# Terrorist attacks sharpen the binary perception of
“Us” vs. “Them”

Milan Jović  Department of Mathematics and Informatics, Faculty of Sciences,
University of Novi Sad, Serbia  Lovro Šubelj  Faculty of Computer and
Information Science, University of Ljubljana, Slovenia  Tea Golob  School of
Advanced Social Studies, Nova Gorica, Slovenia  Faculty of information studies
in Novo mesto, Slovenia  Matej Makarovič  School of Advanced Social Studies,
Nova Gorica, Slovenia  Faculty of information studies in Novo mesto, Slovenia
Taha Yasseri  School of Sociology, University College Dublin, Ireland  Geary
Institute for Public Policy, University College Dublin, Ireland  Danijela
Boberić Krstićev  Department of Mathematics and Informatics, Faculty of
Sciences, University of Novi Sad, Serbia  Srdjan Škrbić  Department of
Mathematics and Informatics, Faculty of Sciences, University of Novi Sad,
Serbia  Zoran Levnajić111To whom the correspondence should be addressed:
<EMAIL_ADDRESS>Faculty of information studies in Novo mesto,
Slovenia

###### Abstract

Terrorist attacks not only harm citizens but also shift their attention, which
has long-lasting impacts on public opinion and government policies. Yet
measuring the changes in public attention beyond media coverage has been
methodologically challenging. Here we approach this problem by starting from
Wikipedia’s répertoire of 5.8 million articles and a sample of 15 recent
terrorist attacks. We deploy a complex exclusion procedure to identify topics
and themes that consistently received a significant increase in attention due
to these incidents. Examining their contents reveals a clear picture:
terrorist attacks foster establishing a sharp boundary between “Us” (the
target society) and “Them” (the terrorist as the enemy). In the midst of this,
one seeks to construct identities of both sides. This triggers curiosity to
learn more about “Them” and soul-search for a clearer understanding of “Us”.
This systematic analysis of public reactions to disruptive events could help
mitigate their societal consequences.

##  1 Introduction

On the evening of 13 November 2015, three suicide bombers struck outside Stade
de France in Paris. This was the beginning of the deadliest event in France
since the Second World War, culminating in a mass shooting at the Bataclan
theatre later that evening. The terrorist attacks claimed the lives of 137
people including the seven attackers’. Events such as these prompt debates
infused with anger, accusations, and hatred, but also sometimes followed by
self-reflection, introspection, and soul-searching [1, 2, 3]. Amid such
turmoil, the public focus suddenly shifts to topics and issues that were
marginal before the event and are not even necessarily related to it directly.
Events in Paris that night sparked curiosity about Islam and its history,
Western colonial legacy, and tourist spots in the Middle East, but also
triggered discussions about West and East, Christianity and Islam, and “Us”
and “Them” [2, 4, 5, 6].
Here we ask: Which topics do come to the forefront of the public discourse in
the wake of a terrorist attack? Are there any common patterns across various
attacks? How long does it take for public attention to return to routine
concerns? Past research in this direction has mostly focused on the psychology
and politics behind the terrorist attacks and their portrayal in the media [7,
8, 9, 10, 11, 12, 13, 14, 15, 16, 17], assuming that media coverage reflects
the overall public reaction. However, the topics discussed in the media are
not necessarily a complete répertoire of issues that people will become
interested in [18].
To operationalize our question we seek to identify what topics are searched
online more frequently after a terrorist attack. However, the diversity of the
search terms concerning any subject makes it difficult to use the search
engine statistics for gauging the public interest without having a predefined
list (répertoire) of keywords. Instead, we exploit the Wikipedia articles’
viewing statistics. There is substantial evidence that this viewing statistics
is correlated with Google search volume [19, 20], making it a suitable proxy
for public attention [21]. Actually, Wikipedia viewing statistics is long
recognized as an efficient tool for both understanding and predicting various
social phenomena [22, 23, 24, 25, 26, 22, 27, 28, 29, 30, 31, 32, 33, 34, 35,
36]. We can safely assume that Wikipedia covers “all” topics of potential
interest, including those awakened by terrorist attacks. And since its
information is organized into articles, mapping the public interests into
topics and categories is much more feasible. Articles are the répertoire we
need.
We studied the viewing statistics of all articles in English Wikipedia around
the time of 15 terrorist attacks in Europe (see section 4.1 and Appendix A).
By detecting the spikes in daily viewcount of specific articles we identified
the issues that the public interest is directed towards as a consequence of
these events (see section 4.2).

##  2 Results

###  2.1 Changes of collective attention around Paris 2015 attack

Focusing on the 12 days surrounding the Paris attack in 2015, we identify 17
articles that received the most attention in this period, shown in Fig. 1 (for
details on the identification method see section 4.3). Among these 17
articles, we found that five were directly related to the attack while the
remaining 12 were not. On the 12 November 2015, a day before the attack, the
largest attention was devoted to three articles related to entertainment: Prem
Ratan Dhan Payo (Hindi romantic movie released on 12 November), Spectre (2015
James Bond movie released on 26 October), and Fallout 4 (computer game
released on 10 November). The attack starts on the evening of 13 November in
Paris local time222Since this was still early afternoon in North America, many
English-speaking views registered on 13 November, even though the event lasted
until the early hours into 14 November..
This immediately diverts attention to the newly created article November 2015
Paris attacks in addition to The Islamic State of Iraq and the Levant (ISIL,
the main perpetrator) and Eagles of Death Metal (rock band that was playing in
Bataclan theatre). The attention to these three articles spiked on 14
November, but without dramatically reducing the attention on the above
entertainment articles. On 15 November the collective attention splits among
these three attack-related articles, the final fight of the women’s Ultimate
Fighting Championship (held on 14 November), and the British TV personality
Lady Colin Campbell due to her appearance in a UK reality show on the same
day.
The situation is largely unchanged on 16 November, except that attention peaks
for other two attack-related articles: Abu Bakr al-Baghdadi (leader of ISIL)
and the group Anonymous, who following the attack announced a major hacking
operation against ISIL. Meanwhile, the attention to three main attack-related
articles declined and is overshadowed on 17 November by the news that the
actor Charlie Sheen was HIV-positive. Eventually, on 20 November, attention is
taken by three entertainment releases: the album “25” by the popular singer
Adele, the detective TV series Jessica Jones, and the new part of the
dystopian movie Hunger games.
Of the five attack-related articles, by 23 November most attention is devoted
to ISIL. The viewing dynamics of all articles exhibit an intense spike
immediately after the triggering event followed by a gradual decay in days to
follow, consistently with [32, 33]. This pattern seems not to differ between
attack-related and other articles.

Figure 1: Articles identified with excess attention around the date of the
Paris attack in 2015. The color encodes the total views of a given article on
a given day (also written within each cell). Articles directly related to the
attack are shown in boldface font.

