of macronutrients,

micronutrients, or both. Malnutrition includes

undernutrition (for example, stunting), overnutrition

(for example, obesity), and micronutrient

deficiencies. Malnutrition is caused by many

factors: biological, socio-economic, and

environmental factors, [2]. Stunting is not an

individual health problem. It is a public health

problem that the government must intervene in

because it interferes with the process of human

regeneration and the quality of the future of the

nation. Stunting is not only about the availability

and access to nutritious food due to many factors

contributing to stunting reduction, for example,

economic growth, quality of health services, poverty

alleviation, and pro-poor government programs, [3].

There has been a lot of research on stunting

from the health aspect. Still, not much research has

been done by analyzing user networks and hashtags

on Twitter messages related to the issue of stunting

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Feni Rosalia, Yulianto, Tina Kartika,

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in Lampung, Indonesia. So this research is quite

essential for the government for policy making.

2 Literature Review

2.1 Stunting

Stunting is a wicked problem. In terms of health, it

is related to knowledge and healthy living behavior

among the population. From the agricultural side, it

relates to the availability and access to quality food.

From a social policy perspective, it is related to

efforts to protect the most vulnerable groups from

starvation and lack of nutrition in the community

due to various market fluctuations. From the aspect

of national resilience, stunting will weaken the

nation's resilience because it will give birth to a

stunted generation with limited physical abilities.

Due to its complex nature, it is not surprising that

several studies have recommended that stunting be

responded to with a collaborative approach, [4], [5],

[6], [7].

So far, the stunting phenomenon has been

mostly analyzed from a health and socio-economic

point of view, both the causes and impacts. Several

factors that researchers consider as contributing to

the decrease/increase in stunting are income,

parental occupation, age of the baby, gender, [ 8 ] ,

Community Health Development Index, [9],

household poverty [10], [11], meat consumption

patterns [12], education level and location of

residence [13], sanitation and drinking water quality

[14], government social assistance programs [15],

family food security [16], family welfare levels

[17], economic growth, poverty alleviation

programs, and programs social insurance [3],

knowledge level of pregnant women, [18], [19], [20].

These various research results confirm the findings

of a systematic review on stunting conducted by [5].

2.2 Social Network Analysis (SNA)

SNA is a quantitative social science approach

influenced by graph theory belonging to

mathematics. SNA starts from the idea that social

life contains various social relations and forms

certain patterns. These patterns then affect social

life, both at the individual, group, and community

levels, [21]. The term social network in the acronym

of SNA can be defined as a group of actors

(nodes/vertices/points) connected to one or more

actors because of certain social relations, [22].

In the SNA method, relation/interaction

(linkage/tie/edge) is a fundamental concept to

explain various processes that occur in social life.

This is because the distribution of resources, both

goods (material) and services (non-material), in

social life, is channeled through certain social

relations. Consequently, the network structure or

network of social relations that a person has will

give birth to opportunities, constraints, challenges,

and obstacles to individual or group action. In this

situation, the actor and the actor's actions are

inseparable or interdependent on each other. While

the term structure refers to the patterns of interaction

between actors that have recently occurred or are the

most recent, [22].

To explain the relationship or structure of social

networks, SNA has some special jargon. The most

basic, of course, are dyads (relationships between

two actors), triads (relationships between three

actors), sub-groups (relationships of a group of

actors in the form of dyads or triads), and groups

(social networks that have relationships in the form

of dyads, triads, and subgroups). Furthermore, when

there are a group of actors interacting with each

other, SNA borrows several terms developed by

graph theory, for example, degree (number of

relationships formed between actors), density

(proportion of relationships that may be formed with

relationships that have already been created),

distance (distance between two actors), geodesic

distance (the shortest distance between two actors),

eccentricity (the actor with the largest geodesic

distance), and so on, [22].

The relationship between actors can be directed

or undirected. A relationship is directed if the

relationship has an orientation (for example, actor A

chooses actor B as a friend). Meanwhile, the

relationship is undirected if the relationship formed

is dichotomous: it may or may not exist (for

example, joint membership in an organization). To

measure and identify actors who have important

roles in various relationships in social networks,

SNA has several jargons: closeness or distance (how

close the actor is to other actors), betweenness (the

position of the actor between two actors),

prestigious (actors who receive many relations

indegree receiving relations from other actors), and

outdegree (sending relations to other actors),

centrality (number of actor relations in the network),

[22].

