https://doi.org/10.1177/1477370821994059
European Journal of Criminology
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DOI: 10.1177/1477370821994059
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Misinformation about fake
news: A systematic critical
review of empirical studies
on the phenomenon and
its status as a ‘threat’
Fernando Miró-Llinares and Jesús C. Aguerri
Crímina Research Center for the Study and Prevention of Crime, Miguel Hernández University of Elche, Spain
Abstract
After the 2016 US presidential elections, the term ‘fake news’ became synonymous with
disinformation and a catch-all term for the problems that social networks were bringing to
communication. Four years later, there are dozens of empirical studies that have attempted
to describe and analyse an issue that, despite still being in the process of definition, has been
identified as one of the key COVID-19 cyberthreats by Interpol, is considered a threat to
democracy by many states and supranational institutions and, as a consequence, is subject
to regulation or even criminalization. These legislative and criminal policy interventions form
part of the first stage in the construction of a moral panic that may lead to the restriction
of freedom of expression and information. By analysing empirical research that attempts to
measure the extent of the issue and its impact, the present article aims to provide critical
reflection on the process of constructing fake news as a threat. Via a systematic review of
the literature, we observe, firstly, that the concept of fake news used in empirical research is
limited and should be refocused because it has not been constructed according to scientific
criteria and can fail to include relevant elements and actors, such as governments and
traditional media. Secondly, the article analyses what is known scientifically about the extent,
consumption and impact of fake news and argues that it is problematic to establish causal
relationships between the issue and the effects it has been said to produce. This conclusion
requires us to conduct further research and to reconsider the position of fake news as a
threat as well as the resulting regulation and criminalization.
Keywords
Criminalization, fake news, misinformation, social networks, threat
Corresponding author:
Jesús C. Aguerri, Crímina Research Center for the Study and Prevention of Crime, Miguel Hernández
University of Elche, Avda. de la Universidad s/n. Edif. Hélike, 03201, Elche, Spain.
Email: j.aguerri@crimina.es
994059EUC0010.1177/1477370821994059European Journal of CriminologyMiró-Llinares and Aguerri
research-article2021
Article
2	 European Journal of Criminology 00(0)
Introduction
It is not easy to find references to the concept of fake news in academic literature before
2016 (Tandoc et al., 2017). Those that exist mainly connect it with ‘parody news’
(Brewer et al., 2013; Marchi, 2012), humorous content that imitates the format of the
mainstream news (Berkowitz and Schwartz, 2016), or the use of real news for comedy
– satirical news (Baym, 2005). The term subsequently began to acquire new nuances
and to be linked to concepts such as political propaganda (Khaldarova and Pantti, 2016),
but it was not until the 2016 US presidential election campaign that the concept of fake
news gained the popularity and meaning it has today (McGonagle, 2017). First, a series
of newspaper articles drew attention to the widespread dissemination of false information
on the social network Facebook regarding issues connected to the political contest
(Silverman and Alexander, 2016; Silverman, 2016). The subsequent victory of the
Republican candidate, contrary to what most surveys had predicted,1
led some authors
to turn fake news into a modern version of disinformation linked to cyberspace and
social networks (Lazer et al., 2018; Tandoc et al., 2017), and to relate it to political
events such as the aforementioned Donald Trump victory (Bennett and Livingston,
2018) or Brexit (Bastos and Mercea, 2019). This prompted statements of concern from
states and institutions about the risks of fake news (European Commission, 2018;
Government of Spain, 2019; Parliament of Singapore, 2018), as well as the proliferation
of journalistic initiatives that aim to verify information – fact-checking. In turn, social
concern, as is often the case, has led to the emergence of various proposals for regulation
and even criminalization (Lee and Lee, 2019; Tandoc, 2019).