###  2.2 Collective memory of terrorist attacks

Research has shown that current events not only attract collective attention
to themselves but also trigger collective memories of similar past events [31,
32, 33, 34, 35, 36]. To examine this for the case of terrorist attacks, we
look at the Wikipedia articles devoted to the attacks in our sample and ask
how strongly each new attack triggers the attention to the previous attacks.
In the top left panel of Fig. 2 we show the viewcount time series for
Magnanville 2016 from March 2017 through June 2017. Five terrorist attacks
that happened during that interval are indicated. Manchester 2017 made the
public intensely recall the events of Magnanville 2016. London Bridge 2017 and
Westminster 2017 did so somewhat less intensely, while St. Petersburg 2017 and
Stockholm 2017 did not at all. Considering this along with the results from
the other two left panels, we note that when a terrorist attack happens, the
public is reminded of prior (similar) attacks, albeit of some more and some
less.

Figure 2: Collective memory of terrorist attacks. Three left panels: Time
series of viewcounts to articles for Magnanville 2016 (top), Nice 2016
(middle), and Berlin 2016 (bottom), shown for the period of four months from
March to June 2017. The dates of five terrorist attacks that happened during
that time interval are indicated. Right panel: the excess attention $Z$ was
received by each attack as triggered by later attacks. Attacks are arranged
vertically (and horizontally) in chronological order from top to bottom (left
to right). Values of $Z$ for the three panels on the left panel are marked by
a bold square.

To study this collective memory more systematically, we focus on a quantity we
call “excess attention” denoted with $Z$. It expresses how strongly a newer
attack triggers the attention to an older attack (see section 4.4). The
calculated values of the excess attention are shown in the right panel of Fig.
2. Most events have triggered considerable excess of attention to the past
events, confirming the existence of a collective memory of terrorist attacks
in Europe. The strongest reactions are found for pairs of attacks that
happened in the same country in line with the findings of [31]. Also, the time
elapsed since the attack seems to be a strong factor, as attacks that happened
a long time ago are seldom remembered.

###  2.3 General patterns in public reaction across attacks

Reaction to a terrorist attack is shaped by many factors, primarily by the
media portrayal of the event, but also by the perceptions established in the
target society about itself and the perpetrators. Searching Wikipedia is a
matter of individual choice, but this choice is founded on an array of social
narratives that entail symbols, stories and memories of a given group, and
turns them into a shared conceptual system [37, 38]. What narratives underpin
the searching choices in Wikipedia after a terrorist act? To answer this, we
identify the universal effects that attacks have on the collective attention.
Starting from all Wikipedia articles (section 4.2), we deployed an exclusion
pipeline combining time series analysis and qualitative approaches (sections
4.4 and 4.5), to extract 69 articles to which attention is consistently drawn
after terrorist attacks. We then manually grouped them into 19 specific
categories according to the commonalities of their thematic codes (section
4.6). Finally, by finding coherent patterns among specific categories we
grouped them into four broad categories (section 4.7). This arrangement is
illustrated in Fig. 3 and schematized in the Appendix B. It can be considered
in a hierarchy, whereby individual articles belong to one of the specific
categories, and each specific category belongs to one broad category. Broad
categories represent a coarse, yet robust and interpretable level of
description. They are the four core overarching groups of interests that
always arise after terrorist attacks.

ENEMY

It is easy to recognize that the concept of enemy, identified as a threat,
underpins specific categories related to both terrorism and conspiracies. The
basic construction of the enemy goes in two ways: the Islamic extremist, a
person driven by extremist ideology posing a danger and/or the terrorist, an
individual who is relying on terror as the method of achieving goals in the
West and elsewhere. We interpret this as a natural reaction of the English-
speaking public to a terrorist attack: Establish the perpetrators (Islamic
extremists) as the enemy, and then learn about their methods and actions,
especially in the West.

SECURITY

National security, weapons, transport, insurance, and survival equipment have
the idea of security as the common denominator. Seeking shelter is a natural
response to the threat of the enemy. Being under attack puts us into fight or
flight mode: either to confront the enemy or escape from the threat. Fight
mode corresponds to the interest in armed forces, military equipment, police,
conscription, and usage/purchasing of weapons. The public wants to rely on the
official national security mechanisms, but also be ready for individual
defence (“take matters into their own hands”). Flight mode is found in the
interest of means of transportation (ways to escape), insurance (financial and
otherwise), and equipment for survival in the wild (the ultimate shelter in
disruptive circumstances).

OTHER-PERCEPTION

Once established who is the enemy and how to defend/escape from it, one wants
to learn about the enemy. We recognize this as the common motif in pronounced
interest for Islam and Muslims, which West often perceives [4] as the
archetype of the “Other”. Of course, this role has deep historical and
cultural roots, which quickly spring out upon a terrorist attack. Induced by
it is also a curiosity about the practices of Islam, its origins and history,
sacred texts, religious figures, presence in the West and elsewhere, and even
tourist attractions in the Middle East.

SELF-PERCEPTION

Finally, one wants to soul-search and re-evaluate who “We” are. This
establishes a self-perception that contrasts the perception of “The Other” and
relies on both historical and imagined features of the West. It is centered
around Western national/religious identities and political activism but also
includes anti-Islam groups and figures. Interestingly, there is a large
curiosity about violent historic events orchestrated by the Western powers.
Given the context of the terrorist attack, it is not surprising that self-
perception is built upon (equally) violent aspects of the West. On the other
hand, this can be interpreted as a form of self-criticism, a realization that
the West has always been violent regardless of Islamic terrorism (“they are
bad, but we are no angels either”).

Figure 3: Organization of 69 articles that survived all the filters (Table 1)
into specific and broad categories (Appendix B). Four broad categories are
illustrated by different colors and positioned as to capture the relationships
among them, i.e., the distinctions along two independent dimensions:
Establishing boundaries and Constructing identities. Two broad categories on
the left explain “Them”, while the two on the right explain “Us”. Specific
categories are shown schematically by their names, whereby in parentheses we
report the number of articles belonging to each specific category.