3 Method

The study adopted a quantitative approach,

particularly the SNA method. The research will be

carried out in Indonesia to utilize big data

downloaded from the Twitter API. The data

download process uses the R, [23], and R Studio

software, specifically the academic twitter R

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package, [24]. The data analysis and visualization

process use the quanteda package, one of the R

packages developed by, [25]. While the calculation

of user network structure attributes and hashtags

will use the Gephi software, [26]. While the

calculation of user network structure attributes and

hashtags will use the Gephi software, [25].

Interpreting the structural attributes of user networks

and hashtags will refer to the jargon of SNA

methods, [22]. The research focuses on calculating

and interpreting the centrality, closeness, and

betweenness of the Twitter user network and

hashtags containing the keywords ‘stunting’ and

‘Lampung.’

4 Result

4.1 The Setting

According to the Central Statistics Agency of

Lampung Province, [27], Lampung is a province

with an area of 35,288.35 km2. Geographically,

Lampung Province is located between 103°40' –

105°50' East Longitude and 6°45' – 3°45' South

Latitude. In 2019, the population of Lampung

Province reached 8.4 million people or 2.1 million

households and spread across 15 districts/cities. The

Golkar was led by Arenal Djunaidi (Governor) and

Chusnunia Chalim (Vice Governor), who won the

2018 Lampung Province regional head election

supported by the Golkar Party (Golkar), the

National Mandate Party (PAN), and the National

Awakening Party (PKB). Golkar has 16,381 civil

servants across 18 offices, seven agencies, and two

secretariats.

The GoLP has chosen the Lampung Provincial

Health Office (LPHO) as the leading sector to

respond to various health issues in Lampung

Province, including stunting. Referring to the

Lampung Governor Regulation Number 32/2010

concerning the Main Duties and Functions of the

LPHO, it has the main tasks: carrying out provincial

government affairs in the health sector based on the

principle of autonomy which is the authority, de-

concentration tasks, and assistance tasks given by

the central government to the governor and other

duties in accordance with the policies set by the

governor based on the applicable laws and

regulations. These tasks are translated into several

functions: (a) formulation of provincial-scale health

policies, regulation, planning and setting

standards/guidelines; (b) management and

recommendation of technical considerations for

permits for special health facilities and

infrastructure such as mental hospitals, leprosy

hospitals and cancer hospitals; (c) implementation

of health and nutrition technology certification; (e)

implementation of epidemiological surveillance and

prevention of outbreaks of communicable and non-

communicable diseases and extraordinary events; (f)

strategic placement of health personnel, transfer of

certain health personnel between districts/cities as

well as provision of health personnel education and

training; (g) fostering, controlling, supervising and

coordinating the health sector; (h) implementation

of health efforts on a provincial scale and which

cannot be carried out by regencies/municipalities; (i)

administrative services; and (j) the implementation

of other tasks assigned by the governor in

accordance with his duties and functions.

The limitations of the main tasks and functions

become the legal basis for the LPHO to formulate

policies and development programs in the health

sector. These policies and programs are based on the

identification of several main health issues in

Lampung Province: (a) high maternal and infant

mortality rates; (b) improving the nutritional quality

of the community as a whole; (c) still high

morbidity and mortality due to communicable and

non-communicable diseases; (d) limited access and

quality of health services; and (e) the low level of

clean and healthy living behavior of the community

members. Starting from the situation above,

development policies in the health sector in

Lampung Province are focused on several issues

such as (a) improving health efforts; (b)

guaranteeing health financing; (c) developing

human health resources; (d) guaranteeing

pharmaceutical preparations, medical devices, and

food; (e) developing health management,

information, and regulation; and (f) increasing

community empowerment in the health sector.

4.2 Description of Data

This study uses Twitter API data as the main data.

Twitter data is searched and downloaded using the

academic twitter R package using the keywords

“stunting” AND “lampung” with the following

additional criteria: (a) tweets are the result of

retweets; (b) include promoted tweets; (c) the tweet

must have a link in the form of a URL; (d) tweets

must be in Indonesian; (e) tweets must be posted

within the period 2010 - 2020. In the first stage of

the search, the researcher got 376,398

observations/tweets. After being cleared of duplicate

tweets, 46,471 observations/tweets remain.

The total tweet above contains 7,046 hashtags.