It seems that a certain degree of moral panic (Cohen, 1972; Young, 1971) is being
generated with regard to fake news and disinformation, or at a minimum the process of
identifying a new phenomenon as a threat to social values and interests is under way,
which constitutes the initial communicative step toward a response based on social control
and criminal regulation. Faced with this type of situation, it is always necessary to
conduct empirical studies that help substantiate the threat or call it into question (Garland,
2008). In fact, from 2016 there was indeed a significant increase in academic research on
the issue and its potential negative impact (Farkas and Schou, 2018). However, the proliferation
of studies in disciplines ranging from computer science to information science
that quantify and measure the various impacts of fake news has not always been combined
with reflection on and conceptualization of the phenomenon. Although it is clear
that the potential to manipulate society and to alter democratic processes by means of
false, distorted or decontextualized information needs to be studied scientifically, this
means that it is necessary to adequately review how the concept of fake news is being
empirically constructed.
And this is the main objective of the present article: to identify which conceptualization
of fake news is being constructed as an empirically ‘proved’object, to use Bourdieu’s
terminology (Bourdieu et al., 2002). This constitutes the first analytical step in order to
reflect upon the consequences of the object being analysed and whether it is the object
itself that should be of particular concern or whether we should broaden our outlook. To
this end, an initial systematic review of the existing empirical literature will be conducted,
followed by a critical reflection on the conceptual bases of the works analysed in
Miró-Llinares and Aguerri	 3
the review. Because, if we do not proceed in this manner, if we do not review what we
are measuring, we run the risk of using science to misinform about an issue that is generating
social concern and that is already being regulated and even criminalized.
Background
The ‘threat’ (and moral panic) of fake news
The dissemination of false information with political or economic objectives is not a new
phenomenon and neither can it be said that the dissemination of false information arose
with the Internet (Levi, 2019). State use of propaganda, as a way of broadcasting the
‘correct’ worldview, is inherent in their existence. At the end of the 19th century, the
magnate William Randolph Hearst, considered the creator of the yellow press (Spencer,
2007), used his media empire to spread distorted news that benefited his interests, even
precipitating the Spanish–American War of 1898 (Lowry, 2013). However, it must be
acknowledged that today’s communicative ecosystem is not identical to that of the 19th
century, or even that of the 20th century. The generalization of the Internet and the
increasing prominence of social networks have substantially modified the field of communication,
making access cheaper – the investment and knowledge needed to create a
website and monetize visits are minimal – and offering new channels for the dissemination
of information (Lazer et al., 2018).
In view of this new communicative context, many authors have suggested that, owing
to the production and dissemination of fake news, there is increased risk of the population
being manipulated and becoming incapable of distinguishing true information from
false (Lazer et al., 2018). Moreover, this is aided not only by the content but also by the
way in which it reaches the recipient. For example, the diffusion of fake news can be
amplified by the use of automated fake-account networks on social networks. These are
capable of increasing the diffusion of certain content, which can make it appear more
reliable or credible and, therefore, contribute to disinformation (Bastos and Mercea,
2019; Shao, Ciampaglia et al., 2018). Furthermore, the consequences of this are not limited
to the electoral sphere but can also affect other areas of vital importance, such as
public health (Wang et al., 2019) or the economy (Fedeli, 2020; Zhang and Ghorbani,
2020). In fact, the COVID-19 crisis seems to have increased the perception that fake
news and disinformation are a threat. The WHO warned (2020) that the COVID-19 pandemic
was accompanied by an infodemic of disinformation, which seemed to be subsequently
corroborated by both Europol (2020) and Interpol (2020). Via its Global
Cybercrime Survey, Interpol confirmed the circulation of false information related to
COVID-19 in many countries and, in addition, expressed concern that incorrect information
could spread panic in the community and social disorder that have already been
exacerbated by the pandemic. Their report also linked fake news to some forms of cyber
fraud and to illegal trade in fraudulent medical products.
