Automated Narratives: On the Inﬂuence of Bots in Narratives during the

2020 Vienna Terror Attack

Lisa Grobelscheg

1,2

, Ema Ku

sen

and Mark Strembeck

1,3,4

Vienna University of Economics and Business, Vienna, Austria

FH CAMPUS 02 , University of Applied Sciences, Graz, Austria

Secure Business Austria (SBA), Vienna, Austria

Complexity Science Hub (CSH), Vienna, Austria

Keywords:

Narratives, Online Social Networks, Social Bots, Topic Modeling, Twitter.

Abstract:

A narrative is a set of topic-wise interconnected messages that have been sent/posted via a social media plat-

form. In recent years, social media play an important role in human information seeking behavior during

and shortly after crisis events. Moreover, automated accounts (so called social bots) have been identiﬁed to

play an instrumental role in manipulating the public discourse on social media. In this paper, we investigate

the impact of bot accounts on the Twitter discourse surrounding the terror attack that took place in Vienna,

Austria, on November 2

2020. The corresponding data-set consists of 399,247 tweets. In our analysis, we

derive a structural topic model and map it to the ﬁve “narratives of crisis” as proposed by Seeger and Sellnow.

Among other things, we were able to identify bot activity in neutral as well as in negative narratives, includ-

ing breaking news updates, ﬁnger pointing, and expressions of shock and grief. Positive narratives, such as

stories of heroes, were predominantly driven by human users. In addition, we found that the bots contributing

to narratives surrounding the Vienna terror attack did not have the ability of picking up local story lines and

contributed to more global narratives instead. Moreover, we identiﬁed similar temporal patterns in narratives

with high bot involvement.

1 INTRODUCTION

Crisis events, such as terror attacks, induce a state of

collective uncertainty and increase the need for infor-

mation to make sense of the situation (Weick, 1988).

In recent years, many people consult social media to

look for breaking news, opinions, or eyewitness re-

ports of such crisis events (Stewart and Gail Wilson,

2016; Zahra et al., 2018). In this context, the strategic

spreading of narratives via automated accounts (so-

cial bots) may hit users in an emotionally vulnerable

state. A recent example is the involvement of bots in

strategic misinformation campaigns during the ongo-

ing COVID-19 “infodemic” (Zarocostas, 2020; Fer-

rara, 2020). However, the question of the degree of

bots’ contribution to speciﬁc types of narratives has

not been thoroughly invested yet.

In this paper we investigate the role of social bots

on the dissemination of speciﬁc narratives over Twit-

ter that are related to the 2020 Vienna terror attack.

In particular we examined (1) the role of bots dur-

ing and (two weeks) after the attack and (2) tempo-

ral patterns of bots’ activities. The remainder of this

paper is organized as follows. In Section 2, we pro-

vide an overview of related work. This is followed by

a description of our research procedure in Section 3.

Our ﬁndings are reported in Section 4 and discussed

in Section 5. Section 6 concludes the paper and pro-

vides directions for future work.

2 RELATED WORK

Social Media and Crisis Events. The role of so-

cial media platforms in providing and diffusing infor-

mation during and after a crisis event has been stud-

ied from various perspectives. For example, the role

of the #JeSuisCharlie hashtag after the attack on the

French satire magazine “Charlie Hebdo” in 2015 has

been investigated in the context of individual coping

strategies (Kiwan, 2016; Giglietto and Lee, 2017).

Stieglitz et al. analyzed the public Twitter discourse

during and after three different crisis events and es-

Grobelscheg, L., Kušen, E. and Strembeck, M.

Automated Narratives: On the Inﬂuence of Bots in Narratives during the 2020 Vienna Terror Attack.

DOI: 10.5220/0011034000003197

In Proceedings of the 7th International Conference on Complexity, Future Information Systems and Risk (COMPLEXIS 2022), pages 15-25

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

pecially investigate the sense-making efforts of social

media users (Stieglitz et al., 2018). Another study ap-

plied the terror management theory (Greenberg et al.,

1986) to conceptualise collective sense-making after

the 2016 Berlin terror attack and applied structural

topic modeling to identify prevalent narratives and

their development over time (Fischer-Preßler et al.,

2019). They found that within the ﬁrst days after a

crisis event users primarily share emotional content

and information updates and, later on, more opinion

related tweets, see also (Ku

sen and Strembeck, 2021a;

sen and Strembeck, 2021b).