Of this number, there are several dominant hashtags:

#pakhalimcegahstunting, #bmkg, #lampung,

#jokowimembangundesa, #onehealthkipm, #repost.

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Meanwhile, the total number of Twitter accounts

involved in stunting data reached 15,574 accounts.

However, there are only a few accounts whose main

Twitter users stand out (frequency of occurrence

above 50% of the total tweets collected), and are

popular and connected to stunting tweets, namely:

@lampung_utara, @jokowi, @putrilailawati,

@se_lampung, @tribunlampung_,

@daily_momentum. Through the social media

Twitter, researchers tracked the existence of

information about these accounts. The results of this

tracking are presented in Table 1, which confirms

that many of these accounts are accounts belonging

to conventional mass media, except for the accounts

of @jokowi and @putrilailawati.

Table 1. Short profile of Twitter account that related to stunting issue in Lampung Province

Twitter account

Short profile

@lampung_utara

Give information about North Lampung. Official email:

saibumiruwaijurai@gmail.com. Join Twitter in 2011.

Following 211 people dan followed by 2.025 Twitter users.

This account does not have a verified sign by Twitter

Corporation.

@jokowi

The official Twitter account of the President of the Republic

of Indonesia, Joko Widodo. Following 58 people and

followed by 16.1 million followers. Joined Twitter in 2011. It

has one hashtag: #MenujuIndonesiaMaju. The account is

marked verified by Twitter.

@putrilailawati

The account owner is Uti, located in Pringsewu District, and

she joined Twitter in 2012. Following 588 people and

followed by 3,676 Twitter users. Haven't gotten a verified

check from Twitter yet.

@tribunlampung_

The official account of the daily newspaper

Tribunlampung.co.id. Joined Twitter in 2010. Following 393

people and followed by 19.6 thousand followers. The official

website is located at www.tribunlampung.co.id. The account

has not yet received a verified check from Twitter.

@se_lampung

The account #semesterlampung has joined Twitter since 2017

with the main activity of “sharing info about tourism, culture,

local wisdom, personal, culinary, development, and lifestyle

throughout Lampung. Following 501 people and followed by

1,074 people. Geographical location is in Lampung,

Indonesia.

@harian_momentum

Momentum Daily's official account. They joined Twitter in

2017 and are in Bandar Lampung City. Following 257 people

and followed by 343 followers. Has an official web page at

www.harianmomentum.com. The account has not yet

received a verified sign from Twitter.

Indicators can be described as follows: first, as

shown in Figure 1, in terms of the degree indicator,

the actor with the largest degree score (13%) is the

green actor (node) (@shintapuspitad, @infoseni_,

@lampung_utara, @ecacamarica_, and

@putrilailawati) and pink (@fachrilabado,

@taufikmadjid71, @anwsanusi, and @jokowi). The

second position is occupied by actors who have a

degree score of 12% (emerald-colored nodes:

@feby_maya_sari and @nyimaswulandari). The

third position is occupied by the accounts

@nunung_unuy15 and @riskyagustinaa, with a

degree of 10. The rest are nodes that have a degree

below 10%. This finding indicates the limited role

of the GoLP (@pmd_lampung, @humaslampung_,

@hi_hermanhn) in influencing network users.

However, this network shows the significant role of

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online media and citizens in promoting stunting

issues on social media platforms.

Fig. 1: The Pattern of Twitter network users based

on degree indicator

Second, based on the betweenness score, there

is only one actor (node) who has the largest score

(6%), namely @pmd_lampung (pink), even though

it only reaches a population of 4% of the total

relations (edge) in the network. Light green actors

occupy the second position with 2.67%. The green

node population reaches 20.83% of the network's

total relations (edges). The third position is occupied

by dark green actors (nodes) by 1.07%. The rest are

nodes that have a betweenness score below 1

percent. Because the @pmd_lampung account

represents the GoLP institution, this finding

emphasizes the important role of the government in

the network of actors involved in the stunting issue.

The @pmd_lampung account is owned by the

Lampung Province of Village Community

Empowerment and Transmigration Office Lampung

Province (LPVCETO). It is very surprising that

LPVCETO appears in the stunting network instead

of the LPHO. As shown above, LPHO has been

chosen by the GoLP as a leading sector to combat

stunting in Lampung Province. In Figure 1, we do

not find the MoHRI as a member of the stunting

network.

