The spread of fake news can amplify the social impacts and consequences of a terror attack on the local community. However, research on the social impact of fake news after a terror incident is not well-studied in the Southeast Asia (SEA) region. To address this gap, this study describes an approach taken to study the social impact of fake news on the local community after a terror attack between 2016-2018. Nine incidents in SEA from 2016 to 2019 were identified through the using of a mix of automated and manual data classification strategies.

1. CHEN Xingyu Kena, Yasmine WONGa, Vaishnavi
HONNAVALLIa, TAN Ann Qi Justinaa, and NEO Loo Senga,b
aHOME TEAM BEHAVIOURAL SCIENCES CENTRE
bNANYANG TECHNOLOGICAL UNIVERSITY
Accurate as of 20th June 2019
1
P o s t e r p r e s e n t a t i o n f o r 4 t h A s i a n C o n f e r e n c e O f
C r i m i n a l & O p e r a t i o n s P s y c h o l o g y

2. © H O M E T E A M B E H A V I O U R A L S C I E N C E S C E N T R E 2
• Views expressed here are the authors’ and do not reflect the official
stance of the Ministry of Home Affairs
• Chen, X. K., Wong, Y., Honnavalli, V., Tan, A. Q. J., and Neo, L. S. (2019,
July). A preliminary analysis on the social impact of fake news after terror
attacks in SEA. Poster session presented at the 4th Asian Conference Of
Criminal & Operations Psychology, Singapore.

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• As observed in attacks such as the 2017 Westminster attack, 2018 Las
Vegas shooting, and 2018 Surabaya bombing, the spread of fake news
can disrupt social harmony in societies after a terror attack (Chen, 2018; Innes, 2017).
– For example, following the Surabaya bombing, there were conspiracies claiming
that the attack was a ploy to tarnish the image of Islam, and distract the
population from the upcoming 2019 Indonesian Presidential elections (Rahmawati, 2018).
Translation:
Top post: “Once you paddle you come across 2 or 3
islands. Once you bomb you will: 1. Tarnish the name of
Islam. 2. The trillions of dollars will melt. 3. The issue of
the change of president will not be on the agenda
anymore. You are a sadist. Your own fellow citizens ...
this is a big sin.”
Bottom post: “Hasn’t the terrorist been moved to NK
[Nusa Kambangan]? Are they asking for more donations
for the anti terror funds? You are extremely cursed for
sacrificing your own fellow citizens. Your first drama not
saleable. Now you want to make another one”

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• Currently, there is a lack of research on fake news after a terror incident,
especially in Southeast Asia (SEA)
– Hence, to address this gap, this study examines the different social impact on the
community that fake news have across different terror attacks in Southeast Asia
(SEA).
• Using a mix of machine learning and manual procedures, terror attacks
in SEA from 2015-2019 with relevant characteristics (i.e., fake news
emerging in its aftermath which had a negative social impact on the
community) were identified.
– For the purpose of this study, fake news is defined as “content that contains
inaccurate, misleading or fabricated information, and is being distributed
through different channels of communication such as print, broadcast, text
messaging, or social media” (Chen & Goh, 2019, p. 121).
– Social impact defined as the effect on the community’s social fabric and well-
being of the individuals and families (Dietz, 1987; Siman-Tov, Bodas, & Peleg, 2016).

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• Units of analysis
– The units of analysis are the reported instances of fake news that occur after a terror attack
and its social impact on the local community. The instances of fake news are collected via
news agencies and websites collected via URLs or links to websites.
TERROR ATTACK IN SEA
STUDY

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• A total of 2427 cases of terror attacks in SEA between 2015-2019 were collected
from the Global Terrorism Database and manual searches.
• A Multinomial Naïve Bayes (NB) Classifier was used to label and identify relevant
texts.
– NB classifiers have been widely used in the labelling of free text and has been evaluated as a
classifier that achieves good accuracy while being computationally efficient (Ting, Ip, & Tsang, 2011).
Workflow Overview
1. Identify
terrors attacks in
SEA via Global
Terrorism
Database and
manual searches
2. Collecting
content related
to the attacks via
Google search
engine
3. Identify
relevant content
through machine
learning
4. Manual
verification and
coding