Bots and Narratives. Some studies investigated the

role that bots play in the formation of narratives on

social media platforms. One of such studies revealed

that social bots and human accounts tend to share the-

matically different hashtags, thereby indicating that

bots try to inﬂuence the corresponding social media

discourse (Allem et al., 2017). Another study ana-

lyzed bot behavior related to the COVID-19 debate

on Twitter and discovered large discrepancies in top-

ics promoted by humans (mainly public health con-

cerns) and bots (political conspiracies), suggesting

that bots try to inﬂuence the public discourse during

crisis events (Ferrara, 2020). Al-Rawi et al. analyzed

bot behavior in the ongoing discourse about climate

change and global warming (Al-Rawi et al., 2021).

They report that bot-generated messages mainly con-

tribute to narratives supporting climate change scep-

tics. Shao et al. studied the quality of content propa-

gated by bots (Shao et al., 2018). Their ﬁndings sug-

gest that Twitter bots act as super spreaders of low-

credibility content and contribute to its mass expo-

sure. In this light, a study on a mass shooting event

found that humans tend to retweet bot-injected con-

tent at a higher rate than vice versa, thus concluding

that bots play a signiﬁcant role in the framing of nar-

ratives (Schuchard et al., 2019).

As noted in (Wirth et al., 2019), bot-injected con-

tent may also lead to uncertainty and unpredictability.

Their key ﬁndings indicate a strategical contribution

of bots to certain conversations. For example, they

report that bots tend to be more active in conserva-

tive conversations rather than liberal or random con-

versations. Moreover, in each conversation bots seem

to follow a certain predeﬁned procedure – in politi-

cal conversations, bots share political posts and to a

lesser degree spam, while in trending topics bots are

predominantly responsible for spam and topic promo-

tion. Furthermore, (Khaund et al., 2018) studied the

behavior of bot accounts during four different natural

disasters in 2017. They found that bot accounts hi-

jack hashtags related to the respective events in order

to disseminate irrelevant information and alternative

narratives. Several studies also investigated bot activ-

ity in terms of emotional content (Ku

sen and Strem-

beck, 2018), pre-deﬁned topics (Wirth et al., 2019),

and particularities of their information sharing behav-

ior (Schuchard et al., 2019).

3 RESEARCH PROCEDURE

On November 2

, 2020 a 20-year-old gunman ﬁred

shots at civilians in the center of Vienna, Austria.

Before the perpetrator was shot dead by the police

he killed four victims and injured more than 20 oth-

ers. The Twitter messages related to the event mainly

transported shock, grief, and empathy, as well as hate

and disgust towards the attacker. In addition to pure

text messages, a number of event-related videos have

also been disseminated via Twitter. For example,

one video showed three men carrying a wounded po-

lice ofﬁcer to an ambulance, risking their lives as

the attacker has not been detained at this point. Af-

ter the video of the incident went viral, the hashtag

#helden (German for heroes), was trending on Twit-

ter in Austria. For our analysis, we collected event-

related tweets from November 2

until November

, 2020. Our study is guided by the following re-

search questions:

RQ1: What is the role of bots in event-related narra-

tives during and after the terror attack?

For the purposes of this case study, we use the con-

cept of the “rhetorical arena” proposed by Frandsen

and Johansen (Frandsen and Johansen, 2007; Frand-

sen and Johansen, 2010). The “rhetorical arena” con-

siders crisis communication as a multi-vocal pub-

lic space. As opposed to traditional sender-receiver

broadcast communication (e.g. government-to-public

or organisation-to-public), the rhetorical arena al-

lows any actor to inﬂuence crisis communication and

thereby create multiple crisis-response narratives. In

(Coombs and Holladay, 2014), the authors argue that

the rhetorical arena consists of numerous sub-arenas.