The status of ‘threat’that has been assigned to disinformation and fake news in a short
period of time has led to many forms of institutional responses. These range from official
declarations by state (Government of Spain, 2019) or supranational (European
Commission, 2018) bodies about the need for action, to the emergence of regulatory
4	 European Journal of Criminology 00(0)
codes (European Commission, EU Action Plan Against Disinformation (2018)) or legislative
initiatives all over the world to avoid fake news and disinformation. Regulating
this phenomenon is obviously difficult (Pielemeier, 2020) because of the intrinsic risk of
limiting freedom of expression (Kaye, 2019), which could explain why some regulatory
initiatives, such as in the United Kingdom or Italy, have not succeeded. Nevertheless,
several laws have already been passed, such as in Germany, where the Network
Enforcement Act (NetzDG) of 1 January 2018 requires censorship of content in order to
avoid being punished with heavy fines (Schmitz and Berndt, 2018). In 2018, Malaysia
passed an Anti-Fake News Act, allowing for prison sentences for those who spread fake
news, but repealed it the following year (Kaye, 2020). Singapore’s Protection from
Online Falsehoods and Manipulation Act (POFMA) provides significant criminal sanctions
for anyone, including service providers, that communicates a ‘false statement’ in
Singapore when they know or have reason to believe that it is false and ‘is likely to be’
harmful in a variety of possible ways (Pielemeier, 2020).
Obviously, this cycle in the emergence of a social concern is not new – it is identified
as a potential threat and there is an immediate legislative reaction in the form of criminalization,
with the consequent jeopardization of fundamental rights. It is unique that the
right that may be affected by these regulations is freedom of expression (Kaye, 2019),
and the intimate relationship between the threat and what it entails and the media is certainly
unprecedented. The emergence of a concept in the media that quickly becomes a
source of social anxieties and an object of ‘disproportionate’demands for criminalization
(Hall et al., 1978) inevitably leads us to the concept of moral panics coined by Young
(1971) and popularized by Cohen (1972). This concept has been used extensively in both
criminology and public debate (Garland, 2008) and has been extremely successful in
academia for studying the response to multiple criminal phenomena. But what is striking
is that now it is the media themselves that are at the centre of the equation, not only as
the disseminators of that moral panic but also as the recipients of it. It is in their field of
activity (the diffusion of news) that the threat propagates, and some of them (those found
in the new informational contexts; Walsh, 2020) could constitute both the threat as well
as the guarantors of security against it. In these circumstances, it has been suggested that
social networks have become the object of social anxieties and the source of what has
come to be called ‘technopanics’ (Marwick, 2008). In fact, we can already find authors
who classify the media treatment of fake news as an informational moral panic. Thus,
Carlson (2020) maintains that, via their discourse on fake news, agents in the field of
communication have created a deviant other that encompasses all the concerns derived
from the irruption of social networks, while they present themselves as a truthful and
trustworthy opposition to that other.
Regardless of the current revision of the concept of moral panic (Garland, 2008;
Horsley, 2017; Jewkes, 2015; Thompson and Williams, 2013) and beyond the controversy
regarding the concept and the theoretical framework built around it, Cohen’s
approach has the virtue of drawing attention to the role of the media in the process of
creating threats that subsequently legitimize punitive responses. In our view, this
approach adequately expresses a process that is under way in relation to fake news, at
least in regard to the early stages of identifying something or someone as a threat, and the
distorted and simplified dissemination of this threat by the media (Simons, 2019).
Miró-Llinares and Aguerri	 5
Although concern about fake news and its alleged threat to democracy arises from the
media (Silverman and Alexander, 2016; Silverman, 2016), it seems reasonable to consider
the possibility that we are witnessing a process, or at least an incipient process,
whereby an artificial threat is constructed that can potentially be used by the authorities
to implement the disproportionate and restrictive regulation of rights. However, what
concerns us in this article is not so much analysing a ‘moral panic’ to determine whether
the aforementioned theoretical framework explains the institutional response to the phenomenon.
Rather, the aim is to show that criminalization is being proposed for an issue
that is still in its definitional phase and that is being ascribed ‘threat’ status when the
impact of fake news is only beginning to be measured and when, as we will see below, it
is still not clear what is being measured, what is being included, what is being omitted
and how, either by action or omission, the authors are producing the process of conceptual
attribution of what is later evaluated as a threat.
