Mainstream and Alternative Narratives in the Wake of Gun Shootings

Lisa Grobelscheg

1,2

, Ema Ku

sen

and Mark Strembeck

1,4,5

Institute of Information Systems and New Media, Vienna University of Economics and Business, Vienna, Austria

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

Faculty of Informatics, University of Vienna, Vienna, Austria

Secure Business Austria (SBA), Vienna, Austria

Complexity Science Hub (CSH), Vienna, Austria

Keywords:

Mass Shootings, Narratives, Terror Attacks, Twitter.

Abstract:

In this paper, we explore narratives that emerge in the Twitter discourse during high stakes, low probability

events. In particular, we analyze 7.4 million tweets related to four shooting events in the United States of

America to identify differences that arise in the semantic structure and message diffusion of mainstream nar-

ratives on the one hand and alternative narratives on the other. Our ﬁndings indicate that alternative narratives

introduce keyterms that have little to no connection to the respective shooting itself and that their diffusion

patterns similar to those of mainstream narratives. Moreover, we found empirical evidence of alternative nar-

ratives, such as false ﬂag accusations, that appear across different events and persist in the Twitter-sphere over

an extensive period of time.

1 INTRODUCTION

If current news about a situation are in high demand,

e.g. during a crisis event (Stieglitz et al., 2018), in-

formation shared via social media can have a strong

impact on the users being exposed to it. It stands to

reason that certain narratives emerging in the wake

of a crisis event might even cause trauma or emo-

tional pain in users engaging in public discourse (see,

e.g., (Goodwin et al., 2018; Ku

sen and Strembeck,

2021)). In this paper, we deﬁne a narrative as a “a

set of topic-wise interconnected messages that have

been sent/posted via a social media platform” (see

also (Cunliffe et al., 2004; Weick, 1995; Grobelscheg

et al., 2022)).

Van Proojen and van Dijk (van Prooijen and van

Dijk, 2014) indicated that two factors correlate with

the probability of narratives being connected to con-

spiracy theories in the public discourse. Firstly, the

scale of consequences caused by an event and sec-

ondly, the degree of perspective-taking with the vic-

tim group of an event. Based on these ﬁndings, we

hypothesize a high potential of narratives describing

conspiracy theories in social media discourse in the

wake of crisis events. In this paper, we focus our

work on the analysis of prevalent narratives related

to gun shootings in the United States. Gun shootings

affect all classes of the population in the U.S. as most

attacks hit “soft targets”, such as concert venues or

schools (see, e.g., (Ku

sen and Strembeck, 2021)).

Following the intuition of (Starbird, 2017a) and

(Nied et al., 2017) we built on the concept of “alter-

native narratives”. As opposed to the notion of con-

spiracy theories and fake news, which inherently in-

clude false information, an alternative narrative might

also convey truthful information. A distinctive feature

of an alternative narrative is the contradiction of the

mainstream view of an event or person (Wang et al.,

2022). Building on this concept, we analyze alterna-

tive and mainstream narratives arising in social media

in the wake of gun shootings in the U.S. To this end,

we investigated semantic structures and hashtag asso-

ciations of narratives as well as user activity. In par-

ticular, we aimed to discover differences in the narra-

tive structure of alternative and mainstream narratives

in order to ﬁnd similarities in alternative narratives

across multiple events.

The remainder of this paper is organized as fol-

lows. Section 2 covers related work on the construc-

tion of narratives in social media and the emergence

of alternative narratives. Section 3 brieﬂy describes

the events considered in this paper and Section 4 out-

lines our research method and research questions. We

present our results in Section 5 and discuss them in 6.

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

Mainstream and Alternative Narratives in the Wake of Gun Shootings.

DOI: 10.5220/0011972800003485

In Proceedings of the 8th International Conference on Complexity, Future Information Systems and Risk (COMPLEXIS 2023), pages 17-26

ISBN: 978-989-758-644-6; ISSN: 2184-5034

 2023 by SCITEPRESS – Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

The ﬁnal section 7 concludes our paper and provides

an outlook for future research directions.