For this paper, we will interpret these sub-arenas as

different narratives in social media. The rhetorical

arena concept assumes that every actor has the ability

to frame a narrative before, during, and after a crisis

event (Gasc

o et al., 2017). To this end, we examine

the topics injected and disseminated by bot accounts

and compare them with those fuelled by human ac-

counts. For our analysis, we use the ﬁve “narratives

of crisis” as proposed by (Seeger and Sellnow, 2016).

In particular, Seeger and Sellnow suggest the follow-

ing typology of crisis narratives:

1. Blame: Accusations, references to actions or rou-

tines in the past that would knowingly cause harm

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

or lead to a crisis;

2. Renewal: Connections between a crisis and the

future, learning from past events, change in struc-

ture/policy resulting from the crisis;

3. Victim: Personiﬁcation of harm and damage

caused by a crisis, expressed feelings of empathy

for victims;

4. Hero: Personiﬁcation of positive, pro-social ac-

tion in relation to a crisis;

5. Memorial: Unity and togetherness of the affected

and unaffected community, establish a connection

to the pre-crisis state, and frame the crisis in a

larger context of purpose and ideals.

As those “narratives of crisis” mainly refer to a post-

crisis state, we added a sixth category called “opera-

tional update” to account for messages referring to an

operational update on developments during the crisis.

For the ﬁrst research question, we analyzed prevalent

topics in our data-set, assigned them to a crisis narra-

tive, and determined the extent of bot engagement in

the respective narrative.

RQ2: Which temporal patterns can be observed in

the event-related narrative activity of bots?

Our second research question examines the temporal

prevalence of narratives. In this context, we focus on

narratives with a high bot contribution and search for

patterns behind their activity.

Our research procedure includes ﬁve phases.

Data Extraction. We extracted tweets related to

the 2020 Vienna terror attack using Twitter’s Search

API and a predeﬁned list of hashtags related to the

event.

In total, we extracted 399,247 English lan-

guage tweets. The messages in the data-set have been

sent by 114,520 unique screennames, 27,800 of which

with a high botscore (see Table 1).

Data Pre-processing. First, we removed duplicate

tweets (i.e. tweets that include multiple hashtags and

have therefore been extracted multiple times). Fol-

lowing (Sasaki et al., 2014; Wang et al., 2017;

Nerghes and Lee, 2019), we kept the retweets in our

We extracted tweets including the following hash-

tags: “vienna terror”, “terrorist attack #Vienna”, “#Vi-

ennaAttack””, “#Viennashooting”, “#prayforvienna”,

“#wienATTACK”, “angriff vienna””, “#terrorwien”,

“#PrayforWien”, “#viennaattacks”, “#ViennaTerrorAt-

tack”, “#austriaAttack”, “sorgen Wien”, “#Viennaterror”,

“#ViennaTerroristAttack”, “#viennapolice”, “#austri-

ashooting”, “terror wien”, “wien Hintergrund”, “vienna

background”, “#Schwedenplatz”, “wien #staysafe”, “vi-

enna #staysafe”, “#StayStrongAustria”, “#zibspezial”,

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

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

“#terroranschlag vienna”, “#schleichdiduoaschloch”.

data-set in order to gain a better understanding of

topic prevalence. Although retweets do not represent

original content, they allow users to express consent

and opinion and thus contribute to a topic’s prevalence

in the corpus.

Text processing was conducted in R with the

structural topic model (stm) package. For our anal-

ysis, we also applied the following pre-processing

steps: converting to lowercase, removing stopwords,

removing punctuation, and removing words with less

than three characters (see also (Roberts et al., 2019)).

COUNT

DAYS

4 8

200K

150K

100K

50K

Figure 1: Tweet distribution for the data extraction period.

