Dynamics of Personal Responses to Terror Attacks: A Temporal Network

Analysis Perspective

Ema Ku

sen

and Mark Strembeck

1,2,3

Vienna University of Economics and Business, Vienna, Austria

Secure Business Austria (SBA), Vienna, Austria

Complexity Science Hub (CSH), Vienna, Austria

Keywords:

Death Anxiety, Emotional Pain, Network Dynamics, Temporal Network Analysis, Terror Attack, Twitter.

Abstract:

In this paper, we analyze responses to terror attacks through the lens of the Terror Management Theory. We

focus on the temporal evolution of Twitter messages that convey death anxiety, emotional pain, as well as

positivity. We model the responses to terror attacks as personal reactions that include the use of a ﬁrst person

singular pronoun along with cues of affect and personal concerns. In order to detect these textual features,

we used the Linguistic Inquiry and Word Count (LIWC) tool. Our data-set includes tweets related to three

terror attacks: the 2017 Manchester terror attack, the 2019 Christchurch terror attack, and the 2020 Vienna

terror attack. Our analysis is based on 3.8 million tweets that have been sent by 1.6 million users. The results

indicate that positive messages associated with the use of religious words (e.g., messages of prayers and hope)

dominate over those that convey emotional pain and fear of death. This points to a tendency to spread hope

and empathy in the aftermath of a terror attack. We found that the acute phase of a terror attack exhibits a high

volume of messages that sharply decline in the immediate aftermath. In contrast, positive messages exhibit

smaller peaks even one week after a terror attack happened.

1 INTRODUCTION

People who face immediate threats to their own life,

their family members, or friends, tend to respond

with elevated emotions of fear, anxiety, anger, and

grief (Hardy and Miller, 2022; Ku

sen and Strembeck,

2021). Such reactions to crises have long been in

focus of behavioral scientists who found that people

universally respond with an initial disbelief, followed

by a wide spread of information and rumors, venting

of intense emotions, as well as various collective reac-

tions (incl. collective panic, organized help and rescue

efforts). Other studies also showed that expressions of

solidarity, togetherness, as well as blame commonly

occur before people can return to their normal routine

(Flynn, 1997; Smelser and Mitchell, 2002).

Terror attacks are a speciﬁc type of crisis that hap-

pen unexpectedly and without a prior warning. More-

over, terror attacks pose an immediate threat to the

well-being and life of those directly affected, aim

to destabilize our world-view (Miller and Landau,

2005), and give rise to an intense anxiety. The latter

is strongly related to the reminder of our own mortal-

ity which is a focal point of Terror Management The-

ory (TMT) (Greenberg et al., 1986; Rosenblatt et al.,

1989). In general human beings are not only aware

of their own mortality but also have a desire for self-

preservation. In this light, a positive worldview serves

as an effective barrier to death anxiety (Kastenbaum,

2007).

Death anxiety related to terror attacks is univer-

sal (Greenberg et al., 1986) and not only concerns

those who are directly affected, but also those who

are reminded of past experiences of terror. (Rosen-

blatt et al., 1989) further showed that people are of-

ten reminded of their own mortality simply by read-

ing newspapers or watching the news on TV. Thereby,

such studies conﬁrm that the fear of death caused by

terror attacks is one of the strongest psychological

threats (Becker, 1997).

As in any type of crisis, Online Social Networks

play an important role during terror attacks. They

serve an informational purpose during the acute phase

of a terror attack (Steensen, 2018; Simon et al., 2014)

and facilitate the organization of crisis relief efforts as

well as expressions of togetherness in the aftermath of

a terror attack (Merrill et al., 2020). While multiple

studies focused on the types of messages and the vol-

Kušen, E. and Strembeck, M.

Dynamics of Personal Responses to Terror Attacks: A Temporal Network Analysis Perspective.

DOI: 10.5220/0011078100003197

In Proceedings of the 7th International Conference on Complexity, Future Information Systems and Risk (COMPLEXIS 2022), pages 36-46

ISBN: 978-989-758-565-4; ISSN: 2184-5034

 2022 by SCITEPRESS – Science and Technology Publications, Lda. All r ights reserved

ume of messages sent in each phase of a terror attack,

it has rarely been studied how speciﬁc types of social

media messages are exchanged and which temporal

communication patterns emerge during terror attacks.