2 RELATED WORK

2.1 Narratives in Social Media

Narratives in social media have been investigated

from various perspectives. Li et al. used the narrative

dimensions proposed by Fisher (Fisher, 1984) to anal-

yse user health information adoption (Li et al., 2019).

They found that information scoring high in narrative

ﬁdelity (how trustworthy a story sounds compared

to the reader’s own experiences) as well as narrative

probability (how coherent a story appears) is more

likely to be adopted by users in social media. As the

credibility of information is crucial during and after

crisis events, it has been the subject of numerous stud-

ies. For example, Hardy and Miller analyzed Twitter

messages after a shooting at a Nightclub in Orlando,

Florida in 2016 (Hardy and Miller, 2022). They found

two main narrative themes among post-crisis narra-

tives, namely uniﬁcation and division. Both themes

serve a different purpose. While division provokes

future action, uniﬁcation is a means of coping.

2.2 Emergence of Alternative

Narratives in Social Media

Sometimes, the narratives in social media on the one

hand and classical mass media outlets (such as news-

papers, radio or TV channels) on the other diverge or

even contradict each other. For example, while clas-

sical mass media outlets suggested an Islamic back-

ground to the 2011 Norway attacks, the discourse on

Twitter condemned its inaccurate reporting, framing a

competing narrative (Eriksson, 2016).

Alternative narratives might especially appear in

the wake of a crisis event. By analyzing social me-

dia narratives in the wake of the Boston Marathon

Bombing of 2013, claims of the event being a “false

ﬂag” appeared online within minutes of the blasts and

kept spreading for months after the event (Nied et al.,

2017). In (Starbird, 2017b), Starbird investigates the

propagation of alternative narratives through a me-

dia ecosystem. Starbird deﬁnes alternative narratives

as narratives offering an explanation of a man-made

crisis event opposing the narrative found in classical

mass media.

Another approach used Twitter data in the af-

termath of the 2015 measles outbreak to create

a narrative structure (Radzikowski et al., 2016).

Radzikowski et al. use spatial data, important terms,

as well as special types of communication (such as

retweets) to identify a narrative of anti-vaxers. Ad-

ditionally, they applied clustering on the hashtags

used on the tweets to identify subgroups of narratives.

In addition, they differentiate between “inﬂuencers”

posting a message and “ampliﬁers” retweeting it. In

(Klein et al., 2019), Klein et al. investigated social and

linguistic features of users engaged in a Reddit con-

spiracy forum and found distinctive features in user

behavior even before users post in conspiracy forums.

Fong et al. (Fong et al., 2021) also analyzed user

data (e.g., followers and tweets) of users in a Reddit

conspiracy forum. They conducted a lexical analy-

sis using LIWC (Tausczik and Pennebaker, 2010) and

found that users proclaiming conspiracy theories in-

clude more negative emotions (e.g., anger) in their

tweets. Also words related to power, death, and reli-

gion are common. Furthermore they express stronger

orientation to the past and out- and in-group language

(“us vs. them”).

3 EVENTS OF STUDY

2017 Las Vegas Shooting. On October 1, 2017, a

mass shooting at the Route 91 Harvest music festival

in Las Vegas, Nevada killed 60 people and wounded

at least 416 others. The subsequent panic resulted in

additional 451 injuries

2018 San Bruno Shooting. On April 3, 2018, Nasim

Najaﬁ Aghdam ﬁred shots at the YouTube headquar-

ters, injuring three individuals before committing sui-

cide

2018 Santa Fe School Shooting. On May 18, 2018,

eight students and two teachers were shot dead and 13

others wounded in a school shooting at Santa Fe high

school in Santa Fe, Texas

2019 El Paso Shooting. On August 3, 2019, Patrick

Wood Crusius ﬁred shots at a Walmart in El Paso,

Texas, killing 23 people and injuring 23 others

see, e.g., https://www.reviewjournal.com/crime/shooti

ngs/las-vegas-woman-becomes-60th-victim-of-october-2

017-mass-shooting-2123456/

see, e.g., https://edition.cnn.com/2018/04/04/us/yout

ube-hq-shooting/index.html

see, e.g., https://www.click2houston.com/news/local/2

022/05/18/santa-fe-high-school-shooting-4-years-later-e

vents-planned-to-mark-anniversary/

see, e.g., https://www.buzzfeednews.com/article/mar

yanngeorgantopoulos/el-paso-shooting-white-supremacist

-terror-attack-victims

COMPLEXIS 2023 - 8th International Conference on Complexity, Future Information Systems and Risk