The organization of the broad categories can be viewed along two independent
dimensions. First, it is easy to recognize that two broad categories on the
left, Enemy and Other-perception, describe “Them” in very general terms. We
are compelled to learn about them, not just as the enemy, but to clarify who
they are and how shell we perceive them. We look for a wider context of where
the enemy comes from and why. In contrast, two broad categories on the right
describe “Us”. They amount to the construction of a sense of belonging and a
safe community, “Our” community. This leads to the introspection of who we are
in face of the enemy. Both “Us” and “Them” can be recognized sharper when
placed in opposition to each other. This binary distinction can be interpreted
as Establishing boundaries: Enemy vs Security, threat/danger vs
safety/belonging, unknown vs known, Other vs Self, “Them” vs “Us”.
Second, two broad categories in the bottom amount to Constructing identities.
The disruptive situation makes the public interested not only in the immediate
concerns but in a more comprehensive and abstract perspective on the event.
The public becomes curious about Islam not exclusively from a retaliatory
perspective. On the other hand, that same public is looking for historic and
current facts about Western nations and their political/social organization,
neither of which is immediately related to the attacks. Both arising interests
point to the need for an identity, i.e., the understanding of how “We”
perceive ourselves in face of the “Them”. This elucidates how we establish the
boundary between what we see as shared within our community in contrast to
whatever is outside of it (and potentially threatening). Interestingly, public
also gets curious about Islam’s presence in the West – the place where the
“They” meet “Us”.
Next, we examine the 15 terrorist attacks through lenses of excess attention
$Z$. For each attack, we calculate its total excess attention by summing over
all broad categories. This leads to a single value of $Z$ for each attack,
reported in the left panel of Fig. 4. We find the most intense reaction to
Paris 2015, Manchester 2017, London Bridge 2017, and Marseille 2017. Other
attacks trigger weaker but still noticeable reactions. We could not find
meaningful correlations between these values and quantifiable parameters
surrounding the attacks, such as the number of deaths or injuries, as it was
done in [21]. Still, we noticed that attacks that took place in UK and France
seem to generate somewhat more diversion than those occurring elsewhere.
Interestingly, a major attack in Brussels in 2016 triggered comparatively
weaker interest.
Looking at excess attention of four broad categories concerning the attacks
(right panel of Fig. 4), we find that the attention was most intensely
diverted to the Enemy category after Paris 2015, and somewhat less so after
Manchester 2017 and Marseille 2017. Turku 2017 triggered no interest in the
Enemy, while Barcelona 2017 and Liege 2018 exhibit very weak interest in the
Enemy. We found the weakest overall reaction in the Security category, where
seven attacks did not react at all. In contrast, London Bridge 2017 has an
extreme reaction in this category. Redirection to the Other-perception
category is somewhat related to that of the Enemy category. Many attacks show
similar values for both, with Manchester 2017 and Marseille 2017 being the
highest. For the Self-perception category we find moderate values across all
attacks, but not much correlated with the Security. The intensities of
reactions across four broad categories are unique for each event, the
strongest being for Paris 2015. This paints the picture of each attack having
a context of its own and impacting the public in a particular way.

Figure 4: Intensity of the public reaction due to terrorist attacks, which are
shown vertically by their abbreviations (section 4.1 and Appendix A). Left
panel: total Excess attention for every attack aggregated cumulatively over
all of the broad categories. Right panel: total Excess attention for every
attack broken down over four broad categories, which are shown horizontally.
The color bar is truncated for Excess Attention $>80$ for better clarity.

###  2.4 Returning the collective attention to ‘normal’

After an attack, how gradually is the public attention restored to routine
concerns? Does this depend on the attack and/or the broad category? We look at
three examples: Paris 2015, Brussels 2016, and London Bridge 2017. For each of
them, we construct a time series that shows the decay of public attention to
each broad category. Results for the period starting a week before the attack
to three weeks after the attack are shown in Fig. 5.
After every attack, the decrease pattern of collective attention is similar
for broad categories of Enemy, Security, and Other-perception, but it is
unique to each event. Specifically, for Paris 2015 we find the slowest
decrease followed by a sudden increase three weeks after the event in all
three broad categories. In contrast, the patterns for Brussels 2016 and London
Bridge 2017 are very similar: the decrease is relatively constant and quick
for Enemy and Security, and somewhat more gradual for Other-perception. The
values three weeks after these two attacks are similar to those before the
attacks. This is not the case for Paris 2015, which again confirms that it was
a unique event, with effects persisting long after the event and even peaking
again after three weeks. In opposition to these three broad categories, we
find a different situation for Self-perception. The behavior for Paris 2015 is
close to that of its other three broad categories. For Brussels 2016 and
London Bridge 2017 we find a rather irregular pattern, characterized by a mix
of decay and sudden spikes. These observations suggest that this broad
category, pointing to introspection and soul-searching, is the longest-lasting
effect of the terrorist trauma.

Figure 5: Dynamics of decay of collective public attention after terrorist
attacks. Three top row panels: the cumulative time series of viewcounts for
Paris 2015, Brussels 2016 and London Bridge 2017, for the broad category
Enemy. The time of each attack is marked by a vertical line and the period
after it is shaded in grey. Second, third, and fourth rows of panels: the same
for the other three broad categories.