Agreements and disagreements about the concept of fake news
Given the political and social interest that the phenomenon of fake news has acquired in
recent times and, in particular, given that it is considered as a ‘threat’ to the essential interests
of democracies, it is understandable that there have been attempts to empirically
‘prove’ hypotheses of a descriptive nature, such as those relating to the existence of an
increase in fake news in certain electoral processes (Silverman, 2016), and hypotheses of
the inferential variety, such as those linking the consumption of fake news to certain consequences
(Allcott and Gentzkow, 2017). Yet, in reality, a phenomenon is beginning to be
measured that, beyond its relationship to the concept of disinformation and the synonymy
between them (Lazer et al., 2018; Wardle and Derakhshan, 2018), is still in the process of
being defined. As other reviews of the literature have already observed (Tandoc et al.,
2017; Zhang and Ghorbani, 2020), the term ‘fake news’ is closely linked to concepts such
as satirical news, hyper-partisan news and conspiracy theories. Whereas some research
restricts the concept of fake news to manufactured information that pretends to be news
(Grinberg et al., 2019), others give it a broader meaning (Bovet and Makse, 2019; Giglietto
et al., 2019) and consider fake news to be a category that includes both manufactured news
and different combinations of hyper-partisan news, satirical news and conspiracy theories.
But these differences in the theoretical contours of fake news, despite having repercussions
on an empirical level, are secondary to the articulation of the concept of fake news as a
scientific object. The central question, prior to the theoretical delimitation of the concept of
fake news, lies in the form of attributing the characteristic ‘fake’to that which is studied as
fake news. This, as we will see below, derives from a conceptual debate that cannot be
considered resolved, despite the fact that empirical research is under way.
Perhaps the most established definition of fake news in the academic literature is that
of Lazer et al., for whom fake news consists of ‘fabricated information that mimics news
media content in form but not in organizational process or intent’ (2018: 1094). It should
be noted that this conceptualization of fake news avoids linking the concept directly to
the idea of truth or lies, and it emphasizes, on the one hand, that this information formally
imitates media content and, on the other hand, that it has not been produced following the
same processes or with the same intention as news produced by the media. From this
6	 European Journal of Criminology 00(0)
definition it is derived that fake news is information that acquires the characteristic of
fake because the producer neither uses the same processes nor has the same intentions as
the media. In other words, based on this approach the ‘attribution of fakeness is thus not
at the level of the story but at that of the publisher’ (Grinberg et al., 2019: 1).
Leaving process issues aside but maintaining the importance of appearance and of the
objectives behind its production, the definition provided byAllcott and Gentzkow (2017)
illustrates the other common way to define fake news. For these authors, fake news
would be ‘news articles that are intentionally and verifiably false, and could mislead
readers’(2017: 213). These authors do introduce the concept of falsehood into the definition
and place the attribution of falsehood at the level of the story itself. However, their
definition clarifies that not all false information will be fake news, only that which is
intentionally false, which can be verified as false and which, moreover, owing to its
characteristics – of whatever kind – can deceive readers.
Although the above-mentioned definitions disagree on the origin of fakeness, to consider
information fake news requires a certain degree of intentionality on the part of the
author and that the information has the potential to not to be recognized as false. Hence,
in the systematic revision by Tandoc et al. (2017), on the one hand, the ability of fake
news to mimic real news and, on the other hand, the fact that there is no intention to
inform, are what have led some authors to blame fake news for generating misinformation
(Grinberg et al., 2019; Lazer et al., 2018) and undermining the traditional media
system (Tandoc et al., 2017). Therefore, these definitions leave out cases of systematic
and systemic, though unintentional, malpractice, which means omitting from the ‘problem
of fake news’ a part of the phenomenon of disinformation that is intimately related
to traditional media (Levi, 2019).
It can therefore be seen that there is significant agreement on some of the aspects that
characterize fake news, as well as significant disagreement and a fundamental unresolved
discussion centred on the epistemological question of how the falsehood or ‘fake’
nature of the information should be determined: whether it should be determined on the
basis of the content of the news, the characteristics of the issuer, or otherwise. Many
other discussions can probably be derived from this that are closely related to the difficult
question of how to attribute the condition of truth to something. In any case, the
questions that interest us are: Are these consensuses, and also these discrepancies,
reflected in the empirical research that is already claiming to study fake news? Do all
studies that measure fake news or similar phenomena start from the same concept and
use the same method to determine what falls within the object of study? And, above all,
does this different conceptual configuration have an impact on the perception of the
threat that disinformation and fake news can pose?