Days after the event

mainstream

alternative

Percent of daily Tweets

(a) Percentage of daily tweets for El Paso.

Narrative

Mainstream

Alternative

Days after the event

Percent of daily Tweets

mainstream

alternative

(b) Percentage of daily tweets for Las Vegas.

Days after the event

Percent of daily Tweets

mainstream

alternative

Days after the event

Percent of daily Tweets

mainstream

alternative

(d) Percentage of daily tweets for San Bruno.

Figure 1: Proportions of daily Tweets for alternative and mainstream narratives for days 1 to n for each event.

4 RESEARCH APPROACH

Our paper is guided by the following research ques-

tions.

RQ1: What is the Semantic Structure of Alterna-

tive Narratives? After a manual labeling procedure

of tweets into tweets conveying mainstream and al-

ternative narratives (see Section 4.1), we investigated

the language used in both types of narratives. We ap-

plied text mining techniques, such as part-of-speech

tagging (POS) and word co-occurrence analysis to

identify semantic patterns. Furthermore, we created

a hashtag co-occurrence network to analyse content

communities.

RQ2: How Do Users Engage in Alternative Nar-

ratives Across Multiple Events? To compare char-

acteristics in terms of diffusion and user engagement,

we analyzed retweet activity and user behavior across

multiple data-sets.

4.1 Procedure

Data Collection. We used Twitter’s search API to

obtain tweets related to four mass shooting events in

the U.S. The hashtags and keyterms used for the data

respective extraction procedure, as well as the obser-

vation periods are presented in Table 1. After the re-

moval of duplicate entries, our data collection counted

over 7.4 million tweets.

Deﬁning Mainstream and Alternative Narratives.

To identify and distinguish between mainstream and

alternative narratives, we used ﬁve classical media

outlets which, according to a Pew Reserach Cen-

ter report (Shearer and Mitchell, 2021), are consid-

ered mainstream media by a large proportion of U.S.

American citizens. This list includes: i) ABC News,

ii) CNN, iii) MSNBC, iv) FoxNews and v) the Wall

Street Journal.

The ﬁltering procedure for news articles in these

mainstream media outlets included the location of the

shooting and a descriptive word (e.g., Santa Fe school

shooting, El Paso shooting) as search terms for the

above-mentioned online news portals. In our study,

we considered articles published up to three months

after a shooting event. In total, we manually inspected

130 related news articles.

This procedure resulted in eight narratives, which

were subsequently categorized into three types of

Mainstream and Alternative Narratives in the Wake of Gun Shootings

Table 1: Basic information about data-sets.