##  3 Discussion and Conclusion

Our findings are three-fold. First, the exhaustiveness of Wikipedia enabled us
to systematically study how the public prioritizes their interests in the wake
of a terrorist attack and how this arrangement of interests differs from the
usual trends. Second, we precisely measured which among them receive more and
which had less attention. Third, we found consistent patterns in these awoken
interests, grounded in the existing ecosystem of beliefs in the affected
European societies.
The reaction of the international English-speaking public to the terrorist
attacks reveals the deeply rooted binary distinction between East and West,
Islam and Europe, “Them” and “Us”. Amid shock, a natural human (individual and
collective) reaction is to search for a community to belong to. This community
has to have a meaning and identity. But the identity is always relational and
constructed in opposition to “Others” [39, 40], perceived as outsiders or even
enemies. The enemy serves as a mirror offering a (real or imaginary)
distinction between our group and their group. This makes “Others” an
indispensable ingredient for creating feelings of belonging. When this becomes
a part of the collective social imaginary, it further fortifies the boundaries
between the groups. In the Western(ized) world, attitudes towards Islam are
often grounded in reluctance, fear, and binary distinctions [4]. Terrorist
acts – especially if perpetrated by the religious extremists – are ideal
mechanisms for recreating this effect.
In conjunction with a community and knowing what is and what is not part of
it, we also need an understanding of “Us”, a narrative about who we are. This
narrative is to contrast the narrative about “Them”, which is to explain who
they are. Both narratives are usually articulated as a series of attributes
about either side, as clearly revealed by the found broad categories.
Specifically, the narrative of threatening Enemy to which we respond by
Security contributes to the symbolic substance of “Us”, who are residing on
“our” side of the boundary [40]. The social imaginary of the community
pertains to a discursive manner of self-understanding, emerging via stories
that people tell about themselves and thus construct their collective
belonging [41]. In that regard, identities serve as narratives that enable the
continuity of a community, form the foundation of the social collective
memory, and unify a group through space and time [42, 43].
The imminent threat may generate other ways of defining the enemy. A prominent
human feature is to search for patterns in seemingly chaotic situations while
maintaining the feeling of connection. We found this in the interest for
conspiracies, which replace a visible enemy with a manufactured one. Worrisome
adherence to online conspiracy theories is a large topic within computational
social science [44, 45, 46, 47].
The last decades saw individuals’ lives grow apart from large bureaucratic
institutions [48]. Individualism and consumerism caused the erosion of the
collective loyalties, boosting the scepticism towards the state authority.
This trend has been already observed in sociological classics [49] and
accelerated by modern technology and globalization [50, 51, 52].
Interestingly, terrorism seems to push in the opposite direction, towards re-
establishing the bond with (presumably) protective institutions, operating at
national level and provided by state authority.
We also found that intense focus on topics arising from terrorist attacks
dissipates after a few weeks. Typically, it is overwhelmed by the never-
yielding interest in entertainment. This is hardly surprising, given that the
public is known to shorten its attention span [32, 33], even during the COVID
crisis [34]. This is in agreement with one of our results, namely, that the
public tends to collectively remember only the most recent attacks (cf. Fig
2). In Appendix C we show a side result: The amount of collective attention
redirected from other routine interests due to terrorism is insignificant from
the perspective of overall viewing behavior on Wikipedia (about 200 to 300
million views per day with weekly and seasonal oscillations). In other words,
none of the attacks was ‘that interesting’ to divert major and lasting
attention from entertainment.
Individuals’ prioritization of topics expresses narratives that he/she was
brought up with. These involve social memories as a basis of collective (for
instance national) identity, which includes cultural norms, notions of
authenticity, power relations, etc. Our findings indicate that for
international English-speaking audiences these narratives are centered around
security, enemy, self-perception, and other-perception. In them we recognize a
search for belonging and meaning in a globalizing late modern society,
triggered by a sudden disruption. These results are in accord with the
classical studies on the relationship between Europe (the West) and its
neighbours (the East), referring especially to the collective perceptions of
the “significant other" [4]. They call for reconsidering the classical ideas
of nationalism, thus advancing the constructivist understanding of society
[40], which is detached from the actual physical places [53]. In that regard,
we are consistent with the contemporary scholarly consideration of belonging,
which is increasingly shifting towards transnational and virtual social
reality, articulated through imagined communities and forged via “fragile
communication bonds” such as common usage of English Wikipedia [54, 55, 56].
Our results could benefit from an independent verification via sources other
than Wikipedia, primarily involving social media. Still, the popularity of any
tweet could be due to lack of a systematic répertoire of tweets, rather than
genuine public interest in that tweet’s content. On the other hand, what else,
besides terrorist attacks, diverts attention to the 69 articles from Table 1?
Perhaps some articles react only to the terrorist attacks and could serve as
‘markers’. We examined this in the Appendix D and found no evidence for them.
Another methodological challenge is the robustness against the modifications
of exclusion criteria. We check this, and found that key results are robust
against all meaningful modifications. For more on this and other robustness
checks see Appendix E.
There are two main directions for advancing the presented work. First,
spontaneous reaction to disruptive situations naturally involves languages
other than English, both in Wikipedia and elsewhere. This makes online sources
in other languages prime target of future work. Second, from how we made our
sample of attacks, it just so turned out that all 15 of them were committed by
Islamic extremists. But what about terrorist acts perpetrated, for example, by
Western far-right groups? We fully acknowledge that terrorism can have very
diverse origins and that public reactions to it must be investigated.
Our conclusion is that while unfortunately terrorism is not likely to vanish
soon, studies like this one can help understand the intricacies of the public
reactions to it, and minimize the consequential societal damages such as
extremism and polarization.

##  4 Data and methods

###  4.1 The sample of terrorist attacks

We considered all acts of terrorism in Europe starting from November 2015
Paris attacks until the end of 2018. Using Wikipedia’s information, we found
43 such acts but kept only those with at least two casualties excluding the
attackers. This led us to a sample of 15 terrorist attacks across Europe (See
Fig. 6 for the geographical location and the timeline of the incidents).

Figure 6: Locations (cities) of 15 terrorist attacks comprising our sample.
Attacks occurred between November 2015 (Paris) and May 2018 (Liege), as
illustrated in the inset. See Appendix A for a comprehensive overview and
details of each attack.

###  4.2 Data preparation

Our overarching aim is to examine the visits to all articles in English
Wikipedia during the relevant period and identify those articles that are
visited more frequently (receive excess attention) after a terrorist attack.
We started by establishing a list of entries that can be considered valid
articles [57, 58]. As of August 2019, this list contained 14,733,158 entries.
Through Wikimedia API [59] we can obtain the exact viewcount (number of
visits) for an article on any day. This daily viewcount is a great proxy for
the attention devoted to that topic by the international English-speaking
audience. We retrieved the time series of daily viewcounts for the entire list
with 14,733,158 entries from 1 January 2015 to 8 August 2019 (1680 days). Each
value is the total number of visits to an article on a given day, cumulatively
generated by entire online traffic from all devices with access to the
Internet. We want to examine only those articles whose viewcounts spiked after
terrorist attack(s) and whose content can be meaningfully connected to the
attack. After the extensive exclusion procedure described below, we identified
69 such articles.

###  4.3 Sampling the articles around the time of Paris 2015 attack

We sample the articles for Fig. 1 by considering the 12 days surrounding the
event. We found this time window most suitable for illustrating the dynamics
of collective attention around the attack. We looked at the top 5 articles by
viewcount on each of these 12 days. We then selected articles, that on at
least two days (of these 12 days) received more than 100,000 views. This gave
us the sample of 17 articles, shown vertically in Fig. 1.

###  4.4 Definition of Excess attention

Excess Attention, $Z$, is defined as follows. Let $A$ be a Wikipedia article
and $T$ the time of a specific attack. The mean $\mu(A,T)$ and standard
deviation ${\sigma}(A,T)$ of viewcounts of $A$ before the attack are
calculated starting from 1st of January 2015 as:

$\mu(A,T)=\frac{1}{\textit{T-1}}\sum_{\textit{t=1}}^{\textit{T-1}}w(A,t)\;\;,\;\;\;\;\;\;{\sigma}(A,T)=\sqrt{\frac{1}{\textit{T-1}}\sum_{t=1}^{T-1}\Big{(}w(A,t)-\mu(A,T)\Big{)}^{2}}\;\;,$

where $w(A,t)$ is the viewcount of $A$ on the day $t$. Next, we compute
$\nu(A,T)$, the average viewcount of $A$ in the week following the attack:

$\nu(A,T)=\frac{1}{7}\Big{(}w(A,T)+w(A,T+1)+\ldots+w(A,T+6)\Big{)}\;\;.$

Our choice of a week (7 days) as the period for averaging is motivated by
pronounced weekly oscillations of overall viewcounts and in line with [21]
(see also Appendix C). Now we define $Z(A,T)$ as:

$Z(A,T)=\frac{\nu(A,T)-\mu(A,T)}{{\sigma}(A,T)}\;\;.$ (1)

Excess Attention measures the increase of mean viewcounts in a week after the
event compared to the mean viewcount before the attack, expressed in the units
of standard deviation characterizing the trend prior333Our measure of Excess
Attention is conceptually similar to $Z$-score, even if it is not exactly the
same. It can be computed for any article $A$ in association with any attack
$T$. For example, if an article displays $Z=3$ in association with $T$, it
means that in the week following $T$ the visits to $A$ have increased by 3
standard deviations compared to the average before $T$. This is our main tool
in identifying the articles that have reacted to an attack in contrast to
those that did not.
For Fig. 2 (right panel), since we need a more stable estimates of $\mu$ and
${\sigma}$, we exclude the first 2 weeks after the older incident. For Fig. 4
(left panel), we assigned the total (cumulative) $Z$ to each attack by summing
$Z$s for all articles that reacted to that attack, cumulatively across all
broad categories. We did the same for Fig. 4 (right panel), but summarizing
each broad category separately.

