Social Media as a Potential Data Source for Cardiovascular Disease Research [in Indonesia] The purpose of this presentation is to explore how social media platform especially Twitter can be used as a potential data source for cardiovascular disease research in Indonesia. A previous study has used Twitter to access a random sample of approximately 10 billion English-language Tweets originating from US associated with cardiovascular disease. It concluded that Twitter offers promise for studying public communication about cardiovascular disease. In this study, we use Indonesia-language Tweets originating from Indonesia associated with cardiovascular diseases using a keyword “jantung” AND filtered with disease related words, namely “sakit, serangan, gagal, henti”. We describe our findings related to user demographics; content of the Tweets such as risk factors, awareness, and treatment; and volume of Tweets that varied across cardiovascular diseases.

1. SOCIAL MEDIA AS A POTENTIAL
DATA SOURCE FOR
CARDIOVASCULAR DISEASE
RESEARCH [IN INDONESIA]
Ismail Fahmi, Ph.D.
Director Media Kernels Indonesia (Drone Emprit)
Lecturer at the University of Islam Indonesia
Ismail.fahmi@gmail.com
IC-CVD 2021
24 OCTOBER 2021
ACADEMIC
IC-CVD 2021
The 3rd International Conference on Cardiovascular Diseases
23rd – 24th, Oktober 2021

2. ACADEMIC
2
1992 – 1997 Undergraduate, Electrical Engineering, ITB, Indonesia
2003 – 2004 Master, Information Science, University of Groningen, NL
2004 – 2009 Doctor, Information Science, University of Groningen, NL
2009 – Now Engineer at Weborama (Paris/Amsterdam)
2014 – Now Founder PT. Media Kernels Indonesia, a Drone Emprit Company
2015 – Now Consultant at Perpustakaan Nasional, Inisiator Indonesia OneSearch
2017 – Now Lecturer at the IT Magister Program of the Universitas Islam Indonesia
Ismail Fahmi, S.T., M.A., Ph.D.
Ismail.fahmi@gmail.com

3. ACADEMIC
ABSTRACT
Social Media as a Potential Data Source for Cardiovascular Disease Research [in
Indonesia]
The purpose of this presentation is to explore how social media platform especially
Twitter can be used as a potential data source for cardiovascular disease research
in Indonesia.
A previous study has used Twitter to access a random sample of approximately 10
billion English-language Tweets originating from US associated with cardiovascular
disease. It concluded that Twitter offers promise for studying public
communication about cardiovascular disease.
In this study, we use Indonesia-language Tweets originating from Indonesia
associated with cardiovascular diseases using a keyword “jantung” AND filtered
with disease related words, namely “sakit, serangan, gagal, henti”.
We describe our findings related to user demographics; content of the Tweets such
as risk factors, awareness, and treatment; and volume of Tweets that varied across
cardiovascular diseases.
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RESEARCH QUESTION AND METHODS

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QUESTION
Can social media, especially Twitter, be used
as a data source for cardiovascular disease
study in Indonesia?
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METHODS
• A mix-methods study of Twitter data associated with cardiovascular
disease in Bahasa Indonesia.
• Data settings:
• Twitter data from 20th July 2021 – 24th October 2021.
• Keywords: jantung AND
• Filters: sakit OR serangan OR gagal OR henti
• Translation: “heart” AND (“disease” OR ”attack” OR “failure” OR “cardiac
arrest”)
• Analysis:
• Tweet volume and rate (trend).
• Twitter user location, sex, age.
• Content analysis (cardiovascular terms and semantics).
• Social Network Analysis.
• Emotion Analysis
• Sentiment Analysis
• Bot Analysis
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PREVIOUS WORK
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8. ACADEMIC
DRONE EMPRIT ACADEMIC

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DRONE EMPRIT ACADEMIC
HTTPS://DEA.UII.AC.ID
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HOW IT WORKS
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STEPS:
• Registration
• Propose keywords
• Analysis and publication
Dashboard
Access
REQUIREMENTS:
• Publish their analysis for public
using any medium at least 1
publication every 2 months.
USERS
• Students
• Researchers
• Lecturers
• Journalists
• Blogger
• Hoax buster
Admin

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DRONE EMPRIT ACADEMIC DASHBOARD
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DRONE EMPRIT: GOOGLE SCHOLAR
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PUBLICATION IN JOURNAL Q1 /1
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PUBLICATION IN JOURNAL Q1 /2
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PUBLICATION: PERS.DRONEEMPRIT.ID
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BOOKS: READING INDONESIA (1-5)
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SOCIAL MEDIA & DATA EXPLOSION

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DATA GROWTH: UNSTRUCTURED DATA
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BIG DATA – BIG GROWTH
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20