Location Observation

period

Victims Users Tweets Search terms

Dead Wounded

Las Vegas (NV) 2-14 Octo-

ber 2017

60 867 1,394,070 3,436,187 #lasvegasattack, #LasVegasMassShoot-

ing, #LasVegasShooter, #LasVeg-

asStrong, #PrayforLasVegas, #Pray-

ingForLasVegas, shooting Las Vegas,

#StephenPaddock, #StephenPaddock-

IsATerrorist, #VegasStrong

San Bruno (CA) 4-10 April

2018

0 3 312,206 648,501 #YouTubeHQShooting, #NasimNajaﬁ-

Aghdam, #SanBurnoShooting, #shoot-

ingYouTube, #YouTubeHQTragedy,

#youtubeShooter, #YouTubeStrong,

#YouTubeShooting,

Santa Fe (TX) 18-25 May

2018

10 13 458,644 967,674 #DimitriosPagourtzis, #SantaFe,

#SantaFeGunControl, #SantaFe-

GunControlNow, #SantaFeViolence,

#SantaFeHighschool, #SantaFeStrong,

#SantaFeShooting,

El Paso (TX) 3-18 Au-

gust 2019

23 23 939,940 2,307,577 #ElPaso, #elpassoshooter, #ElPa-

soshooting, #ElPasostrong, #ElPa-

soterroristattack, #massshooting,

#PrayersforElPaso, #PrayforElPaso,

#walmartshooting, #domesticterrorism

mainstream narratives: pro-gun narratives, anti-gun

narratives, and general narratives, as summarized in

Table 2. For each news article, we derived the most

prevalent narrative conveyed in that very article. This

implies that an article might also include messages of

(for example) unity even though it is labeled as blam-

ing mental health issues.

The narrative of blaming mental health issues es-

pecially includes mentions of wrong or insufﬁcient

treatment of mental health disorders and illnesses, as

well as possible side effects of medication against it

(e.g., Ritalin). In messages related to the Santa Fe gun

shooting we found accusations towards (“young”)

people’s attitude towards the value of life (“life is

worth nothing”). In this light, abortion rights, video

games, and porn consumption were mentioned as the

main culprits for the act of shooting. Since many gun

shootings are directed at “soft targets” such as schools

or entertainment venues, pro-gun narratives occasion-

ally blame the security measures of these structures.

Common demands include more weapons for teach-

ers, less entry points and more security personnel. For

all events, pro- and anti-gun advocates formulated ac-

cusations for the opposite side. Anti-gun narratives

include demands for stricter gun laws or less power

for the National Riﬂe Association (NRA). General

narratives include appeals for hope and unity.

Table 2: Narratives in mainstream media.

MAINSTREAM NARRATIVE EVENT

Pro-gun narratives

blaming mental health issues, medication

against it (e.g. Ritalin)

Santa Fe,

Las Vegas

blaming attitude towards life, abortion,

porn, video games

Santa Fe

blaming vulnerability of soft targets El Paso,

Santa Fe

blaming opposing politicians, e.g. repub-

licans blaming democrats

all events

Anti-gun narratives

blaming current gun laws all events

blaming inﬂuence of National Riﬂe Asso-

ciation (NRA)

Santa Fe

blaming opposing politicians, e.g.

democrats blaming republicans

all events

General narratives

stop the hate, unite as one nation Las Vegas

Narrative Discovery in Our Collection. We build on

the conclusions of (Zappavigna, 2015) who suggest

that hashtags are widely used as topic-markers and

COMPLEXIS 2023 - 8th International Conference on Complexity, Future Information Systems and Risk

possess the ability to add structural and interpersonal

meaning. In our analysis, we used hashtags to distin-

guish between alternative narratives and mainstream

media narratives. For this task, we deployed two an-

notators who manually examined the list of hashtags

and labelled them as either “mainstream” or “alter-

native”. The annotators were guided by the list of

mainstream narratives that resulted from the news ar-

ticle inspection described above. For some hashtags,

the annotators inspected a random sample of tweets

containing the corresponding hashtags to decide on

classiﬁcation of that label. This procedure resulted

in a substantial inter-rater agreement (Cohen Kappa

79.35%).

After the labels were revised and any discrepan-

cies between the two annotators resolved, the propor-

tion of alternative narratives in the data-set was 2.2%

for the Las Vegas shooting, 4.6% for YouTube HQ

shooting, 0.9% for El Paso Walmart shooting, and

0.1% for the Santa Fe school shooting.

Semantic and User Analysis. For our semantic anal-

ysis, we used the UDPipe R package (Straka and

Strakov

a, 2017) to carry out Part-of-Speech-Tagging

(POS), word frequency, and a co-occurrence analysis.

Furthermore, we created a hashtag co-occurrence net-

work and applied the Louvain-clustering algorithm to

obtain themes in the hashtags of the four events.