Bot Detection. For bot detection, we used Botome-

ter’s Python API

. As discussed in (Daniel and Mil-

limaggi, 2020), bots may have many different charac-

teristics, some of which do not necessarily result in

a high bot score. For example, some accounts may

only be partially automated and are thus partially op-

erated by one or more human users. Nevertheless,

for the purposes of this paper we introduced a binary

classiﬁcation rule to ensure a meaningful interpreta-

tion of our results. Following the suggestion of (Varol

et al., 2017), we set our threshold for bot accounts at

a Botometer score of ≥ 0.6

Exploratory Data Analysis. The Vienna terror at-

tack happened on November 2

2020 around 8 pm.

20% of all tweets in the data-set were posted on the

same day and nearly 69% during the subsequent day,

whereas tweeting activity rapidly declined after that

(see Figure 1). Following the estimates of Varol et al.,

9% to 15% of all active Twitter accounts are assumed

to be bot accounts (Varol et al., 2017; Davis et al.,

Botometer: https://botometer.iuni.iu.edu/ (see also

(Davis et al., 2016))

Botometer delivers scores between 0 and 1.

Automated Narratives: On the Inﬂuence of Bots in Narratives during the 2020 Vienna Terror Attack

BOTSCORE

COUNT

Figure 2: Distribution of bot scores with threshold at 0.6.

2016). In our data-set, 24.28% of the respective Twit-

ter accounts have been identiﬁed as bots according to

the threshold described above. The histogram in Fig-

ure 2 depicts the distribution of bot scores indicating

peaks around a score of 0.25 (most likely human) and

0.75 (most likely bot). In our data-set, 82.29% of the

messages are retweets. Interestingly, bots distribute

a higher share of retweets (87.41% as compared to

80.21% for humans). An overview of basic informa-

tion about the data-set is provided in Table 1.

-300 -200

-100

Figure 3: Semantic coherence and exclusivity of each topic

with k = 20.

Topic Model. We derived a structural topic model

to ﬁnd prevalent topics in our data-set. For imple-

mentation purposes, we used R and the stm package

(Roberts et al., 2019). In particular, we treat each

tweet as a separate document and assign one or more

topics to each tweet. To identify a meaningful number

of topics k for our data-set, we ﬁrst ran several topic

models with a ﬂexible number of topics (between 5

and 40 topics) and then decided on the most suitable

number k, based on semantic coherence, exclusivity,

residuals, and held-out likelihood, see also (Roberts

et al., 2019). Our ﬁnal model consisted of k = 20 top-

ics (see Figure 3).

In contrast to other topic models, e.g. LDA (Blei

et al., 2003), structural topic models allow to incor-

porate covariates. For our model, we introduced a

dummy variable (“bot class”) to distinguish between

“bot” and “human” accounts. This variable was then

used as a covariate besides the creation date of a

tweet.

The “creation date” covariate was estimated via

a spline function. We added custom stopwords

and

excluded words that appeared in less than ﬁve tweets

or appeared in more than 80% of all tweets. Figure 5

shows the topics resulting from our model.

While different methods for an assisted valida-

tion of topics exist, see, e.g., (Grimmer and Stew-

art, 2013; Ramirez et al., 2012; Chan and S

altzer,

2020), we opted for a human interpretation of the re-

spective topics. To this end, we used two raters to

assign narratives to each topic based on the top 10

words (see Figure 5) and example quotes for each

topic. The inter-rater agreement (Cohen’s kappa) was

between 72.22% and 84.38% for all six groups of nar-

ratives. Disagreement cases were discussed after the

ﬁrst round of rating. Table 2 shows an overview of the

results of the procedure. Afterwards, further analysis

of the output was conducted by running a linear re-

gression using the estimateEffect function of the stm

package with the “botclass” and “creation date” vari-

ables (see also (Roberts et al., 2019)).

4 RESULTS

Among the 20 overall topics that we used in our anal-

ysis, four belong to the “operational updates” cate-

gory. These topics include breaking news content

(Topic 14 and Topic 15) describing the situation, ap-

peals to refrain from posting footage of the scene,

spreading rumours (Topic 5 and Topic 7) and requests

for staying at home or seek shelter (Topic 5). Oper-

ational updates belonged to the most prevalent topics

in our data-set.

Aside from operational updates, “blame narra-

tives” dominated the discourse (see Table 2) with ﬁve

The custom stopwords included: vienna, terror, terro-

rattack, austria, attack

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

Table 1: Basic information about the data-set.