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CRAWLING METHOD

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SYSTEM ARCHITECTURE OF “DRONE
EMPRIT”
Twitter
Facebook
YouTube
Instagram
Online News
Physical Hardware
SOLR Index 1 SOLR Index 2 SOLR Index N
Data Lake Engine
Crawler &
Data
Ingest
Engine
Realtime
Job
Analytics Engine
SQL DB Engine
Scheduled
Job
Advanced
Analytics
Basic
Analytics
API Engine
Access
Control
Data
Advanced
Analytics
Basic
Analytics
UI Engine
Data Sources
Theme
Config
Logo
Features
Hostname
TikTok

23. ACADEMIC
FREE TWITTER API: SEARCH & STREAM
23
Search: 7 days
Start search
Streaming

24. ACADEMIC
LIMITED TO 400 KEYWORDS PER SERVER
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TWITTER
Max 400
keywords
Server
IP Addr 1
Server
IP Addr 2
Server
IP Addr n
Max 400
keywords
Max 400
keywords
DRONE EMPRIT
ACADEMIC
DATA LAKE

25. ACADEMIC
RESULTS

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INITIAL TWEET DATA
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TWITTER USER LOCATION
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TWITTER USER COUNTRIES
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PREPARATION – MANUAL CLEANING UP
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MALAYSIAN TWEETS – SET AS IRRELEVANT
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CLEANED DATA SET

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TWEET DATA
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TWITTER USER - LOCATION
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Total Active Users:
16.279

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ENGAGEMENT TYPES
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TWEETS BY NUMBER OF FOLLOWERS
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USER DISTRIBUTION BY NUMBER OF
FOLLOWERS
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ACCOUNT CREATION DATES
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DEMOGRAPHY ANALYSIS

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40

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GITHUB LIBRARY
41
https://github.com/euagendas/m3inference

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TWITTER USER - AGE
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TWITTER USER - SEX
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TWITTER USER - TYPE
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SOCIAL NETWORK ANALYSIS

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SNA: SOCIAL NETWORK ANALYSIS
• SNA is a mapping of
relationships between people,
organizations, topics, locations,
and other information entities.
• Nodes or points in the network
represent people,
organizations, locations, or
information entities.
• The connection line between
the points describes the
relationship between the points.
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47. ACADEMIC
GRAPH THEORY
• Dyad: smallest unit of SNA (Social Network Analysis)
• Consist of:
• Node
• Link
• Node
47
- node
- vertex
- edge
- link
- relationship

48. ACADEMIC
CENTRALITY
• Centrality is a method to measure power and influence (from
individual/person/node).
• How to measure centrality:
• Degree centrality
• Betweenness centrality
• Closeness centrality
• Eigenvector centrality
• PageRank
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https://cambridge-intelligence.com/social-network-analysis/

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SOCIAL NETWORK ANALYSIS
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Malaysian
INDONESIAN

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TOP INFLUENCERS
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TOP INFLUENCER PROFILES
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CONTENT ANALYSIS

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CARDIOVASCULAR TERMS & SEMANTICS
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CONTENT RELATED TO CARDIOVASCULAR
TERMS & SEMANTIC
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EXAMPLES – MYOCARDIAL INFARCTION
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Translation: After the control was over, the administration mba told me that yesterday the clinic was closed
for a long time, not just because of PPKM, but it turned out that my dentist had a heart attack and had to
put a ring on.
Translation: Let's prepare the heart, liver, lungs, throat, and digestive system well before getting a sudden
heart attack, shortness of breath, dizziness, dizzy eyes, and heartburn at the same time.

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EXAMPLES – CARDIAC ARREST
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Ditpolairud Polda Central Java conducts CPR training to Save Someone's Life CPR is carried out on people
who are unable to breathe or experience cardiac arrest due to something, such as drowning or a heart attack.
By restoring breath function
Dude, have you ever heard of cardiac arrest? This time, Mimin discusses important facts about cardiac arrest
that you should know! Don't forget to share with your friends so #AllHealthy! #AllHealthy #HealthyFacts
#healthy tips #healthy living #hentijantung #cardiacarrest #serangjantung #heart

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EXAMPLES - TREATMENT
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Translation: Reduce the risk of heart disease Mango contains fiber, potassium, and several vitamins that
can keep the arteries of the heart healthy
Translation: What is a Sudden Heart Attack? Know How to Help Someone During an Emergency

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EXAMPLES - SYMPTOM
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Translation: Although you may know that chest pain and shortness of breath are signs of heart disease, it
doesn't mean that everyone with similar symptoms can be sentenced to heart disease, unless you are an
expert.
Translation: Shortness of breath, cough, to chest pain are some signs of heart failure to watch out for.
Recognize other symptoms and their causes.