5 RESULTS

Semantic Analysis. First, we investigated the corre-

lation between proportional word frequencies among

alternative and mainstream narrative tweets. Pear-

son’s product-moment correlation sample estimate is

between 0.2347 for Santa Fe and 0.3051 for El Paso

which indicates a low correlation of word proportion

between the two narrative subsets. The proportion

of original messages (excluding retweets) among all

messages accounts to 17.0415% for alternative and

18.4892% for mainstream messages. We then car-

ried out a temporal analysis of alternative vs. main-

stream narratives in order to identify differences in

their prevalence. Figure 1 depicts the temporal (pro-

portional) prevalence of alternative and mainstream

narratives. The analysis indicated a time-lag in preva-

lence of alternative narratives which is intuitive as al-

ternative narratives do not include news updates dur-

ing the event. Across data-sets, we found peaks of

alternative tweets on certain days after an event. No

obvious connection to real-world incidents could be

identiﬁed for the peaks in alternative narratives.

As described above, we applied part-of-speech

tagging to investigate structural differences. Figure

2 depicts the proportional difference in prevalence

of universal part of speech categories between al-

ternative and mainstream narrative. For example,

in alternative narratives, the proportion of verbs is

1.59% higher than in mainstream narratives. More-

over, mainstream narratives include more pronouns,

prepositions (such as in, to, during) and adjectives

compared to alternative narratives. Interestingly, the

proportion of undeﬁned tokens “X” was higher for al-

ternative narratives. The most frequent words in the

“X” category were: qanon, falseﬂag, george, good-

man, jason, webb, maga and wwg1wga (for our anal-

ysis, we excluded stop words such as lasvegasshoot-

ing).

To investigate the use of entities in alternative and

mainstream narratives, we analyzed the top 15 nouns

of each corpus and aggregated them in Figure 3. As

expected, the top alternative nouns were populated

by words associated with conspiracy theories, such

as ”wwg1wgaworldwide”, which stands for ”where

we go 1, we go all worldwide” and is used by Qanon

followers. “Fakenews”, “fakenewsmedia” and “msm”

(mainstream media) on the other hand accused main-

stream media of spreading misinformation to support

a hidden agenda. We also identiﬁed attempts to de-

clare the event as a “falseﬂag” attack carried out by

the government itself to cover-up for other actions to-

wards alleged secret goals.

Mainstream word analysis included references to

other shootings, labels such as “domestic terrorism”

and entities directly associated with the events (e.g.,

“walmart”, “victims” or “shootings”).

Furthermore, we carried out a co-occurrence anal-

ysis for each narrative and data-set. Co-occurrences

cover combinations of nouns and adjectives. Fig-

ure 4 depicts the 30 most prevalent combinations per

tweet. The number of items in each Figure varies, as

some terms might be included in multiple prevalent

combinations (e.g., for mainstream narratives for San

Bruno: gun - woman, gun - state).

When comparing word networks of alternative

narratives with mainstream narratives, positive words

that can be found in mainstream narratives (e.g.,

“prayer”, “thought”, “love”) cannot be found in the

word networks resulting from alternative narratives.

Also, across all events, we found alternative narra-

tives to be connected with conspiracy theories, such

as Qanon. Prevalent word combinations were, for ex-

ample, “deep state”, “qanon”, “george soros”. Inter-

estingly, alternative narratives often included names

of well-known individuals who were not directly con-

nected to an event itself (e.g., George Soros, Jason

Webb). This could not be observed for mainstream

narratives. We often found negative references to

Mainstream and Alternative Narratives in the Wake of Gun Shootings

alternative

mainstream

average proportion

Figure 2: Difference in UPos categories between mainstream and alternative narrative.

frequency in percent

wwg1wgaworldwide

(a) Top 15 nouns in alternative narratives proportional to all

words per data-set.

frequency in percent

(b) Top 15 nouns in mainstream narratives proportional

to all words per data-set.

Figure 3: Top 15 nouns for across all events.

“the” media (used for referring to mainstream media

outlets) in alternative narratives. For example, “fake-

newsmedia” as a central connection phrase in the El

Paso data-set.