In this paper, we investigate the temporal pat-

terns resulting from messages that convey death anx-

iety, emotional pain, as well as positive perspectives

about an otherwise bleak future (associated with one’s

faith). We model these responses as personal stories

told from a person’s individual perspective (indicated

by the use of the ﬁrst singular personal pronoun) in or-

der to make sense out of a terror attack. According to

(Hermans and Hermans-Jansen, 1995), self-narratives

serve as a good indicator of situations that have a high

personal meaning during an episode in the speaker’s

life.

Our ﬁndings bring forth a couple of valuable in-

sights into the psychological underpinnings of terror

attacks. We extend the existing literature on human

responses to terror attacks by showing that positive

messages associated with the use of religious words

(e.g., messages of prayers and hope) play a special

role in the acute phase of the terror attack as well as

in its aftermath.

The remainder of this paper is organized as fol-

lows. In Section 2 we provide a summary of related

work. Section 3 describes our research method. Re-

sults are presented in Section 4 and discussed in Sec-

tion 5. We conclude the paper in Section 6 and pro-

vide directions for future research.

2 RELATED WORK

Informational Role of Twitter. (Simon et al., 2014)

studied the role of Twitter during the 2013 Westgate

mall terror attack in Kenya and found that initially

an uncontrolled surge of messages occurred. Though

there are obvious positive effects of such a high rate

of message sharing (e.g., social bonding with the

community, stress relief), the authors found that the

terrorists also monitored Twitter to acquire informa-

tion about the actions of the police and armed forces.

Along these lines, a study by (Oh et al., 2011) found

the same phenomenon during the 2008 Mumbai ter-

rorist attack. As a result of the uncontrolled surge of

messages, sensitive situational information about the

ofﬁcials’ operational activities were revealed to the

terrorists.

Another study on the 2013 Westgate mall in

Kenya analyzed narratives formed by a terrorist group

(Mair, 2017). This study found that the terrorists

took control of their own narrative by not providing

any links to external websites or engaging with other

Twitter users. Their sole purpose was to spread their

ideology and justify their actions.

Twitter as a Channel for Collective Sharing of

Emotions. (Bruns and Hanusch, 2017) focused on the

dissemination of audiovisual content during the 2015

Paris terror attack and the 2016 Brussels terror attack.

This study found that the intent of audiovisual content

was predominantly of an affective nature (e.g., photos

of landmarks lit up in French national colors, images

with text “Pray for Paris”) and informational nature

(eyewitness photos). The study pointed to the im-

portance of affective intent in the audiovisual content

as it reveals how a collective responds to an attack.

Another study on collective emotions is presented in

(Steensen, 2018), who found that the narratives of em-

pathy associated with the #PrayForOslo and #Pray-

ForNorway hashtags emerged about two and a half

hours since the 2011 attack in Oslo and on the island

of Utøya in Norway. One week after the terror attack,

tweets of grief and love (#oslove, #showyourhearts)

dominated the Norwegian Twitter discourse.

The 2013 Woolwich/London terror attack was

studied by (Burnap et al., 2014). They found that af-

fects conveyed in tweets are directly associated with

message diffusion rates. In particular, the authors

found that tweets that convey a high level of tension

and antagonism circulated for a shorter amount of

time compared to those with a low level of these two

sentiments. Several important implications were re-

ported in this study, one of them being the role of pos-

itive messages which were shown to propagate longer

than the negative messages.

Twitter as a Channel for Sense-making. In terms

of message creation, (Kwon et al., 2017) found that

tweets sent during terror attacks predominantly fo-

cus on present (rather than past or future) and in-

cluded detailed and concrete descriptions of the event

rather than an abstract account of the event. (Eriks-

son, 2016) analyzed post-attack responses to the 2011

terror attacks in Oslo and on the island of Utøya, Nor-

way. The results revealed that people dealt with the

collective trauma by expressing solidarity and col-

lectively tried to make sense of what had happened

by searching for the underlying causes for the attack.

The latter discussion especially focused on the polit-

ical ideology of the perpetrator and those sharing a

similar ideological viewpoint. Interestingly, the study

found that people try to compare and reference dif-

ferent violent incidents to construct a narrative of a

growing trend of violence.

Twitter as a Channel for Pro-social Behavior. Dur-

ing the acute phase of a terror attack, a collective sol-

idarity towards those who seek shelter may emerge.