In the present article we investigate the object that is being studied, and what the
empirical research that claims to describe the phenomenon and the impact of fake news
is actually measuring. After all, this research can form part of the public debate on the
risks that fake news can entail and, therefore, it can influence its regulation, or even
criminalization. And, if we do not first review the object that empirical research is studying,
in an area in which the definitive question clearly seems neither simple nor irrelevant,
we run the risk of collaborating in the false configuration of a threat and in the
justification of inadequate regulations.
Miró-Llinares and Aguerri	 7
Method
With the aim of addressing the objects that guide empirical research on disinformation,
a systematic review of the literature was conducted. To this end, the most relevant articles
in which the term ‘fake news’ was mentioned have been extracted from the Web of
Science (WoS) database. The number of citations was used as the reference criterion to
determine the relevance of the articles; thus, we extracted all articles published before
2019 with 10 or more citations, articles published in 2019 with 5 or more citations and
articles published in 2020 with 3 or more citations. This initial search was supplemented
by a second Google Scholar search, which yielded nine articles with more than 100 citations
that were not found on Web of Science.
After combining the results from both databases, a list of 99 articles was obtained.
These were manually reviewed to distinguish those of a theoretical nature (42 articles)
from those that provided their own empirical results, as well as to certify that the articles
on the list dealt with the subject matter of interest and did not simply mention the term
‘fake news’(15 articles were removed from the final review for this reason). This process
produced a list of 42 empirical articles that constitute the object of the present bibliographical
review (see Figure 1).
Based on the analytical perspective previously outlined and as a result of the bibliographic
review, it has been established that research on fake news can be classified into
two large groups according to where they seek to identify the origin of its fake nature
(Figure 2). Thus, on the one hand, there are studies that attribute the fakeness to the
issuer of the message, so that it will be some quality attributable to the issuer that will
lead to the information being assumed to be true or false. On the other hand, there are
studies in which the characteristics of the information itself will determine, in the eyes of
the researchers, whether it is considered fake. In turn, and independently of the method
for attributing fakeness, the different studies can also be divided between those that carry
out internal analysis and those that conduct external analysis. The research that conducts
external analysis takes fake news as a variable and observes how it relates to other variables,
whereas the research that carries out internal analysis will observe the characteristics
of those contents that have been called fake news. When conducting internal analysis,
researchers who attribute the condition of fake news to the issuer, by selecting what they
are going to observe based on its source and not on its intrinsic characteristics, are in fact
studying the characteristics of the content published by certain issuers; in contrast, when
carrying out an external analysis, researchers will be studying how the receivers relate to
certain issuers, or, if they take a psychological perspective centred on the subject, they
will be studying the psychology of the receiver – personality traits or cognitive skills that
could influence how the subject relates to fake news – when faced with these new issuers
that seem to have appeared in the field of communication. On the other hand, the studies
that attribute some form of falsehood to the messages will be studying the characteristics
of these – specifically those to which they attribute the status of fake – when they carry
out internal analysis. In the case of external analysis, we can distinguish between studies
on the psychology of the receiver when faced with certain content (if they study the relationship
between the human psyche and fake news) and studies on the relationship of the
subject with certain content (if we observe how the subject relates to fake news).
8	 European Journal of Criminology 00(0)
Figure 1.  Selection process.
Figure 2.  Classification diagram.
Miró-Llinares and Aguerri	 9
Results
As can be seen in Table 1, regardless of the way in which the characteristic of fake is
attributed to news, very few studies carry out internal analyses, and 90 percent of the
reviewed research focused on external analyses. Within this category, all investigations
that attribute fakeness based on the issuer study how the receivers relate to certain issuers.
This perspective has been used by studies that have measured the dissemination and consumption
of fake news in certain contexts. It should be noted that this group of research
accounts for only 26 percent of all empirical studies analysed in the present article.
On the other hand, the research that, via external analysis, attributes falsehood as a
result of the characteristics of the message is divided between researchers who study the
psychology of the receiver when faced with certain content and those who study how the
users relate to that content. This last group, which is mainly composed of proposals for
algorithms capable of detecting fake news on social networks, is the most numerous of
all, accounting for 40 percent of all the research reviewed.