Users Engaging in Alternative Narratives. Our user

analysis was led by two main questions, namely: “Do

certain users exclusively participate in the spreading

of alternative messages?” and “How many of the

users who are spreading alternative narratives partic-

ipate in multiple events?”. To compare our numbers,

we also analyzed users spreading mainstream narra-

tives with respect to the above stated questions.

Most users exclusively contribute to mainstream

narratives (on average 97.51% of all users across all

data-sets), whereas 1.57% (numbers rounded) engage

in alternative narratives only, and 1.91% of all users

contribute to both types of narratives. The number

of messages sent per user is slightly higher for main-

stream narratives (1.786 messages/user) as opposed to

alternative narratives (1.317 messages/user). 2.49%

of all users contributed to an alternative narrative at

least once, 7.32% of these users contributed to nar-

ratives related to multiple events. For users who en-

gaged in mainstream narratives at least once 10% con-

tributed to narratives related to multiple events. In

contrast, only 1.57% of the users who exclusively par-

ticipated in the spread of alternative narratives con-

tributed to multiple events. Therefore, we conclude

no higher repeated engagement for alternative narra-

tives as compared to mainstream narratives.

Hashtag Co-Occurrence Networks. We created net-

works from hashtag co-occurrence and applied the

COMPLEXIS 2023 - 8th International Conference on Complexity, Future Information Systems and Risk

ies

(a) Word network for El Paso - alternative narratives.

(b) Word network for El Paso - mainstream narratives.

kenews

prayforla as

veg

(d) Word network for Las Vegas - mainstream narratives.

(e) Word network for Santa Fe - alternative narratives.

texas

(f) Word network for Santa Fe - mainstream narratives.

qanon

(g) Word network for San Bruno - alternative narratives.

people

(h) Word network for San Bruno - mainstream narratives.

Figure 4: Word co-occurence networks for each event and narrative.

Louvain-clustering algorithm to obtain communities,

or hashtag themes. These networks include the most

popular hashtags for each network

. For this analysis,

We took the number of appearances of a hashtag and

used the 0.9995 percentile for El Paso and Las Vegas, 0.998

for Santa Fe and 0.995 for San Bruno

we did not distinguish between alternative and main-

stream hashtags, as our aim was to investigate if any

alternative themes would arise.

Figure 5 depicts hashtag co-occurrence networks

for all events. For the attack in Las Vegas, we found

one hashtag theme focusing on hope and prayer (e.g.,

Mainstream and Alternative Narratives in the Wake of Gun Shootings

#prayingforvegas, #prayforvegas). The other two

themes are connected to gun control and mixed dis-

cussions (see green theme). For El Paso, we ﬁnd

mainly mixed clusters, dealing with references to

other attacks (Chicago, Dayton), gun control and the

classiﬁcation of the event as “domestic terrorism”.

Our analysis indicates a strong connection (see edge

width in Figure 5a) between #domesticterrorism and

#antifa which we traced back to various tweets ei-

ther blaming or defending the Antifa movement. One

hashtag theme in the El Paso only covered the de-

bate about white supremacy and terror attacks car-

ried out by white citizens (#whitesupremacistterror-

ism, #whitenationalistterrorism).

For Santa Fe, we identiﬁed one irrelevant hash-

tag cluster, consisting of hashtags about the royal

wedding, Syria and racism. Furthermore, we found

gun control themes mixed with general information

(e.g., #santafestrong,#santafehighschool”). Another

more deﬁned cluster arose with #nrabloodmoney and

#neveragain, mainly blaming the NRA. Interestingly,

many pro-democrat tweets use hashtags usually as-

sociated with the Republican party, e.g. #maga for

“make America great again” or #gop for “grand old

party” but an investigation of sample tweets using

those hashtags found an appeal for voting against Re-

publicans. This practice is also known as “hashtag

hijacking”(Hadgu et al., 2013).