One such example was studied in (Reilly and Vicari,

Dynamics of Personal Responses to Terror Attacks: A Temporal Network Analysis Perspective

2021) during the 2015 Paris terror attack where the

hashtag #PorteOuverte (open door) was highly shared

on Twitter as means of offering a safe place to hide

from the terrorists roaming the streets of Paris. This

inspired a series of similar initiatives (Tikka, 2019),

such as #openhouse during the 2016 attack in Brus-

sels, #offenet

ur during the 2016 attack in Munich,

#opendoors during the 2016 attack in Nice, #room-

formanchester during the 2017 attack in Manchester,

#openstockholm during the 2017 attack in Stock-

holm, or #bedinbarcelona during the 2017 attack in

Barcelona.

In addition to offering shelter, another form of

pro-social behavior during terror attacks is the expres-

sion of togetherness and unity. While studying the

responses to two terror attacks (Ezzati, 2021) found

that people are united through common values, such

as tolerance, co-existence, love, justice, peace, and

fraternity. The author pointed out that the purpose of

such value-conveying messages is to dissociate reli-

gious groups and members of a certain ethnicity from

the perpetrator(s).

3 METHOD

In this paper we analyze temporal characteristics of

emotional human reactions to terror attacks through

the lens of TMT. Our paper is guided by the following

main research question:

• How do responses to terror attacks evolve over

time?

To further elaborate on this question, we explore the

following types of human responses to terror attacks:

1) emotional pain, 2) death anxiety, and 3) messages

of positivity associated with a use of religious words,

and deﬁne the following sub-questions:

• How does the expression of emotional pain by

those affected evolve over time?

• How does the expression of anxiety related to own

mortality by those affected evolve over time?

• How does the expression of positivity related to

the use of religious words evolve over time?

In order to answer to these questions, our research

method follows three phases, as outlined below.

Data Extraction. We extracted the data by using

Twitter’s Search API and a list of predeﬁned hashtags

and key terms for each terror attack

. The data extrac-

Christchurch terror attack: #NewZealandTerroris-

tAttack, #Christchurch, #ChristchurchMosqueShootings,

#ChristchurchMosqueAttack, #ChristchurchTerrorAttack;

tion started on the day of the terror attack and contin-

ued for another two weeks. After removing duplicate

entries, our data-set comprised 3,854,670 tweets au-

thored by 1,643,939 Twitter users. Details are pre-

sented in Table 1.

Data Pre-processing. In our analysis, we used

the Linguistic Inquiry and Word Count (LIWC) tool

(Pennebaker et al., 2015) to identify psycholinguistic

features in Twitter messages. LIWC’s internal lexicon

is organized into a set of categories, each designating

a speciﬁc psycholinguistic phenomenon. For exam-

ple, a category called “Personal Concerns” includes

variables “religion”, “death”, “money”, and “home”

along with their corresponding internal lexica, while

the category “Affective Processes” includes anxiety,

fear, anger, positive emotions, and negative emotions,

(see (Pennebaker et al., 2015) for details).

We ﬁrst deﬁned a rule to capture a narrative (main

theme) conveyed in each tweet. The rule was as fol-

lows:

f (x,y) =

(

x + y, if x > 0 ∧ y > 0

0, otherwise

where x and y represent a variable in the category

“Personal Concerns” and “Affective Processes” re-

spectively. If both values were positive, we multiplied

their sum with the LIWC score of the ﬁrst person sin-

gular personal pronoun. The LIWC variables used in

this paper are outlined in Table 2 along with a note

on the rationale behind the choice and application of

LIWC variables.

Network Construction. Upon inferring the scores

for emotional pain, death anxiety, and religion (in this

context associated with positivity), we measured the

daily intensities of the three personal responses. For

each of the three types of personal responses, we con-

structed temporal direct messaging (DM) networks

for each type of a narrative by tracing the @-mentions

in the text

of a tweet. Users mentioned via the @-

mechanism are considered target nodes in our net-

Manchester terror attack: #ManchesterArena, #manch-

esterstrong, “terror manchester”, “manchester ariana

grande”; Vienna terror attack: “vienna terror”, “terrorist

attack #Vienna”, “#ViennaAttack””, “#Viennashooting”,

“#prayforvienna”, “#wienATTACK”, “angriff vienna””,

“#terrorwien”, “#PrayforWien”, “#viennaattacks”, “#Vien-

naTerrorAttack”, “#austriaAttack”, “sorgen Wien”, “#Vi-

ennaterror”, “#ViennaTerroristAttack”, “#viennapolice”,

“#austriashooting”, “terror wien”, “wien Hintergrund”, “vi-

enna background”, “#Schwedenplatz”, “wien #staysafe”,

“vienna #staysafe”, “#StayStrongAustria”, “#zibspezial”,

“#Nehammer”, “#0211w”, “@ORFBreakingNews”,

“#Synagoge”, “#Schießerei”, “#terroranschlag wien”,

“#terroranschlag vienna”, “#schleichdiduoaschloch”.