About the issuer
Many of the studies analysed have chosen to determine the fakeness of the news not by
its content, but by its source, so that the ‘fake’ status of the information is determined by
its issuer. Thus, a fake news item is conceptualized, in a broad sense, as a piece of information
that comes from a website that pretends to be a real media outlet (Lazer et al.,
2018). This way of understanding fake news has been prominent in the empirical studies
that have tried to measure the diffusion or consumption of fake news among the population,
as it allows the news consumed or shared by the subject to be configured as independent
variables, without the need to individually evaluate the content of each piece.
Furthermore, this approach has also allowed research on the patterns of dissemination of
Table 1.  Number of items per category.
Fakeness
attribution (FA)
Scope Object N Percent in FA
category
Percent
of total
Issuer Internal The content published by certain
issuers
2 15.4 4.8
External How receivers relate to certain
issuers
11 84.6 26.2
The psychology of the receiver
when faced with new issuer
0 0.0 0.0
Message Internal The characteristics of certain
messages
2 6.9 4.8
  External How the user relates to certain
content
17 58.6 40.5
  The psychology of the receiver
when faced with certain content
10 34.5 23.8
Total 42 200.0 100.0
10	 European Journal of Criminology 00(0)
certain content through social networks, which has led to botnets being detected as significant
collaborators in this task (Bastos and Mercea, 2019; Shao, Ciampaglia et al.,
2018; Shao, Hui et al., 2018). However, this way of approaching fake news via the issuer
is not exclusive to external analyses. We also find internal analyses that study the characteristics
of the content created and disseminated by these issuers (Bakir and McStay,
2018; Mourão and Robertson, 2019).
Using the issuer to determine the ‘fake’ nature of information makes it necessary to
determine which sources are actually considered as media, or legitimate media, and
which are not. In order to solve this problem, the observed studies resort to categorizations
of media elaborated by academics, media or fact-checking organizations (such as
Opensources.co,2
Buzzfeed,3
FactCheck.org4
and PolitiFact.com5
).
About the message
The other conceptual model for the study of fake news places the condition of ‘fake’ in
the study of the content, and not of the issuer. From this perspective, research has also
been carried out to measure the importance and extent of the phenomenon of fake news
in certain political contexts, such as the 2016 US presidential elections (Allcott and
Gentzkow, 2017) or the 2014 Ukraine–Russia conflict (Khaldarova and Pantti, 2016).
This way of attributing falsehood to fake news has also been used by the different studies
on the psychology of the receiver when faced with content considered to be fake
(Bronstein et al., 2019; De Keersmaecker and Roets, 2017; Pennycooka and Randa,
2019). In both cases, for the construction of the variable ‘fake news’ we no longer find
media lists but we do find fake news lists.
It should be noted that, within the studies that attribute the fakeness of fake news to
its content, the most numerous group is made up of proposals for models trained by
machine and deep learning for the detection of this type of content. Those models that
carry out internal analysis use features based on questions related to the use of language
– grammar, syntax, etc. (Jang et al., 2018; Pérez-Rosas et al., 2018; Potthast et al., 2018).
On the other hand, research that carries out external analysis uses as features the patterns
of dissemination of false information in social networks, as well as characteristics associated
with the user posts through which they are disseminated – characteristics of the
account or of the message or the presence and characteristics of associated images
(Alrubaian et al., 2018; Jang et al., 2018; Jin et al., 2016; Jin et al., 2017; Reis et al.,
2019; Ruchansky et al., 2017; Shin et al., 2018; Wang et al., 2018).
As with the previous model, to access this false information, researchers use fake
news indexes that are also developed by certain official news agencies or media outlets
that are dedicated to fact-checking. Thus, with the exception of the proposals from PérezRosas
et al. (2018), who build their database by inventing fake news, and Alrubaian et al.
(2018), in which the participating researchers verify the information, the different algorithms
proposed for the detection of fake news have been trained on databases that have
been constructed using information labelled as fake news by the aforementioned factchecking
organizations (news agencies such as Xinhua News Agency, or fact-checkers
such as Snopes.com). However, in this kind of approach the object is no longer the fake
news itself, understood as the ‘original’ false information. The focus of the research, and
Miró-Llinares and Aguerri	 11
consequently of the classification model, becomes the characteristics of the vehicles,
accounts and messages through which fake news is disseminated and the patterns of dissemination.