###  4.5 Article exclusion steps

#### Step 1: Removal of stubs and redirects.

Wikipedia uses the term “stub” for abnormally incomplete articles. Using the
stub tags we identified and removed them. We also removed articles that only
carry an alternative title and a link to the original article with the more
conventional title (“redirects”). Starting from the complete list of articles
in English Wikipedia, excluding all stubs and redirects left us with 5,876,878
articles.

#### Step 2: Exclusion of articles with low viewership.

To filter our low view articles, we used a cut-off based on the maximal daily
viewcount. We kept only the articles whose daily viewcount at least once
reached 300. See Fig. 7 for the distribution of maximum daily views and our
cut-off determined based on the “elbow rule”. This step left us with 912,192
articles.

Figure 7: The distribution of maximal daily viewcounts (maximal number of
views reached on a single day) between 1 January 2015 and 8 August 2019, for
all 5,876,878 articles after Step 1. We kept only the articles in the shaded
area, i.e., excluded those that were never visited more than 300 times on a
single day. Note a slight change of slope near 300, making this a natural
choice for cut-off.

#### Step 3: Removal of articles with small Excess Attention after attacks.

Next, for each attack separately, we exclude articles that received no marked
increase in attention immediately after the incident (true negatives). As
threshold we use $Z=3$: Assuming that viewcounts are normally distributed, the
chance of observing $Z\geq 3$ at random is $p\sim 0.001$ (see also Appendix
E). Upon excluding articles with $Z<3$, we were left with 9,000 to 24,000
articles per attack. To better illustrate this step, we show in Fig. 8 the
viewcounts for three example articles around the time of Paris 2015. This
filter is successful in detecting articles whose increase in viewcounts is
related to the attack (true positive), but unsuccessful in filtering out
articles that had an increase in viewcounts due to other unrelated events
(false positive). An example is G20 that received spike in attention due to
G20 meeting that accidentally took place around the same time as Paris 2015.
We deal more with false positives later.

Figure 8: Viewcounts of three articles around the time of the November 2015
Paris attack. Attention on the article in red peaks immediately after the
attack, and given its content, we reasonably expect that this was a reaction
to the attack (true positive). The article in grey exhibits no qualitative
change, and indeed, given its topic (a city in Serbia), we did not expect any
(true negative). The attention is devoted to the example in blue peaks, but
this article’s content precludes us to attribute this to the attack (false
positive).

#### Step 4: Automated removal based on content

Inspecting the articles which passed the previous filters, we noticed common
patterns in their topics, pointing to an overarching picture of the arising
interests. Still, as too many events occur simultaneously, there were still
too many false positives. To remove them, we employed semi-supervised Google’s
text-miner BERT (Bidirectional Encoder Representations from Transformers) [60,
61]. It outputs a score that expresses how similar is the content of some text
to the content of the training text. We prepared the training text by taking a
random sample of 10% of articles for each attack and manually filtering out
the articles which were not conceptually related to the attacks. We then
extracted the summaries444Wikipedia calls ‘summary’ a brief description of the
article’s content, available immediately after the title. of all remaining
articles and trained BERT on them. Then, we had BERT recognize the similar
content in the summaries of all articles surviving Step 3. Upon completion, we
were left with 1000 to 8000 articles per attack. We evaluated BERT’s
performance by manually inspecting a 10% sample of thus excluded articles and
indeed found them all true negatives.

#### Step 5: Distilling the reactions.

Our next aim is to identify themes that systematically appear in public
interests and are not connected to a single attack. We put the surviving
articles back together and remove those that reacted to less than three
independent attacks, not counting as independent the reactions to events in
the same week. In establishing what constitutes a reaction to an attack, we
made two additional requirements:

  1. 1.

Looking at some obvious false positives, we noticed that their viewcounts
start increasing before the attack and hence clearly not due to the attack. To
account for this we introduce $Z_{0}(A,T)=Z(A,T-7)$, the $Z$-score for a week
immediately before the attack. We found $Z_{0}$ to be unusually large for most
false positives. Yet, a slightly increased $Z_{0}$ is not problematic as long
as it is followed by a considerably higher $Z$ post-attack. For consistency
with earlier steps, we requested

$Z(A,T)>2+\frac{3}{3-Z_{0}(A,T)}\;\;$

for every reaction of $A$ to $T$. Hence, for an article with $Z_{0}=1$ we
request at least $Z=3.5$, for articles with $Z_{0}=2$ at least $Z=5$, etc.

  2. 2.

Since the mean $\mu(A,T)$ appears in the denominator of Eq. 1, $Z$ is more
volatile when $\mu(A,T)$ is small. An unusually high $Z$ found for $A$ with
small $\mu$ could easily be an artifact (division by a number very close to
zero). To account for this, we excluded the reactions of articles $A$ to
attacks $T$ whenever we found $\mu(A,T)<20$.

See Fig. 9 for illustration. This step left us with 86 articles.

Figure 9: Examples of four articles identified in Step 5. Top left: viewcount
time series of Terrorist incidents linked to ISIL from 1 March 2017 to 1
September 2017. Attacks that occurred during those 6 months are indicated. Top
right: the histogram of $Z$-scores for that article for the entire time span
(region $Z>3$ is shaded). Other panels: same as on the top panels (left and
right) for other three examples of articles. These plots demonstrate how well
the threshold $Z=3$ separates day-to-day fluctuations from what we can
consider a reaction to an attack.

#### Step 6: Qualitative (manual) exclusion based on content.

We next inspected a broader context of every remaining article around the time
of each observed reaction. We assigned a thematic code to each article using
open coding [62] based on two criteria:

  * •

consideration of the articles’ content and its thematic relevance from the
perspective of each particular attack,

  * •

timing of its appearance in terms of accidental coinciding with other
unrelated but simultaneously occurring events (possible false positives).