For the shooting at the Youtube Headquarters in

San Bruno we identiﬁed one hashtag theme about

the female shooter (light blue cluster in Figure 5d)

as well as clusters about gun control, Donald Trump,

and event-speciﬁc entities. This network was the only

one with an alternative hashtag among the most pop-

ular (#qanon) which was found in the same theme as

#kag (“keep America great”, which was used to sup-

port Donald Trump).

6 DISCUSSION

Alternative Narratives Use Nouns with Little Con-

nection to the Event Itself. We found alternative

narratives to be deﬁned by nouns and combinations

thereof that exhibit no direct connection to the event

itself (e.g., George Soros, Israel, George Webb). This

also included news outlets known to spread misin-

formation (e.g., Crowdsource the Truth, #csthetruth,

Jason Goodman). In addition, we found vocabulary

from conspiracy theories, such as Qanon or False

Flag

accusations across all data-sets. Based on these

False Flag accusations assume that an attack was

planned by an ofﬁcial institution, usually the Government,

to pursue some hidden agenda.

ﬁndings, alternative narrative structures can prevail

over multiple events.

Alternative Narrative Patterns Persist Across

Events. We found alternative narratives, such as false

ﬂag accusations, across all data-sets. Additionally,

we saw a strong partisan divide, which was indicated

by hashtags like #maga (make america great again),

or #bluewave (Democrats expecting an election win).

The structural patterns of alternative narratives did not

differ from mainstream narratives, though. Neverthe-

less, patterns reappeared across events, even in the

time-span of two years between the events studied in

this paper.

Alternative Narratives Diffuse no Differently than

Mainstream Narratives. In our user analysis, we

found no obvious difference in message diffusion be-

havior of alternative vs. mainstream messages. On av-

erage, mainstream messages received more retweets.

Such retweets often originate from a few breaking

news messages. We could not identify more favourite

tags for alternative tweets either.

Limitations. Our study comes with several limita-

tions. Firstly, users might try to disturb (”troll”) al-

ternative narratives to upset other users engaging in

them. Klein et al. (Klein et al., 2018) suggest be-

tween 4% - 12% of all users in a Reddit conspiracy

forum just want to annoy or provoke users engaging

in the respective discourse. This should be kept in

mind when working with alternative narratives. Fur-

thermore, our data-sets only cover four gun shootings

in the U.S. which might be too little to draw a conclu-

sion over time and across different events.

7 CONCLUSION

In our study, we analyzed tweets during and in the

immediate aftermath of four gun shootings in the

U.S. between 2017 and 2019. By analyzing main-

stream news articles, we identiﬁed mainstream nar-

ratives which were prevalent in the wake of each

event. We labeled tweets based on their hashtags ei-

ther as an alternative narrative or a mainstream narra-

tive and carried out a semantic analysis on the text

of the tweet. Thereby, we found alternative narra-

tives include much more unrelated topics without an

obvious connection to the event itself, as compared

to mainstream narratives. We also identiﬁed reoccur-

ring themes connected to conspiracy theories, such as

Qanon.

COMPLEXIS 2023 - 8th International Conference on Complexity, Future Information Systems and Risk

texas

elpasostrong

hooting

pasoterroristattack

domesticterrorism

guncontrolnow

chicago

daytonstrong

daytonshooting

dayton

whitenationalistterrorism

whitesupremacistterrorism

prayforelpa

nra

antifa

gunreformnow

walmartshooting

trum

massshootings

(a) Hashtag Co-occurrence network for El Paso.

rvegas

step

man

pray

(b) Hashtag Co-occurrence network for Las Vegas.

parkland

guncontrolnow

control

xas

nra

santafehighschool

syria

acism

themout2018

nrabloodmoney

gop

nora

royalwed

gunreformnow

neveragain

schoolshooting

maga

santafestron

enoughisen

texasshooting

iran

i ghdam

shooter

eaking

kag

news

ravelban

nasimeabz

msm

wednesdaywisdom

alyssam

guncontro

qanon

nra

nasi

breakingne

peta

(d) Hashtag Co-occurrence network for San Bruno.

Figure 5: Hashtag Co-occurrence networks for each event. Nodes in the same color belong to the same thematic cluster.

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