Account type Count Tweets Retweets

Bots 27,800 (24.28%) 115,784 (29.00%) 101,209 (30.80%)

Humans 86,720 (75.72%) 283,463 (71.00%) 227,369 (69.20%)

Total 114,520 399,247 328,578

Figure 4: Effect of bot score on topic proportion with γ indicating the probability of a topic over all tweets.

topics in this category (Topics 4, 8, 11, 12, 17). These

topics covered accusations against government ofﬁ-

cials from different countries, referrals to other terror

attacks, a lack of lessons-learned from previous terror

attacks, and different types of criticism towards Mus-

lim values. Surprisingly, one blame and one victim

narrative included the term “India” inside of their top

ten terms (see Topic 6 and Topic 11). These occur-

rences were traced back to a high activity of bots as

indicated in Figure 4.

Moreover, “victim narratives” were characterised

by the expression of emotions (see Topic 6 and 13)

as well as empathy towards the victims of the at-

tack. They were tightly connected to “memorial nar-

ratives”, with the distinction of putting empathy for

victims and their family before an appeal for unity.

Only two topics have been assigned to the “hero

narrative” category. The ﬁrst one refers to three men

who carried a police ofﬁcer to an ambulance despite

the continued threat of the perpetrator (Topic 19).

Whereas, the second hero narrative (Topic 10) in-

cluded praise for the commitment of the police forces

involved and several Viennese cultural institutions

(e.g. Wiener Konzerthaus) which continued with their

(musical) performances to distract the audience and

keep them inside the premises.

The “memorial narratives” category (Topics 1, 2,

16, 20) especially includes expressions of unity and

togetherness. Sometimes these expressions reﬂected

retweeted statements of foreign government ofﬁcials

(e.g. “During a phone call with the Austrian Chan-

cellor I conveyed to him our deepest condolences fol-

lowing the terror attacks in Vienna. We stand united

with Austria in its ﬁght against extremism and we look

forward to expanding our joint cooperation on this

front”, Topic 2).

Topics pointing to future measures that should fol-

low the terror attack were assigned to the “renewal

narratives” category. For example, those narratives

include tweets about an announcement of the amend-

ment of the Austrian “Islamgesetz” which was estab-

lished in 1912 and was amended in 2015 for the last

time (Topics 9 and 18). In particular, resentment to-

wards the amendment and mentioning of critical fac-

tors to prevent terror attacks in the future (Topic 3)

dominated this narrative.

Overall, most topics were assigned to the blame (5

topics), operational updates (4 topics) and memorial

(4 topics) narrative categories. Our case study led to

the following observations.

Bots Favour Global Narratives. Figure 4 shows

how topic preference changes with respect to account

classiﬁcation. Twitter accounts that have been clas-

siﬁed as bots show a disproportionately strong con-

Automated Narratives: On the Inﬂuence of Bots in Narratives during the 2020 Vienna Terror Attack

0.0% 5.0% 10.0% 15.0%

Figure 5: Topic proportion and top 10 words per topic with γ indicating the probability of a topic over all tweets.

tribution to Topics 6, 11, and 15. The elevated bot

activity in Topic 15 might be explained by its “break-

ing news” content which has already been linked to

elevated bot activity by other studies, see, e.g., (Al-

Rawi and Shukla, 2020). Topic 6 and 11 (blame and

victim narrative) include statements of ﬁnger-pointing

and empathy with a strong relation to India (see Table

2). In contrast, topics with a strong local connection

(e.g. local heroes, comments about the Austrian “Is-

lamgesetz”) are preferred by humans rather than bots.

The prevalence of two globally connected topics (6

and 11) gives rise to the hypothesis that bot accounts

operate on a more international level whereas humans

show a preference for local narratives.

Bots Contribute to Neutral or Negative Narratives.