We excluded 17 articles as we found them to coincide with: sporting events;
launching of movies and TV shows; music events, personalities and their media
coverage; and other accidentally coinciding events (solar eclipse, legislation
procedures, cultural events, etc.). There were three articles on the Syrian
civil war: the Palmyra offensive in March 2016, the Russian offensive in
November 2015, and the US response to the alleged chemical attack by the
Syrian regime in April 2017. Upon very close inspection we could not connect
them to the terrorist events in Europe. This left us with 69 articles, shown
in Table 1.

Andrew O’Hagan | Kyle Kulinski | Devin Nunes
---|---|---
James Wickstrom | Paul Weston (politician) | Sebastian Gorka
Tommy Robinson (activist) | False flag | Orvis
TD Banknorth | Thornburg Mortgage | Moinuddin Chishti
Islam in Qatar | Ed Husain | Islam in Poland
List of mosques in the United Kingdom | Quilliam (think tank) | Susa
Juan Carlos I | Conscription in the United States | Crisis actor
Kimberly Dozier | List of police firearms in the United Kingdom | Police use of firearms in the United Kingdom
Ring of steel (London) | Spanish Armed Forces | UK Threat Levels
USS Cheyenne (SSN-773) | Church of the Holy Sepulchre | Lone wolf (terrorism)
Mass shooting | Massacre | Bhagwan Shree Rajneesh
List of terrorist incidents in Australia | List of terrorist incidents in Great Britain | List of terrorist incidents in London
Terrorism in Europe | Terrorism in Greece | Terrorism in Sweden
2002 Los Angeles International Airport shooting | 2004 SuperFerry 14 bombing | 2007 Glasgow Airport attack
2010 Moscow Metro bombings | Abduction of Russian diplomats in Iraq | List of terrorist incidents linked to ISIL
Muslim Brotherhood | Rick Leventhal | Terrorism in the Philippines
Basque conflict | Basque National Liberation Movement | Timeline of Real Irish Republican Army actions
Big Sky Airlines | Boston-Maine Airways | Corporate Air
Dakota, Minnesota and Eastern Railroad | Eclipse Aviation | Charles B. McVay III
Grdelica train bombing | Greek War of Independence | Legitimacy of the NATO bombing of Yugoslavia
List of events named massacres | The Man with the Iron Heart | Assault weapon
Comparison of the AK-47 and M16 | Gun culture in the United States | Gun politics in the United States
Jews for the Preservation of Firearms Ownership | United States v. Miller | Universal background check
Table 1: The list of 69 English Wikipedia articles that have reacted to at
least three independent attacks. They jointly represent the content to which
terrorist acts divert public attention (irrespective of the particular
attack’s details).

###  4.6 Construction of specific categories

Table 1 is a succinct list of what the public becomes interested in following
a terrorist attack. Not surprisingly, it includes terrorism, weapons, and
anti-Islamism, but also national identity, the violent history of the West,
and conspiracy theories. As many articles have similar content, we used axial
coding [62] to extract their thematic codes and connect them. By this, we
manually cluster the articles into meaningful groups, defined by the coherency
of their content. We call these groups specific categories and assign a name
to each of them that summarizes the content. The 69 articles from Table 1 are
arranged into the following 19 specific categories.

  1. 1.

Terrorism in general. Definitional aspects of terrorism, such as what
constitutes a mass shooting and what is a massacre (3 articles).

  2. 2.

Islamic terrorism. Past Islamic terrorist acts and organizations, regardless
of location, context, and targets (9 articles).

  3. 3.

Non-Islamic terrorism. Far-right and far-left terrorism and extremism in the
West, not related to Islam (3 articles).

  4. 4.

Terrorism in Western countries. Summaries of terrorist activities in the West,
including those not part of our sample, regardless of religious/ideological
origin (7 articles).

  5. 5.

Conspiracies. Possible conspiracies behind or related to the attacks (1
article).

  6. 6.

National security. Western security and defence institutions, their official
weaponry, conscription policies and related legal frameworks (9 articles).

  7. 7.

Weapons. Weapons for personal defence and comparisons among them (e.g. AK47
vs. M16), firearms legislation and purchasing regulations (7 articles).

  8. 8.

Transport options. Public transportation infrastructure, including air travel,
railroads, and highways (5 articles).

  9. 9.

Insurance options. Financial security and purchasing of insurance (2
articles).

  10. 10.

Equipment for survival. Purchasing equipment for surviving in the wild (1
article).

  11. 11.

Islam in general. Islam religious figures (1 article).

  12. 12.

Islam in Western countries. Muslim religious institutions, figures, and
practices in the West (4 articles).

  13. 13.

Islam in Non-western countries. Role and history of Islam in non-Western
countries (1 article).

  14. 14.

About Middle East. Locations and history of the Middle East (1 article).

  15. 15.

National identity. Generalities about Western nations and their political
order (1 article).

  16. 16.

Religious identity. Christian religious institutions in the West (1 article).

  17. 17.

Activism in general. Western political activists and writers, not related to
Islam (2 articles).

  18. 18.

Anti-Islam activism. Western far-right politicians, public figures and anti-
Islam extremists (5 articles).

  19. 19.

Violent history of Western countries Violent historic events in the West,
famous battles in the two World Wars, local wars of independence (6 articles).

Arrangement of individual articles from Table 1 into above 19 specific
categories is shown in form of table in Appendix B.

###  4.7 Construction of broad categories

The above organization of articles into specific categories is not immune to
ambiguities, such as placing an article into one category or another or
merging or splitting categories. We resolve this by noticing similarities
across specific categories’ thematic codes. For instance, categories 1–4
revolve around terrorism, whereas categories 11–14 deal with varying aspects
of Islam and its relation to (real or perceived) perpetrators. This allows us
to coarse-grain 19 specific categories into four broad categories: Enemy
(Specific categories 1–5, 23 articles), Security (Specific categories 6–10, 24
articles), Other-perception (Specific categories 11–14, 7 articles), and Self-
perception (Specific categories 15–19, 15 articles).
We verified that the topical coherency of broad categories is robust against
the above ambiguities and even against the most meaningful modifications of
the exclusion choices (e.g. requesting the reaction to only two independent
attacks). To further increase the reliability and validity, we sought to
define their thematic codes in the context of discourse analysis. We applied
intra-coding in terms of re-checking broad categories’ thematic codes 12
months after the initial coding process and found them to be consistent555We
decided against deploying inter-coded reliability since we did not find it
compatible with recursive and incremental nature of our coding process.. Thus
constructed broad categories are universal and interpretable as the broad
public interests awakened by any terrorist act.

## Acknowledgements

We acknowledge the support of the Ministry of Education, Science and
Technological Development of the Republic of Serbia (Grant No.
451-03-68/2022-14/200125) and by the Slovenian Research Agency (ARRS) via
programs P1-0383 and P5-0168, and via project J5-8236. Special thanks to
Novica Bjelica for help with setting up the datasets.