Fig. 2: The Pattern of Twitter network users based

on betweenness indicator

Based on the closeness score, the 24 (twenty-

four) actors in Figure 3 can be grouped into two

categories: (a) actors who have a closeness score of

1 or pink nodes (@srikandilpg, @hi_hermanhn,

@infoseni_, @shintapuspitad, @putrilailawati,

@lampung_utara, and @ecacamarica_) and (b)

actors who have closeness scores below 1 (other

than pink). Like Figure 1, this network stresses the

roles of the non-government actor in the stunting

network. Based on Figure 3, we can interpret that

there is strong citizen participation in promoting

stunting issues on social media, especially on

Twitter, in Lampung Province. However, three sub-

group in this network are not fully integrated. We

did not find an edge connecting the three subgroups

in the stunting network. It means there is no

collaboration between government, market, and civil

society in stunting prevention in Lampung Province.

Fig. 3: The Pattern of Twitter network users based

on closeness indicator

4.3 Hashtag Network

Figure 4 shows some of the hashtags commonly

used by Twitter users related to the issue of stunting.

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Figure 6 only hashtags with a minimum frequency

of 5 percent of the total existing hashtags. The wider

the blue line connecting the two hashtags, the higher

the frequency of that hashtag's traffic on Twitter.

This means that the intensity of users campaigning

for this hashtag is getting higher. When viewed with

this technique, even though the issue of stunting is

considered a health domain, what appears as the

most popular actor on Twitter is

#pakhalimcegahstunting which refers to Abdul

Halim Iskandar (Ministry of Villages, Development

of Disadvantaged Regions and Transmigration of

the Republic of Indonesia/MoVDDRT). The next

hashtag that is also popular is

#jokowimembangundesa which is often used by the

official Twitter account of the Presidential Staff

Office in their tweets. This finding confirms the

government's attitude to adopt a multi-sectoral

strategy to prevent and combat stunting. Meanwhile,

the hashtag #lampung is quite connected to the

hashtag network related to village funds, although

the frequency is not too high. This can be seen from

the blue line connecting the hashtag #lampung with

several hashtags connected to village funds, which

are not too wide. At the regional level, this hashtag

is quite popular and has the highest frequency, and

is connected to various hashtags that have regional

identities, for example, #tribunlampung (local

media) and #bandarlampung.

Fig. 4: Network of hashtags linked to the words

“stunting” and “lampung” on Twitter

5 Discussion

Although previous research had recommended a

collaborative approach to prevent stunting [1, 11,

15], it does not happen in Lampung Province.

Because stunting is a health problem, we presume

the MoHRI is a dominant actor in the stunting

network. However, our estimate is wrong because

the most popular actor is the MoVDDRT and its

organization network at the district level. We have

three interpretations of this finding. First, it shows a

paradigm shift, from sectoral-centered (health

sector) to territorial-centered (village based), in the

GoI to prevent stunting. Until now, the rural area is

still perceived as underdeveloped and the

epicentrum of various social deprivation in

Indonesia. Second, it is part of the GoI strategy to

control the utility of village funds and keep it

aligned with development priorities and programs

set by the GoI. Third, the MoVDDRT is a

newcomer in stunting policy. She uses Twitter to

increase public awareness and mobilize digital

public support in preventing stunting.

6 Conclusion

Stunting is one of the wicked problems in Lampung

Province. The GoI and GoLP have started

implementing various policies to prevent stunting.

This study concludes that central and local

government actors encourage preventive and cross-

sectoral intervention even though there is still little

collaboration between actors and institutions

involved in this process. Many citizens, as personal

Twitter users or online media, have participated in

stunting discourse on social media. But their roles

are not integrated into the network that contains

government actors. The popularity of

#pakhalimtahanstunting and

#jokowimembangundesa as hashtags among Twitter

users is strong evidence for stunting policy as a

product of the technocratic and top-down process.

This research recommends the central and local

governments revitalize the institution of

collaboration in stunting prevention.

Acknowledgement:

This research is fully funded by the Rector of

Lampung University based on contract number

1502/UN26.21/PN/2021, 21 April 2021.

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Jeni Wulandari, Albet Maydiantoro

E-ISSN: 2224-3496

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WSEAS TRANSACTIONS on ENVIRONMENT and DEVELOPMENT

DOI: 10.37394/232015.2022.18.118

Feni Rosalia, Yulianto, Tina Kartika,

Jeni Wulandari, Albet Maydiantoro

E-ISSN: 2224-3496

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