Bots tend to be more active in operational updates,

blame, and victim narratives – which are often as-

sociated to a negative or neutral sentiment, see also

(Ku

sen and Strembeck, 2019). Even though victim

narratives might exhibit an expression of hope and to-

getherness, their main focus lies on grief, shock, and

fear. In contrast, positive narratives, such as hero nar-

ratives, are propagated predominantly by human ac-

counts. This ﬁnding is in line with previous case stud-

ies on the behavior of bots during crisis events, see,

e.g., (Stella et al., 2018; Shi et al., 2020; Ku

sen and

Strembeck, 2020).

Bot Activity Follows a Temporal Pattern. Figure

6 shows the temporal development of each narrative

and Figure 6f provides an overview of topics with a

high bot involvement. The temporal analysis shows

that the three narratives with the highest bot involve-

ment exhibit a similar temporal pattern. Moreover, we

found that narratives with a low bot involvement show

peaks that can be linked to certain events after the at-

tack. For example, the renewal narrative from Topic

18 (connected to the “Islamgesetz”) started picking

up popularity shortly after November 9

(see Figure

6e). This development coincides with the date of a

press conference where the Austrian Minister of the

Interior announced changes in the corresponding law.

5 DISCUSSION

The ﬁndings of our study suggest that bots especially

contributed to neutral and negative narratives rather

than to positive ones. In particular, our analysis indi-

cates elevated bot participation in “victim”, “blame”

and “operational update” narratives. This ﬁnding is

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

in line with other studies of bot behavior, such as

(Stella et al., 2018; Shi et al., 2020; Ku

sen and Strem-

beck, 2020). Moreover, we found that bots prefer-

ably spread topics with an international focus and fail

to pick up local narratives, as for example the praise

for “local heroes”. Interestingly, narratives with high

bot involvement show a tight connection to India (see

Topics 6 and 11 in Table 2).

By means of a temporal analysis, we also found

that narratives showing a high bot contribution also

exhibit a similar temporal pattern (see Figure 6f).

This pattern can be distinguished from other narrative

patterns, see Figures 6 (a) to (e).

Our study is subject to several limitations. Firstly,

our data-set has been extracted from Twitter only.

Therefore, all ﬁndings only apply to social media dis-

cussions conducted via Twitter. Moreover, we col-

lected tweets based on popular hashtags that appeared

in connection with the Vienna terror attack and can-

not guarantee the full coverage of all conversations

about the event on Twitter. Therefore, our ﬁndings

should be reviewed and contrasted with results from

other social media platforms, e.g. (Wang et al., 2020;

Bolsover and Howard, 2019).

One conclusion suggested by our analysis is that

bots contribute more to global than local narratives

about the event (e.g. topics including the term “In-

dia”), which could result from the fact that the data-

set we analyzed for this case study included English

language tweets only (while the ofﬁcial language in

Austria is German). Nevertheless, we believe that our

ﬁndings can still contribute to a better understanding

of social bot behaviour. In particular, the ignorance

that bots appear to show towards local narratives is an

interesting prospect for further investigations.

We used the structural topic model approach to

analyse our text corpus. Since such topic models are

derived via unsupervised learning algorithms, it is dif-

ﬁcult to provide a robust measure for the quality of the

corresponding results. Nevertheless, for our study we

used human raters to check the results for plausibility

(see Section 3).

As discussed in (Rauchﬂeisch and Kaiser, 2020),

bot detection in general and Botometer in particular

might produce inaccurate results when used in any

other language than English. Also, the implemen-

tation of arbitrary thresholds can lead to either false

positive (humans are classiﬁed as bots) or false neg-

atives (bots are classiﬁed as humans). To reduce the

risk of incorrect bot scores for our accounts, we in-

cluded English tweets only. However, a change of

our threshold for bot classiﬁcation would certainly af-

fect our results. Therefore, we chose the threshold of

≥ 0.6 based on previous studies on large Twitter data-

sets (see Section 3) and an exploratory analysis of the

distribution of bot scores in our data-set (see Figure

2).

Based on our ﬁndings, we suggest further re-

search to investigate the role of locally emerging nar-

ratives (e.g. the “hero narrative”) during and after cri-

sis events to deduce policy measures for preventing

bots from inﬂuencing the public discourse.