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## References

  * [63] Wikipedia  “November 2015 Paris Attack”  URL: https://en.wikipedia.org/wiki/November_2015_Paris_attacks
  * [64] Wikipedia  “2016 Brussels bombing”  URL: https://en.wikipedia.org/wiki/2016_Brussels_bombings
  * [65] Wikipedia  “2016 Magnanville stabbing”  URL: https://en.wikipedia.org/wiki/2016_Magnanville_stabbing
  * [66] Wikipedia  “2016 Nice truck attack”  URL: https://en.wikipedia.org/wiki/2016_Nice_truck_attack
  * [67] Wikipedia  “2016 Berlin truck attack”  URL: https://en.wikipedia.org/wiki/2016_Berlin_truck_attack
  * [68] Wikipedia  “2017 Westminster attack”  URL: https://en.wikipedia.org/wiki/2017_Westminster_attack
  * [69] Wikipedia  “2017 Saint Petersburg Metro bombing”  URL: https://en.wikipedia.org/wiki/2017_Saint_Petersburg_Metro_bombing
  * [70] Wikipedia  “2017 Stockholm truck attack”  URL: https://en.wikipedia.org/wiki/2017_Stockholm_truck_attack
  * [71] Wikipedia  “Manchester Arena bombing”  URL: https://en.wikipedia.org/wiki/Manchester_Arena_bombing
  * [72] Wikipedia  “2017 London Bridge attack”  URL: https://en.wikipedia.org/wiki/2017_London_Bridge_attack
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  * [74] Wikipedia  “2017 Turku attack”  URL: https://en.wikipedia.org/wiki/2017_Turku_attack
  * [75] Wikipedia  “2017 Marseille stabbing”  URL: https://en.wikipedia.org/wiki/2017_Marseille_stabbing
  * [76] Wikipedia  “Carcassonne and Trèbes attack”  URL: https://en.wikipedia.org/wiki/Carcassonne_and_Tr%5C%C3%5C%A8bes_attack
  * [77] Wikipedia  “2018 Liège attack”  URL: https://en.wikipedia.org/wiki/2018_Li%5C%C3%5C%A8ge_attack

##  Appendix A Details of our sample of terrorist attacks

Date |  Wiki article (abbreviation) |  Details |  Deaths |  Injuries
---|---|---|---|---
13-Nov-15 |  November 2015 Paris attacks[63]
(Paris 2015) |  A series of co-ordinated attacks. The first shooting attack occurred in a restaurant and a bar in the 10th arrondissement of Paris. Other bombings took place outside the Stade de France stadium in the suburb of Saint-Denis. |  130 (+7
attackers) |  413
22-Mar-16 |  2016 Brussels bombings[64]
(Brussels 2016) |  There were three coordinated suicide bombings in Brussels: two at Brussels Airport in Zaventem, and one at Maalbeek metro station. |  32 (+3
attackers) |  340
13-Jun-16 |  2016 Magnanville stabbing[65]
(Magnanville 2016) |  A man stabbed and killed a police officer in his home, before taking the officer’s wife and son hostage. ISIL claimed responsibility. |  2 (+1
attacker) |  0
14-Jul-16 |  2016 Nice attack[66]
(Nice 2016) |  A cargo truck was deliberately driven into crowds celebrating Bastille Day on the Promenade des Anglais in Nice. ISIL claimed the responsibility. |  86 (+1
attacker) |  458
19-Dec-16 |  2016 Berlin attack[67]
(Berlin 2016) |  A truck was driven into a Christmas market in Berlin. ISIL claimed responsibility. |  12 |  56
22-Mar-17 |  2017 Westminster attack[68]
(Westminster 2017) |  A Muslim convert drove a car into pedestrians on Westminster Bridge, He then crashed his car into the fence of the Palace of Westminster and fatally stabbed an unarmed policeman. |  5 (+1
attacker) |  50
03-Apr-17 |  2017 Saint Petersburg Metro bombing[69]
(St Petersburg 2017) |  A suicide bomber blew himself up on the St Petersburg Metro, Imam Shamil Battalion, an Al-Qaeda affiliate, claimed responsibility, but according to the FSB, attacker acted on the orders of a field commander from ISIL. |  5 (+1
attacker) |  64
07-Apr-17 |  2017 Stockholm attack[70]
(Stockholm 2017) |  A hijacked truck was driven into pedestrians along a shopping street before crashing into a department store. The attacker had shown sympathies for extremist organizations including ISIL. |  5 |  14
22-May-17 |  Manchester Arena bombing[71]
(Manchester 2017) |  A suicide bombing was carried out at Manchester Arena after a concert by American singer Ariana Grande. |  22 (+1
attacker) |  512
03-Jun-17 |  2017 London Bridge attack[72]
(London Bridge 2017) |  A van ran into pedestrians on London Bridge and then drove to Borough Market. |  8 (+3
attackers) |  48
16-Aug-17 |  2017 Barcelona attacks[73]
(Barcelona 2017) |  Two suspects were killed in an initial accidental explosion during the preparation of explosives. Latter on, a van was driven into pedestrians in Las Ramblas, Barcelona. The following day a related attack occurred in Cambrils. ISIL claimed responsibility. |  16 (+8
attackers) |  152
18-Aug-17 |  2017 Turku stabbing[74]
(Turku 2017) |  An ISIL inspired attacker said a motive for his attack was airstrikes by the Western Coalition during the 2017 Battle of Raqqa in Syria. |  2 |  8 (+1
attacker)
01-Oct-17 |  Marseille stabbing[75]
(Marseille 2017) |  Two women were stabbed by a migrant from Tunisia. ISIL claimed responsibility. |  2 (+1
attacker) |  0
23-Mar-18 |  Carcassonne and Trèbes attack[76]
(Carcassonne 2018) |  An attacker stole a car, killing a passenger in Carcassonne. Then, he attacked a supermarket. |  4 (+1
attacker) |  15
29-May-18 |  2018 Liège attack[77]
(Liege 2018) |  A man killed two police officers and a civilian. He is also believed to have killed a man the day before the attack. |  4 (+1
attacker) |  4

Overview and descriptions of 15 terrorist attacks comprising our sample and
the abbreviations used in the main text. Dates are as established for our
analysis, although some attacks spanned several days.