Note that we removed the retweets prior to a network

construction.

COMPLEXIS 2022 - 7th International Conference on Complexity, Future Information Systems and Risk

Table 1: Basic information about the data-set.

Event Time period Tweets Retweets Usernames Language

Christchurch 15 – 28 March, 2019 1,775,901 1,567,946 (88%) 846,858 en

Manchester 22 May – 4 June, 2017 1,582,586 1,232,085 (77.8%) 675,183 en

Vienna 2 – 15 Nov, 2020 496,183 378,171 (76%) 202,719 en, de

Table 2: The choice of LIWC variables.

LIWC variables Explanation

Emotional pain

“I” × (sadness +

anger)

(Junghaenel et al., 2017; Rude et al., 2004; Tausczik and Pennebaker, 2010) indicated that

those who experience emotional and physical pain tend to draw attention to themselves (via

a ﬁrst person singular pronoun) and use words that indicate their emotions, especially sad-

ness and anger.

Example:

“I’m sorry I’m sorry I’m sorry I send my love if I could come hold all the families #Manch-

esterArena”,

“I didn’t sleep a second. I am tired. My head hurts. My heart hurts. #PrayforVienna”,

“I f**ing hate humanity man #ChristchurchMosqueAttack”

Death anxiety

“I” × (death +

anxiety)

(Greenberg et al., 1986) indicated that threat to one’s mortality is reﬂected through anxiety

over death, either an own death or death of someone else.

Example:

“Every time I see the conﬁrmation picture of another death in Manchester attack I feel so

much pain... #manchester #terror”,

“#PrayforVienna I swear I’ve never been so scared of death”,

“I just learned about #Christchurch terrorist massacre... I’m just...appalled. Speechless. I

just can’t...”

Religion

“I” × (religion +

positive affect)

(Flynn, 1997) found that religious beliefs provide hope for the otherwise bleak future.

Example:

“I pray for the victims. I pray for the families of the victims. I pray for #Manchester. I pray

for Ariana Grande. I pray for the world.”,

“My heart is sore for my other hometown tonight. God bless Vienna #viennaterrorattack”,

“I hope the victims ﬁnd paradise #NewZealandTerroristAttack”

work, while the author of a corresponding tweet is

considered a source node. Basic information about

our networks is presented in Table 3.

Data Analysis. We analyzed our networks with

respect to their underlying temporal dynamics that

emerge due to direct message exchanges among the

Twitter users. Subsequently, we analyzed the tempo-

ral networks with respect to their momentary struc-

ture (edge formation patterns and dyad density). To

construct and analyze the temporal networks, we used

tsna (Bender-deMoll and Morris, 2021) R package.

Our analysis was conducted on a quad-core machine

with Intel(R) Core(TM) i7-5600U CPU @2.60GHz.

4 RESULTS

The results revealed distinctive temporal patterns

among the three personal responses analyzed in this

paper. As shown in Figure 1, all three emotional re-

Table 3: Basic information about each network.

Event Nodes Edges

EMOTIONAL PAIN

Christchurch 241 133

Manchester 324 264

Vienna 2344 2675

DEATH ANXIETY

Christchurch 140 78

Manchester 282 246

Vienna 1555 1728

RELIGION

Christchurch 1101 690

Manchester 648 540

Vienna 3498 4106

sponses are present throughout the 14-day extraction

period in all data-sets. We observed a higher level

of death anxiety during the acute phase of each ter-

Dynamics of Personal Responses to Terror Attacks: A Temporal Network Analysis Perspective

ror attack and its gradual decrease over time. Positive

messages (associated with the use of religious words)

are dominant throughout the entire extraction period,

while emotional pain peaks in the immediate after-

math and afterwards decreases over time.

DATE

INTENSITY

18 March 25 March

DATE

22 May

29 May

2 Nov

9 Nov

DATE

INTENSITYINTENSITY

Death anxietyEmotional pain

Religion

CHRISTCHURCH

MANCHESTER

VIENNA

Figure 1: Intensities of emotional pain, death anxiety, and

religion in each data-set.