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Cases from Global
Terrorism Database
(2015-2017)
Cases of terrorist
incidents
(2018-2019)
Keywords for Google searches
Sources for
each GTD
case
Named Entity
Recognition (NER)
on information given
on the attack
1. Identify terrors attacks in SEA via
Global Terrorism Database and manual
searches
3. Identify relevant content
through machine learning
Texts that are likely to be relevant
from 472 URLs
19 Keyword
filters from
existing
research
Multinomial
Naïve Bayes
Classifier
Training set
with 143
references to
an instance
of fake news
or social
impact
Training set used for
training classifier
Classifier used to
label remaining cases
4. Manual verification
and coding
9 attacks of interest
Attacks of interest verified by three coders
using the inclusion and exclusion criteria.
Disagreements are resolved by examining
source material. 27 URLs related to 9 attacks
were used for coding
Database of texts from 25,043 unique URLs
Inclusion criteria Exclusion criteria
 Each case must have more
than 2 links from different
sources confirming an
incidence of fake news
 Fake news occurs after a
terror attack that took place
 The incident must contain at
least one instance of fake
news and at least one
instance of a social impact on
the community
 If the fake news was a
mistake and it was
subsequently removed
 If the fake news perpetrator
did it as a joke
 Open warfare /military
crackdowns /humanitarian
crisis (e.g., Marawi Siege and
Rohingya crisis)
good average
accuracy of
67% using 5-
fold cross-
validation
2. Collecting content related to the
attacks via Google search engine
Note: GTD records attacks of varying magnitudes. Attacks of very low magnitude or impact will not have any recorded instances of fake news or its social impact

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• Nine attacks in SEA from 2015 to 2019 (see Table 1) was identified through the using
of a mix of automated and manual data classification strategies.
Table 1 Types of social impact of fake news across the different attacks
Year Attack Social Impact Count
2015 Paragon blast (Thailand) Erode trust in authorities 1
2016 Jakarta attacks (Indonesia)
Incite fear and unrest 2
Engage in identity politics 1
2017 East Jakarta attacks (Indonesia)
Erode trust in authorities 1
Incite fear and unrest 1
2017 Benguet truck burning (The Philippines)
Incite fear and unrest 1
False alarm concerning public safety 1
2017 Freeport shooting/hostage taking (Indonesia) Erode trust in authorities 1
2018 Surabaya bombings (Indonesia)
Engage in identity politics 1
False alarm concerning public safety 1
Incite fear and unrest 1
2018 Yoma Bank bombings (Myanmar)
Erode trust in authorities 1
Public safety warnings 1
2018 Lamitan van bombing (The Philippines) Creating fear and unrest 1
2019 Zamboanga mosque attack (The Philippines) Incite fear and unrest 1

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Social impact of fake news on a community
• The social impact on the community posed by the fake news can be
characterised by these main types: erode trust in authorities, engage in
identity politics, false alarms concerning public safety, and spread fear
and unrest (see Table 2).
• The most commonly occurring type of social impact of the fake news
appears to be spread fear and unrest.