6 CONCLUSION

We analysed a data-set consisting of 399,247 English

language tweets related to the Vienna terror attack in

November 2020. We used the structural topic model

approach to identify 20 topics that have been dis-

cussed along with the terror attack. In order to de-

tect narratives during and after the attack, we applied

the “narratives of crisis” as proposed by Seeger and

Sellnow (Seeger and Sellnow, 2016). The framework

suggests that ﬁve types of different narratives (blame,

victim, memorial, renewal and heroes) mainly occur

in the immediate aftermath of a crisis. Moreover, due

to Twitter’s role as a breaking news outlet (Petrovic

et al., 2021), we introduced the “operational update”

narrative as an additional category.

In order to map our topics to the six narrative cat-

egories, we deployed two human raters who assigned

a narrative to each topic. This procedure resulted in

the identiﬁcation of ﬁve (25%) blame narratives, four

(20%) operational, four (20%) memorial, three (15

%) renewal, two (10%) victim, and two (10%) hero

narratives. The most signiﬁcant contributions made

by bot accounts were found in the “operational up-

dates”, “blame”, and “victim” narratives, with two of

them having a clear international focus. In addition,

our temporal analysis indicated that bots seem to fol-

low the same temporal pattern even when contributing

to the different narratives. Since a single case study

provides a limited view only, we aim to conduct fur-

ther analyses on data-sets related to other crisis events

in the future.

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Automated Narratives: On the Inﬂuence of Bots in Narratives during the 2020 Vienna Terror Attack

(a) Topic proportion of “blame narratives”. (b) Topic proportion of “victim narratives”.

Topic proportion of “hero narratives”. (d) Topic proportion of “memorial narratives”.

(e) Topic proportion of “renewal narratives”. (f) Topic proportion of highly bot fuelled narratives.

Figure 6: Topic proportion of narratives over extraction period (02-16 Nov 2020) with the covariate day smoothed by a spline

function, with 95 percent conﬁdence intervals.

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

Table 2: Topic overview including assigned narrative, topic probability, top 10 most frequent words and an example quote of

a tweet with a high proportion of the topic.

Topic

No.

Narrative Prob. Top 10 words Quote

Topic 15

operational

update

10.97%

one, dead, injured, least, terrorist, peo-

ple, multiple, still, synagogue, austrian

“At least one killed in suspected Vienna terror attack says interior minister READ MORE (URL)”

Topic 7

operational

update

6.67%

city, can, support, inner, please, europe,

kind, way, media, dont

“If you have footage of any kind of the shooting incident in the inner city of Vienna please upload it on this link DONT

share it on social media This way you can support us”

Topic 3 renewal 6.16%

islamist, islamic, france, radical, al-

ways, can, state, news, india, leaders

“With an ofﬁcial claim of responsibility by releasing a video of the perpetrator pledging allegiance to top leader AlHashimi

alQurashi calling him an Islamic State ﬁghters is reminding us all that neither its external nor internal ops are over”

Topic 6 victim 5.90%

victims, thoughts, families, stands,

shocked, saddened, deeply, india, das-

tardly, tragic

“Deeply shocked and saddened by the dastardly terror attacks in Vienna India stands with Austria during this tragic time My

thoughts are with the victims and their families”

Topic 8 blame 5.66%

terrorists, peace, back, paris, nice,

shooting, kabul, visuals, erdogan,

spreading

“Enough with the opendoor and bleedingheart policies today more than ever we need to declare zero tolerance towards

Islamic fanatics lurking in our societies close ports control our borders protect our children defend our identity”

Topic 11 blame 5.47%

terrorism, now, india, europe, will, fake,

islamic, hope, terrorists, radical

“Europe ignored radical islamic terrorism in India. They kept teaching us fake secularism funding campaigns for fake human

rights of terrorists. Now the same terror is knocking at their door Hope they will now take it seriously GRAPHIC VIDEO”

Topic 16 memorial 5.44%

hate, terrorist, violence, pray, express,

let, peace, love, victims, police

“I express my sorrow and dismay for the terrorist attack in and I pray for the victims and their families Enough violence Let

us together strengthen peace and fraternity Only love can silence hate”