Table 4 shows reactions to the tweets that

convey death anxiety, emotional pain, and re-

ligious words. The results point to a tendency

to highly retweet messages that convey emo-

tional pain (µ(Christchurch) = 873.72 ± 788.76,

x(Christchurch) = 571; µ(Manchester) =

27303.87 ± 11746.99,

x(Manchester) = 32373;

µ(Vienna) = 7560.01 ± 6182.08,

x(Vienna) = 4620)

while those that convey death anxiety re-

ceived substantially smaller number of retweets

(µ(Manchester) = 28.13 ± 39.54,

x(Manchester) =

5; µ(Vienna) = 561.15 ± 773.70,

x(Vienna) = 186;

with an exception of the Christchurch terror at-

tack data-set). In terms of “likes”, the results are

inconclusive.

Next, we turn to the analysis of temporal commu-

nication networks. Three examples of daily message-

exchange networks shown in Figure 2 reveal that

the networks substantially change their structure over

time (note that, in our analysis, once an edge is

formed, it does not cease to exist but a new edge is

added in each time step).

In order to describe the

evolution of each network, we ﬁrst report on the mea-

sures of momentary structure.

Figure 3 illustrates daily edge formations for each

network and shows that a great majority of edges

are formed in the ﬁrst two days of the terror attacks

we analyzed. The edge formation count sharply de-

creases during the immediate aftermath and only oc-

casionally new edges are formed. This is illustrated

via small spikes in Figure 3 (we point the attention

to “religion” for the Christchurch and Vienna terror

attacks). We also observed a substantially higher vol-

ume of edges formed in the religion-related networks

throughout the data-extraction period for all terror at-

tacks in our study. For example, the Manchester terror

attack counts between 130 and 160 edges for the ex-

change of emotional pain and death anxiety and over

340 edges for messages that convey positivity and re-

ligious words (which corresponds to an increase of

approx. 100%).

We also measured dyad density. The results indi-

cate that for all possible dyads, the fraction of time

they are tied is relatively low in all networks (see Fig-

ure 4). For example, if we randomly select a pair of

nodes from the network representing emotional pain

(Vienna), there is only about 5 in 10,000 chance that

the pair will be connected by an active edge. How-

ever, one pattern still emerges across all terror at-

tacks we analyzed – the dyad duration of all net-

works representing religion (positivity) is higher than

the dyad duration in emotional pain and death anxiety

networks.

5 DISCUSSION

Our study on the evolution of messages expressing

emotional pain, death anxiety, and positivity brings

forth some interesting insights into human (online)

behavior during terror attacks. While our focus was

on the personal reactions to the terror attacks, we ob-

served a high dominance of positive messages related

to the use of religious words throughout the data ex-

traction period. We found that all personal narratives

initially show a sudden surge in a message volume,

which is in line with the ﬁndings presented in (Simon

Our animated networks are available at

https://nm.wu.ac.at/nm/Complexis22-supplemental.

COMPLEXIS 2022 - 7th International Conference on Complexity, Future Information Systems and Risk

Table 4: User reactions to tweets conveying emotional pain, death anxiety, and religion.

RETWEETS LIKES

Event

Mean Median Max Mean Median Max

EMOTIONAL PAIN

Christchurch 873.72±788.76 571 1918 3.35±119.2 0 9590

Manchester 27303.87±11746.99 32373 32373 1.22±140.55 0 23247

Vienna 7560.01±6182.08 4620 13752 7.50087±745.73 0 114013

DEATH ANXIETY

Christchurch 1839.74±1804.45 1770 4654 2.28±83.91 0 6842

Manchester 28.13±39.54 5 127 2.14±20.84 0 614

Vienna 561.15±773.70 186 2008 4.67±81.20 0 3918

RELIGION

Christchurch 1123.84±1617.37 314 4731 3.57±183.00 0 31906

Manchester 24729.12±13621.65 32373 32373 1.17±133.70 0 23247

Vienna 1092.27±1836.44 120 4636 5.97±312.28 0 40771

t=1

t=2

t=3

c) Religion (Vienna)

b) Death anxiety (Manchester)

a) Emotional pain (Christchurch)

Figure 2: Examples of daily message exchange networks that convey emotional pain, death anxiety, and religion sent during

the ﬁrst three days of the Christchurch terror attack, Manchester terror attack, and Vienna terror attack, respectively.

et al., 2014). While the majority of previous stud-

ies focused on the informative role of Twitter during

terror attacks, we focused on the personal emotional

responses to the terror attack. In contrast to our prior

assumption that death anxiety and emotional pain will

be dominant in the acute phase of the terror attack, our

data points to an immediate surge in messages that

convey positivity. To explain this observation, we re-

fer to (Steensen, 2018) who showed that the messages

of prayer emerge already a few hours after the news of

a terror attack have reached the audience. These mes-

sages often contain a #prayfor hashtag (followed by a

name of a country or a city affected by a terror attack,

e.g., #prayforparis). This clearly points to a human

tendency to show resilience against the otherwise dis-

ruptive effects of a terror attack and engage in a pro-

social behavior while expressing empathy, hope, and

well-wishes.