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EMOTION ANALYSIS

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PLUTCHIK’S WHEEL OF EMOTIONS
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https://www.6seconds.org/2017/04/27/plu
tchiks-model-of-emotions/

61. ACADEMIC
LEXICON BASED ANALYSIS
• JOY
• senang, bahagia, suka, seneng, hepi, menyenangkan, happy,
menggembirakan, gembira, fun, sukacita, riang, ceria, …
• TRUST
• FEAR
• SURPRISE
• SADNESS
• DISGUST
• ANGER
• ANTICIPATION
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EMOTION ANALYSIS
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EXAMPLE OF TWEETS - ANTICIPATION
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EXAMPLE OF TWEETS - SADNESS
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SENTIMENT ANALYSIS

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UNDERSTANDING PUBLIC SENTIMENT
• Sentiment in conversations on social media about a topic can
describe the public's response to an issue, and this can be an
important indicator.
• Sentiment is usually divided into three: positive, negative, and
neutral.
• Every sentiment analysis system must have a certain accuracy,
which is impossible to reach 100% accurate, and depends on the
input data for the training used, and depending on the selected
classification algorithm.
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EVALUATION
67
http://www.sciencedirect.com/science/article/pii/S2090447914000550
"one model for all" can not give
the right label for each subject.
The lexicon base depends on the
presence of the word in the sentiment
dictionary, unable to properly label
different subjects.

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SENTIMENT ANALYSIS RESULT
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EXAMPLE – POSITIVE SENTIMENT
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Translation: I know breakfast makes
the heart healthy, and smoking
makes the heart sick. That's why I
always use cigarettes for breakfast to
keep it balanced
Translation: Wonderful! 5 Benefits of
Bay Leaf for Health, Can Treat Kidney
Stones, Heart Attacks to Cancer —
Indonesian Version

70. ACADEMIC
EXAMPLE – NEGATIVE SENTIMENT
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Translation: A neighbor died of a heart
attack, he died while sleeping N then he
had a baby who was 2 months younger
than my son, his son was crying and
looking for his mother
Translation: Astaghfirullah, the death
rate due to heart attacks is 1600/today,
Covid is about to overtake. Hopefully
the death rate can be reduced.

71. ACADEMIC
BOT ANALYSIS

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HOW IT WORKS
• Botometer is a machine learning algorithm trained to classify an
account as bot or human based on tens of thousands of labeled
examples.
• When you check an account, you fetches its public profile and
hundreds of its public tweets and mentions using the Twitter API.
• This data is passed to the Botometer API, which extracts about
1,200 features to characterize the account's profile, friends, social
network structure, temporal activity patterns, language, and
sentiment.
• Finally, the features are used by various machine learning models
to compute the bot scores.
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METODE
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Botometer
Profile + 200 twits
Bot scores
https://rapidapi.com/OSoMe/api/botometer
API

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BOT ANALYSIS RESULT
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DISCUSSION & CONCLUSIONS

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DISCUSSION
• This study aimed at reproducing previous works on using Twitter as data
source for cardiovascular disease research that was applied to US
conversation data, to Indonesian conversation data.
• Our findings:
• Using cardiovascular disease related keyword and filters in Bahasa Indonesia, we
were able to collect and identified a large enough volume of Indonesia-based
Tweets about cardiovascular disease.
• We were able to identified and cleanup significant enough Tweets from users
located in other countries, especially Malaysia who use similar language; from 42k
Tweets reduced to 19k Tweets.
• We were able to characterize the volume, trend, engagement types, demography
of the senders of the Tweets, location, creation dates, distribution of followers, and
their social network map.
• We were able to analyze the content of the Tweets using sentiment analysis,
emotion analysis, bot analysis, and cardiovascular related terms and semantics.
• From the 16k Twitter users, we were able to analyze 51% of them for the
characteristic of bot; and with 1.52 bot score it indicated the Tweets are natural.
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CONTENT ANALYSIS FINDINGS
• The cardiovascular terms that commonly found in the Tweets are:
myocardial infarction (64%), cardiac arrest (22%), and heart failure
(3%); while the cardiovascular semantics are: treatment (5%),
symptom (1%).
• The most common emotion in the Tweets are: anticipation or
expecting for good condition in the future (2.7k Tweets) and
sadness (365 Tweets).
• The sentiment expressed in the Tweets are mostly negative (59%),
and positive (34%).
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78. ACADEMIC
CONCLUSION
• Twitter can be useful for studying public conversation, opinion,
and emotion about cardiovascular disease in Indonesia.
• Since Bahasa Indonesia and Malaysia are similar, a more thorough
data cleanup is needed to get more accurate data set for
Indonesia.
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79. ACADEMIC
THANK YOU
Ismail Fahmi, PhD.