In other words, the research focuses on how users relate to certain content
but also endows them with an active role in the production of misinformation.
The impact of fake news on democracy
Finally, it should be noted that few studies were found that address the impact of fake
news. Of the articles reviewed, only eight based their research on a specific context in
such a way that it was possible to use relevant empirical material to attempt to gauge the
impact of fake news within that context. With the exception of one of them – related to
Brexit (Bastos and Mercea, 2019) – the remaining seven studies examine fake news in
the context of the 2016 US election (Allcott and Gentzkow, 2017; Bovet and Makse,
2019; Grinberg et al., 2019; Guess et al., 2019; Guess et al., 2020; Nelson and Taneja,
2018; Shao, Hui et al., 2018). These studies have taken different perspectives (see
Table 2) but their results are consistent, as all found substantially small and highly concentrated
diffusion and consumption of fake news among a specific profile of subjects,
which significantly weakened the initial hypotheses about the relationship between fake
news and Donald Trump’s victory (Mihailidis and Viotty, 2017; Silverman, 2015).
Studies such as that by Guess, Nyhan and Reifler (2020) have estimated that fake news
accounted for 5.9 percent of the news consumed by each user in the month prior to the
elections. With regard only to Twitter, Grinberg and co-authors (2019) observed that,
during the month prior to the elections, each user was exposed to fake news related to the
political campaign 10 times on average, only 1.18 percent of the user’s total exposure to
political news. This same research also found that 1.0 percent of their sample consumed
80.0 percent of the detected fake news. These big consumers of fake news were mainly
conservative and were characterized by high consumption of all kinds of news. This
conclusion regarding the profile of consumers of fake news is shared with other studies
reviewed herein (Allcott and Gentzkow, 2017; Guess et al., 2019; Guess et al., 2020;
Nelson and Taneja, 2018). These findings are summarized in Table 2, and, as will be
considered in greater detail in the Discussion section, none of the existing studies allows
a causal relationship to be established between the results of the elections and fake news.
Table 2.  Articles that analyse the impact of fake news on the 2016 US presidential elections.
Fakeness
attribution (FA)
Main data source N Percent
of total
Issuer Poll 0 0.0
Web traffic (navigation history) 2 28.6
Social network data 4 57.1
Message Poll 1 14.3
  Web traffic (navigation history) 0 0.0
  Social network data 0 0.0
Total 7 100.0
12	 European Journal of Criminology 00(0)
Discussion and critical reflection
The possible impact of disinformation on the 2016 US presidential elections brought into
public debate the potential need to regulate fake news on social networks, either through
criminalization or by forcing social media to prevent its dissemination. In turn, this gave
rise to an important debate on the role that Internet service providers play in regulating
political debate and shaping freedom of expression (Kaye, 2019). The premise on which
all the regulatory proposals are founded is that misinformation, as a genus, and fake news,
as a species, constitute a serious threat to essential values such as democracy, public health
or free public opinion. Empirical studies have begun to emerge that attempt to confirm
some of these premises or to detect ‘fake news’, despite the enormous difficulties posed
by the measurement of phenomena that are conceptually not very precise, or to determine
causal inferences in political processes or similar areas of social decision-making. The
results of our study show, however, a tendency to simplify the characterization of the
conditions that constitute ‘fake news’ for the purposes of its empirical measurement.
We have seen that a large number of studies on fake news introduce a concept that is
related to the incursion of new actors into the field of communication who do not follow
the traditional verification processes and to whom the characteristic ‘fake’ is implicitly
attributed. Without denying the possible relationship between source and truth, this omits
certain news from the phenomenon of fake news and disinformation that, because of its
content, may be clearly untrue but does not come from sources categorized as fake or that
is even disseminated by legitimate media. Furthermore, this ignores the possibility,
hypothesized by certain authors (Mihailidis and Viotty, 2017; Silverman, 2015) and
rejected by others (Guo and Vargo, 2020), that the content disseminated by these new
actors can influence the content of legitimate media sources by filtering into them or
conditioning their agenda. Moreover, the vast majority of the empirical research on fake
news depends on pre-constituted lists of information or media that are prepared by factcheckers
or government news agencies. In both cases, the criteria for the attribution of
the status of ‘fake’and for the elaboration of the lists are not transparent, and nor are they
scientific since none of these organizations principally carries out scientific activities.