Dynamics of Personal Responses to Terror Attacks: A Temporal Network Analysis Perspective

Time Time Time

Edge formation countEdge formation countEdge formation count

EMOTIONAL PAIN DEATH ANXIETY RELIGION

Christchurch

EMOTIONAL PAIN DEATH ANXIETY RELIGION

Manchester

EMOTIONAL PAIN DEATH ANXIETY RELIGION

Vienna

Figure 3: Edge formation.

Dyad duration density

Networks

Emotional pain (Christchurch)

Figure 4: Dyad duration. The networks are grouped according to the event of study (Vienna terror attack in orange, Manchester

terror attack in blue, Christchurch terror attack in green alongside x-axis).

These ﬁndings are in line with the Terror Man-

agement Theory’s basic premise, which points to a

human tendency to focus on a positive worldview as

a shield against the fear of death (Greenberg et al.,

1986). We examined a 3% random sample of tweets

(excluding retweets) for each personal response and

each terror attack

to get further insights into the

In total, we manually inspected 261 messages related

COMPLEXIS 2022 - 7th International Conference on Complexity, Future Information Systems and Risk

content associated with emotional pain, death anxi-

ety, and positivity associated with the use of religious

words.

The emotional pain category predominantly in-

cluded messages of condolences (92%). This cat-

egory also conveyed a smaller amount of messages

of intense anger (2.5%), e.g., “I’m BREWING OVER

with anger at THIS evil loser. #ManchesterArena”,

“What a cruel and evil cu*t this Brenton Tarrant

is hope you rot in hell you vile evil cunt”, “Jesus

fu**ing christ, I’m so unbearably fu**ing sad and

angry.”, “I hope the perpetrators are caught alive,

these deranged scum disgust me. #PrayforVienna

#fu*kterrorism” and annoyance (1%), e.g., “I think by

equating #Brexit and #Christchurch attack you have

reached a new low” and “Anyone who sends private

videos of terror, suffering and death to [magazine

name] to grab a few euros should be struck by light-

ning”.

To a lesser amount (1.5%), values such as the role

of “love” to “silence hate” or describing terror as a

“cowardly act” that destroys lives and “violates our

human values”, were also a theme of the messages in

the emotional pain category. Such messages that con-

vey values indirectly also communicate a messages of

togetherness. (Ezzati, 2021) showed that values serve

as means to unite those affected by the terror attack.

While the purpose of such messages is to dissociate

religious groups from the perpetrator, we found that

our examples do not explicitly refer to any speciﬁc

religious group or ethnicity but rather generally refer

to human values as means for hoping for a better fu-

ture – “War may be loud and strong - I hope we can

be louder and stronger. For the peace. Together - no

matter who and from where.”

However, distancing oneself from the perpetrators

was conveyed in responses to islamophobic tweets.

We label such tweets as messages of counter-bigotry

(1%), e.g., “As a Muslim I feel double pain, (1) for the

lose of innocent people, (2) coz terrorists kill using the

name of Islam. #ManchesterArena”.

In total, 2% of the messages in the emotional pain

category were misclassiﬁed, for example: “#Manch-

esterStrong I would hope that politicians have re-

spect enough to not use the tragic events to launch

scatching attacks at each other.”, “The obnoxious

[anonymized] has made a glorifying video about the

Islamist attack in Vienna. I ask everyone to report

the video on YouTube and rate it negatively! #vienna

#terror #islam #attack”.

Our second type of a personal response to ter-

ror attacks, death anxiety, was largely conveyed in

to religion, 101 related to death anxiety, and 299 related to

emotional pain, equally distributed over each terror attack.

messages of condemnation and detestation against the

perpetrators followed by well-wishing to those af-

fected (78%), e.g.:

• “Unhuman act of terror(IS)m hits Manchester, my

thoughts and feelings go out to the ones who died

#manchester”

• “Another senseless tragedy for a pointless ’war’.