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Table 2 Types of social impact that the fake news has on the community
Type Coding Description Example Count
Erode trust
in authorities
Fake news aimed at undermining the
public’s trust in the authorities. This can
affect the community social fabric.
2017 East Jakarta attacks
“he said the suicide bomb attack that killed three police
officers and injured 16 others in East Jakarta last week, was
a police plot.”
4
Engage in
identity
politics
Fake news aimed at inciting racial,
religious, or political groups to defend
their interests. This can affect the
community social fabric.
2018 Surabaya bombings
“The conspiracy theories ranged from a smear campaign
against Islam to a political motive related to the upcoming
elections, as HDL had suggested.”
2
False alarm
concerning
public safety
Fake news aimed at threatening and/or
warning about impending physical harm
to the public. This can affect the well-
being of individuals and families.
2017 Benguet truck burning
“Baguio City is under “red alert” status because of the
presence of New People’s Army (NPA) rebels in nearby
municipalities of Benguet.”
2
Incite fear
and unrest
Fake news aimed at inciting emotional
and/or psychological responses amongst
members of the public, including, but not
restricted to, feelings of fear, anxiety and
panic. This can affect the well-being of
individuals and families.
2019 Zamboanga Mosque attack
“In light of the messages, some social media users urged
the public to refrain from spreading unverified information,
noting that it would only incite panic and mass hysteria.”
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• Not all terror attacks in SEA saw the emergence of fake news (with
reported instances of social impact on the community) in the aftermath.
• Of the nine attacks identified, they have certain characteristics that
facilitate the spread of fake news such as:
– Urban areas
– Interest from the media both locally and internationally
– History of chronic terrorism

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The different types of social impact of fake news across different attacks show that
crisis communications needs to address these various types of social impact.
• Authorities need to quickly address information vacuums left in the aftermath of a
terror attack to prevent rumours from flourishing and causing fear to spread.
– The most commonly occurring type of social impact of fake news after a terror attack is the
creation of fear and unrest (7 instances).
– It is critical for the authorities to be able to provide information to help reduce ambiguity and
to prevent rumours from flourishing (Centers for Disease Control and Prevention, 2019; Coombs, 2015).

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The different types of social impact of fake news across different attacks show that
crisis communications needs to address these various types of social impact.
• Additionally, the spread of fake news can erode trust in authorities (4 instances),
trigger false alarms concerning public safety (2 instances, or engage in identity
politics (2 instances), which can further destabilise an already volatile situation.
– Fake news aimed at eroding trust in authorities should be countered before the narratives of
rallying the public and building resilience becomes sidetracked by conspiracy theories
alleging government wrongdoing (Tan et al., 2017).
– Fake news that trigger false alarms concerning public safety can divert emergency resources
to attend to non-emergencies, which makes it important to educate the public to check the
information first (Goh et al., 2017).
– Fake news that engages in divisive identity politics ought to be countered with narratives of
solidarity to deny the terrorists from achieving their objectives of sowing social disharmony
(Neubronner, 2018; Yulianto & Thenarianto, 2018).

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Limitations
• This study has certain methodological limitations which presents
opportunities for further research.
– The words used in the algorithm search were done only in English.
 In SEA, there are at least eight different languages (officially) and most of the local news (where instances of
fake news and news on social impact are likely to be published) are broadcasted in these languages. English-
language versions of these local news are more likely to be picked up by the algorithm search than news in
the original language.
– The units of analysis were reported instances of fake news via news agencies and websites.
 Although there are richer sources of information such as social media posts, they are not included due to
language reasons. Future research could address this.
– The design of the study examines the intersection of fake news and the social impact on the
community associated with these fake news after a terror attack incident.
 Future work examining these categories in isolation may yield more typologies.

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• Despite these limitations, the current methodology of systematically
collecting open-source accounts of fake news incidents after a terror
attack in SEA and its social impact on the community can still provide
rich preliminary insights into the nature of the phenomenon.
– Feasibility of using a mix of machine learning and manual coding to study the
spread of fake news after a terror attack in SEA in a systematic, effective, and
scalable manner.
– The study of the spread of fake news after a terror attack is an area that is
currently understudied, a systematic approach can provide a clearer picture of
the phenomenon at hand.

16. © H O M E T E A M B E H A V I O U R A L S C I E N C E S C E N T R E
For enquires, please contact
16
Chen Xingyu Ken
Behavioural Sciences Research Analyst
Xingyu_CHEN_from.TP@mha.gov.sg
Research Team:
Yasmine Wong, Behavioural Sciences Research Analyst
Vaishnavi Honnavalli, Behavioural Sciences Research Analyst
Tan Ann Qi, Justina, Research Intern
Neo Loo Seng, Principal Behavioural Sciences Research Analyst, PhD candidate at NTU

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