Topic 2 memorial 5.32%

people, stand, solidarity, united, europe,

austrian, ﬁght, condemn, place, full

“During a phone call with the Austrian Chancellor I conveyed to him our deepest condolences following the terror attacks in

Vienna We stand united with Austria in its ﬁght against extremism and we look forward to expanding our joint cooperation

on this front”

Topic 17 blame 5.28%

amp, people, must, world, islam, today,

terrible, faced, ones, possible

“If any holy book under any circumstances propogates killing beheading lynching owning women amp stoning gays to death

then that book amp its ignorant followers belong in the dark ages Time to call for rewrites of such problematic passages amp

to demand reforms”

Topic 14

operational

update

5.27%

several, killed, police, person, locations,

injured, shot, suspect, shooting, con-

ﬁrmed

“CONFIRMED at the moment 0800 pm several shots ﬁred beginning at Seitenstettengasse several suspects armed with riﬂes

six different shooting locations one deceaced person several injured 1 ofﬁcer included 1 suspect shot and killed by police

ofﬁcers”

Topic 12 blame 5.07%

terrorist, also, said, blame, happened,

erdogan, president, isis, government,

muslim

“I am Muslim and I blame Turkish president Erdogan for what happened in [removed] I also blame every government that

empowered and funded Wahhabist terrorist ideology. We await your apology”

Topic 5

operational

update

4.93%

public, stay, keep, dont, take, places,

away, streets, shelter, home

“Please dont stare any rumours accusations speculations or unconﬁrmed numbers of victims that does not help at all Stay

inside take shelter Keep away from public places.”

Topic 9 renewal 4.76%

just, know, islam, europe, turkey, never,

right, need, says, macron

“After the terror attack in Vienna Austria wants to ban political Islam s government wants powers to close mosques strip

citizenship and imprison terrorists for life tells you more”

Topic 18 renewal 4.46%

will, people, dear, spread, political, dan-

gerous, tweet, make, well, human

”Austria will make it a criminal to offence to spread political Islam following Islamic extremists terror attack”

Topic 19 hero 4.28%

muslim, terrorist, people, turkish, aus-

trian, injured, woman, police, old, bring

“Turkish youths who rescue an old woman in a terrorist attack in the Austrian capital Vienna and bring an injured police

ofﬁcer to an ambulance Yes these people are Muslim”

Topic 10 hero 3.69%

stop, will, safe, police, started, ever, in-

side, members, tonights, play

“Police kept us safe inside the after tonights performance While we waited members of phil started to play No [removed]

will ever stop the music in [removed] ”

Topic 4 blame 3.43%

muslims, name, europe, others, human-

ity, terrorists, years, countries, going, at-

tacked

“Terrorists attacked multiple places in Austrian capital Many dead n several others injured 1 terrorist gunned down others

on the run Europe is going to pay massively in coming years for supporting amp giving refuge to radicals in the name of

humanity”

Topic 13 victim 2.76%

religion, prayers, innocent, strong, ago,

whole, problem, religious, since, hear

“Incredibly shocked to know about the ongoing terror attack in Vienna. Having lived in that city its even more heartbreaking

to hear whats happening since last night. All my love amp prayers to the ppl of Vienna Hope the perpetrators are brought to

justice soon Stay strong”

Topic 1 memorial 2.32%

attacks, time, wish, modi, heartfelt,

hunted, prime, bless, collaborators,

namo

“Ahm deeply shocked by tterrible attacks in vienna tonight tuks thoughts are wi tfolk of austria we stand united wi you

against terror”

Topic 20 memorial 2.17%

like, country, world, european, get, war,

nothing, cant, live, day

“If someone Hurt your Religious feelings You Can 1 Protest on Social media 2 File FIR 3 File Court cases 4 Do Peaceful

protest March But You Cant 1 Behead people 2 Burn Cities 3 Loot their properties 4 Rape their women 5 Start Genocide”

Automated Narratives: On the Inﬂuence of Bots in Narratives during the 2020 Vienna Terror Attack