My heart hurts too much to sleep.”

• “This is one of the most disgustingly disturbing

mass shooting footage I’ve ever seen. Sick. Burn-

ing him alive would do him no justice. #pray-

forchristchurch”

However, we also found messages that express con-

cern for the lives of the ones directly affected by a

terror attack (9%), e.g.:

• “I can only think of #Paris #Nice #Kabul #Vienna

all the victims of the #terrorist attack.”

• “I hope that no one else has to die out there

from these terrorists who are still walking around

freely.”

Death anxiety was also associated with a reminder of

previous terror attacks (8%), e.g., “I am reminded to-

day of the awful feeling when I learned that a terrorist

had killed dozens of my people at night”, and “Horri-

fying visuals and chilling reminder of Mumbai”. Such

a reference to other acts of violence is commonly

found in various reports on human responses to terror

attacks and, as pointed in (Eriksson, 2016), is used to

put the current terror attack in a context of a grow-

ing trend of violence. Moreover, we also found that

the three terror attacks studied in this paper brought

memories of other terror attacks which further points

to the universal fear of death regardless of one’s tem-

poral, social, or geographical proximity to the event.

In the category “death anxiety”, we also found

instances of islamophobic tweets (3.5%) that convey

anger and convey a lack of tolerance towards speciﬁc

religious groups. For example, “When I think of Vi-

enna I see architecture and culture. When Muslims

think of Vienna they see murder and terrorism. Is-

lam is a cancer on Europe. Outlaw it now. Banish

them all. Their teachings tell them to kill us. Their

books guide them into terror #islamiscancer”, “One

after another terror attacks r happening in world I

don’t know when will dis sought of mentality will end

these radicalised islamics r still living in previous

centuries they r not getting that dis is 21st century

u can’t get power by killing people #ViennaTerrorAt-

tack”. Islamophobic tweets were shown to be a com-

mon narrative in the aftermath of terror attacks, espe-

cially when the perpetrator(s) were identiﬁed as mem-

bers of the Islamic religion (see, e.g., (Awan, 2014;

Dynamics of Personal Responses to Terror Attacks: A Temporal Network Analysis Perspective

Hardy and Miller, 2022)). Islamophobia is seen as a

prejudice against Muslims with a goal to cause hostil-

ity and provoke feelings of intolerance (Awan, 2014).

As pointed out in (Hardy and Miller, 2022), islamo-

phobic tweets can be regarded as a form of blaming,

which commonly emerges in the aftermath of terror

attacks and serves as a mechanism to regain a psy-

chological control over the highly emotional situation

(Flynn, 1997).

The remaining messages (1.5%) conveyed disbe-

lief and a shift in one’s worldview. Messages such

as “I never thought that this would happen. Vienna

is one of the best places in the world. I always felt

so good there - far away from all the war and ter-

ror. Now it’s over. Wtf is wrong with this world.”

are in line with the goal of terrorists to destabilize our

worldview (see (Miller and Landau, 2005)) and an ex-

pected reaction to the terror attack by those affected.

Other tweets in the sub-category of “death anxi-

ety” included feelings of pain as a result of hearing

about people dying (“each massacre and terrorist at-

tack kills a tiny piece of me... maybe all of us?... no

words are adequate.”, “I cannot begin to image how

one deals with ones child being killed by a terrorist.

#ManchesterBombing #ManchesterArena”), as well

as gloriﬁcation of human values (“Whoever kills an

innocent life it is as if he has killed all of human-

ity..”), and a critique to human behavior during the

acute phase of the terror attack (“With all the videos

of the #terrorist attack in #Vienna, I wonder how peo-

ple manage to keep their smartphones steady while

people are being shot in front of their eyes.”).

We found that a great majority of tweets re-

lated to positivity were messages that convey prayers

that express well-wishing and sympathies (86.59%),

e.g, “I’m praying for everyone in Manchester!

#staystrongmanchester”, “All my prayers and sym-

pathies are with those who have lost their precious

lives and are in the hospitals. #newzealandterroris-

tattack”.

Some of the prayers conveyed calls for peace, to-

getherness, and unity, e.g.:

• ‘I pray for the victims and their families. Enough

violence! Let us together strengthen peace and

fraternity. Only love can silence hate. #viennater-

rorattack”,

• “Whether you agree with people religious beliefs

or not, we should love them equally. My heart

goes out to the victims’ families. #christchurch”,

• “Murdering people - and supposedly in the name

of God: unbearable. Terrorism knows no reli-

gion. Together we are stronger than hate and ter-

ror. #viennaATTACK”.