##  Appendix B Arrangement of surviving 69 articles into Specific and Broad
categories

Broad category | Specific category | Article from English Wikipedia
---|---|---
ENEMY | Terrorism in general | Massacre
Mass shooting
Lone wolf (terrorism)
Islamic terrorism | 2002 Los Angeles International Airport shooting
2004 SuperFerry 14 bombing
2007 Glasgow Airport attack
2010 Moscow Metro bombings
Abduction of Russian diplomats in Iraq
List of terrorist incidents linked to ISIL
Muslim Brotherhood
Rick Leventhal
Kimberly Dozier
Non-Islamic terrorism | Basque conflict
Basque National Liberation Movement
Timeline of Real Irish Republican Army actions
Terrorism in Western countries | List of terrorist incidents in Australia
List of terrorist incidents in Great Britain
List of terrorist incidents in London
Terrorism in Europe
Terrorism in Greece
Terrorism in Sweden
Terrorism in the Philippines
Conspiracies | False flag
SECURITY | National security | Conscription in the United States
Crisis actor
List of police firearms in the United Kingdom
Police use of firearms in the United Kingdom
Ring of steel (London)
Spanish Armed Forces
UK Threat Levels
Universal background check
Devin Nunes
Weapons | Comparison of the AK-47 and M16
Gun culture in the United States
Gun politics in the United States
Jews for the Preservation of Firearms Ownership
Assault weapon
USS Cheyenne (SSN-773)
United States v. Miller
Transport options | Big Sky Airlines
Boston-Maine Airways
Corporate Air
Dakota, Minnesota and Eastern Railroad
Eclipse Aviation
Insurance options | Thornburg Mortgage
TD Banknorth
Equipment for survival | Orvis
OTHER PERCEPTION | Islam in general | Moinuddin Chishti
Islam in Western countries | Islam in Poland
List of mosques in the United Kingdom
Ed Husain
Bhagwan Shree Rajneesh
Islam in non-Western countries | Islam in Qatar
About Middle East | Susa
SELF PERCEPTION | National identity | Juan Carlos I
Religious identity | Church of the Holy Sepulchre
Activism in general | Kyle Kulinski
Andrew O’Hagan
Anti-Islam activism | James Wickstrom
Quilliam (think tank)
Tommy Robinson (activist)
Sebastian Gorka
Paul Weston (politician)
Violent history of Western countries | Grdelica train bombing
Greek War of Independence
Legitimacy of the NATO bombing of Yugoslavia
Charles B. McVay III
The Man with the Iron Heart
List of events named massacres
Organization of the 69 articles that survived all filters (right column, same
as Table 1) into Specific categories (middle column) and broad categories
(left column).

##  Appendix C Dynamics of total visits to English Wikipedia in relation to
the attacks

Do terrorist attacks divert attention from other simultaneously ongoing
concerns? Here, we examine the time series of daily viewcounts to all articles
cumulatively, excluding only stubs and redirects (see section 4.5, immediately
after the exclusion Step 1). The obtained time series is shown in Fig. 10 (top
panel), in which we marked our sample of attacks. For better clarity, in the
bottom panel of Fig. 10 we zoom to a 3-month period in 2017 during which five
attacks occurred.
The total views vary from 200 to 300 million per day. There are pronounced and
stable weekly oscillations, with more activity during weekdays and less during
the weekend. There are less pronounced seasonal oscillations, with slightly
more activity during the early months of the year (winter in the northern
hemisphere). As the Internet becomes ever more accessible, we expected to find
more viewing activity in 2018 compared to 2015. Surprisingly, we found none.
None of the considered terrorist events seems to affect the overall viewing
dynamics, as clear from the bottom panel of Fig. 10. Weekly oscillations
remain statistically stable immediately after any of the attacks. In the eyes
of the global English-speaking public, terrorism is only one of very many
concerns ongoing in parallel. Attacks might have diverted some attention from
routine concerns, but the intensity of this diversion is too small to be
recognized against the background of normal fluctuations.

Figure 10: The time series of daily viewcounts to all articles in English
Wikipedia excluding only stubs and redirects. Top panel: the time series with
dates of the attacks shown. Bottom panel: the same time series for 3 months in
2017 during which five attacks occurred (dates shown on $x$-axis are all
Mondays).

##  Appendix D Bench-marking against reactions to other events

What else, besides terrorist attacks, diverts public attention to these
issues? We investigate this by systematically computing Excess Attention $Z$
for all articles from Table 1, starting with the Paris 2015 event. For each
article, we isolate the reactions with $Z>3$ that happened at least two weeks
away from any of the attacks. This led to a list of non-terrorism-related
reactions for each article.
We looked for patterns in this list but found none to be very clear. For a
typical article, we found about 10–30 reactions to events other than
terrorism. This number seems relatively constant across all broad categories.
Overall, articles with more general content (e.g. Terrorism in Europe) had
more, and articles with more focused contents (e.g. Terrorism in Greece) had
less non-terrorism-related reactions. While broad categories react (in
specific ways) to acts of terrorism, they also react to other events, albeit
in a less consistent manner. This comes across as natural, since terrorism is
not the only public concern. For instance, we found the viewcount of several
articles from Table 1 to spike on 25 January 2017. Yet many events could have
been responsible: the inauguration of US President Donald Trump, withdrawal of
the US from the Trans-Pacific Partnership, Oscar nominations, Syrian civil
war, etc.
Along the same lines, we checked what else draws attention to the 15 articles
devoted to the attacks themselves. To this end, we looked for large $Z$ away
from all attacks. We found a few non-terrorism-related reactions, but much
less than for articles from Table 1. It appears that the best ‘markers’ of
terrorist attacks are articles devoted to the attacks themselves. At any rate,
the message of the above analysis is that too many events occur in the world
simultaneously for any clear-cut result on non-terrorism-related reactions.

##  Appendix E Robustness checks

Critical for reaching our results was the choice of exclusion criteria.
Indeed, many of our choices may appear arbitrary, leading one to think that
different exclusion choices could have led to different results. For this
reason, we checked whenever possible the robustness of the results against
variations of the exclusion criteria. Upon very close examination we found no
significant impact of such variations on our results, i.e., the overall
picture (such as the structure of broad categories) still holds even with non-
trivial modifications of our exclusion choices.
In the same context, recall that in defining Excess Attention, we implicitly
assumed that daily viewcounts (in fact, their weekly averages) are normally
distributed around the mean, which may not always be the case. But in
practice, as clear from Fig. 9, $Z>3$ is a sufficient guarantee for a
redirection of attention, even if viewcounts are not (perfectly) normally
distributed. Upon excluding articles with $Z<3$, the only false positives we
found (and examined manually, see 4.5) were the issues that accidentally
coincided with the attacks. This confirms the validity of our $Z>3$ criterion
regardless of viewcount distribution.
On the other hand, our results could benefit from an independent verification
via sources other than Wikipedia. These would rely primarily on social media,
which nowadays are a standard channel of communication. This is an important
direction for future work since it can elucidate the public reaction from a
wider perspective. Still, each tweet is just one particular message (usually)
designed by one person. Even if this tweet is very popular, its popularity
might be due to a lack of a systematic répertoire of tweets from which the
audience could choose the best ones. Alternatively, one could verify our
results via surveys and interviews: Ask people directly what interests them
after a terrorist attack. However, there is a clear difference between what
people do and what they report. Surveys typically measure the reported
behavior only. In contrast, our insights are in the actual views of Wikipedia,
hence revealing the real behavior.