These ﬁndings are in line with (Steensen, 2018), who

found that the messages of empathy and love domi-

nated a discourse even two weeks since the 2011 Nor-

way terror attack occurred.

Among the remaining 13.41% tweets in this cat-

egory, some instances called for (symbolic) action,

e.g., “Let our memory of the victims remind us of

the dignity of every human being, which we will de-

fend together. I invite you to attend a nationwide

minute of silence at 12 p.m. #viennaterrorattack”,

while some tweets gloriﬁed diversity and acceptance

towards those of a different culture or religion, e.g.,

“Edinburgh Central Mosque. Crowds gathering in

solidarity with #christchurch. So glad I came. Such

diversity here.”, “I hugged my neighbors this morn-

ing! They are Muslim, and the best family I’ve ever

met!”.

Other messages expressed counter-bigotry, e.g.:

• “I’m beyond proud of my lil sis and her friends

at their ﬁrst protest against islamophobia! The

youth are so powerful.”

• “I will forever ﬁght against antisemitism, any

kinda of segregation isn’t okay, we all should be

against hate crime. violence is never the answer,

today i sadly came across some ppl being ex-

tremely racist and islamophobic under the #Vien-

naAttack hashtag”

Approximately 3% of tweets were misclassiﬁed. For

example, messages such as “I hope you rot in hell”,

“No innocent person deserves to die from a ﬂawed

ideology.”, and “You going to the gig to enjoy yourself

and create beautiful memories. Not to die.” were clas-

siﬁed as messages that predominantly convey positiv-

ity, but instead they are of a predominantly negative

affect.

Limitations. Even though we conducted a focused

data extraction and monitored Twitter to select appro-

priate key terms and hashtags to fetch tweets, we can-

not exclude the possibility that we missed some rele-

vant tweets. Moreover, social bots and their role in so-

cial networks is still of a great concern among the re-

searchers. While our data-set on the Vienna terror at-

tack excludes bots (based on Botometer score ≥ 0.8),

we did not process the remaining two terror attacks

data-sets to identify and exclude social bots due to the

large number of screennames. We acknowledge this

as a limitation as our results might be affected by the

inﬂuence of bots to a certain degree. Finally, our com-

putational method to detect death anxiety, emotional

pain, and positive messages associated with the use of

religious words is solely based on LIWC and its inter-

nal lexicon. Though we manually inspected a ran-

dom sample of tweets and their corresponding scores

COMPLEXIS 2022 - 7th International Conference on Complexity, Future Information Systems and Risk

assigned by LIWC during the pre-processing phase

of our research method, we still identiﬁed tweets that

were misclassiﬁed.

6 CONCLUSION

In this paper, we analyzed a data-set consisting over

3.8 million tweets related to three terror attacks (the

2017 Manchester terror attack, the 2019 Christchurch

terror attack, and the 2020 Vienna terror attack). Our

goal was to examine the temporal evolution of three

types of personal responses to the terror attacks –

death anxiety, emotional pain, and words of positivity

associated with the use of religious words. To ana-

lyze our tweets and detect the level of emotional pain,

fear of death, and positivity, we used the Linguistic

Inquiry and Word Count (LIWC) tool for English and

German language.

Our ﬁndings clearly indicate the prominent role

of positive religion-related messages that convey em-

pathy and well-wishing. We showed that not only

do such messages dominate over the messages of

death anxiety and emotional pain, but they are dis-

seminated with a higher frequency over Twitter (via

retweeting), and discussed in a substantially higher

rate than their counterparts. Given such a differ-

ence in the underlying communication networks, we

subsequently examined the measures of momentary

structure and showed that a duration of dyads in the

religion/positivity-related networks is comparatively

higher than the remaining networks in all terror at-

tacks that we analyzed.

In our future work we plan to further investi-

gate the temporal dynamics of speciﬁc narratives that

emerge during different types of crisis events. More-

over, in our previous studies we showed that the net-

works resulting from the exchange of messages that

convey basic emotions, exhibited substantial differ-

ences in their underlying structures. Along these

lines, it would be interesting to expand our work to

the underlying structure and a dynamic evolution of

narratives.

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COMPLEXIS 2022 - 7th International Conference on Complexity, Future Information Systems and Risk