It is true that the models proposed for the detection of fake news in social networks,
which, as we have seen, constitute 36 percent of all the empirical research reviewed,
deserve separate attention. This is because, although these studies also depend on the
sources from which they extract what they consider to be fake news, the very nature of
machine and deep learning is the generation of tools that transcend the database and are
capable of learning beyond it. Furthermore, this way of approaching the phenomenon is not
limited to observing user interaction with certain agents that disseminate fake news; it also
observes how users disseminate this information, how they modify and re-distribute it, and
how they react to it and to other users. This is, therefore, a broader and more ambitious way
of approaching disinformation, since it is not limited to the origin of the information but
takes into account the waves – the different reactions – generated by the appearance of false
information in a social network. Right now, this kind of research says little about the phenomenon
of disinformation because it has been limited to developing tools in ‘laboratories’,
that is, within databases created specifically for this purpose, but the application of
this type of tool ‘to the real world’ could have great potential to generate knowledge.
Miró-Llinares and Aguerri	 13
It is true that dependence on the sources from which the potential fakes are extracted is
understandable, given the philosophical complexity intrinsic to any phenomenon based
on the ideas of ‘truth’ or ‘lie’. However, it is undeniable that such a procedure introduces
significant bias into research that aims to study fake news and forces us to be careful when
establishing consequences from these studies. The question of disinformation, misinformation
and fake news is a complex issue that cannot be reduced to the circulation of
information on social networks by websites that pretend to be media and are obviously
false or partisan. On several occasions, the theoretical and empirical literature on this
subject has drawn attention to the need to observe the role of media considered legitimate
or traditional in disinformation (Brulle et al., 2012; Lewandowsky et al., 2017). From this
perspective, issues such as clickbait (Rochlin, 2017), the primacy of opinion over expert
analysis (Case and Given, 2016), or the speed of publication imposed by their own corporate
structure (Cooke, 2017) cannot be ignored if we are to understand the phenomenon in
its entirety and its potential effects. If one wishes to study the dissemination of fake news
and the impact that it can have as a ‘potential threat to democratic systems’, one must at
least explore the possibility that governments and large media institutions have some role
in the propagation of this type of content. Likewise, if the objective is more ambitious and
one wishes to approach disinformation and misinformation as a social phenomenon, the
logics and practices of the main actors in the field of communication – large media institutions,
news agencies, press offices, etc. – cannot be completely ignored. Ignoring the
questions we have just mentioned and ‘borrowing’ the object of study means that scientific
research into fake news runs the risk of being caught up in the game of exchanging
meanings and legitimacy that, as in any other field (Bourdieu and Wacquant, 2005), is
disputed in the field of communication.
This is not to deny the usefulness, even the necessity, of conducting empirical research
on this, let us say, restricted concept of fake news. But, in addition to the fact that we
must demand an explanation of the concept that is being used as a starting point and of
the manner in which the condition of fake is attributed, we believe that it is essential to
make it compatible with other empirical research. The present article establishes the need
to adopt a clear, as well as a broad, concept of the phenomenon of disinformation that, in
turn, can be empirically articulated in different typologies. These should, at a minimum,
pay attention to the way in which something is considered fake, whether it is through the
medium or through the message, and make the process of attribution explicit so that it is
possible to debate and refute the procedure. What we have seen in the literature analysed
in the present article is that, despite the fact that what guides and justifies the research is
disinformation and its potential harmful effects, what is ultimately studied, either because
of lack of reflection on the concept of fakeness or because the attribution of fakeness is
not dealt with scientifically, are questions related to certain contents or certain issuers
whose relationship with fake news is not sufficiently demonstrated – beyond the fact that
someone has said at some point that this content was fake news or that this website disseminated
fake news. We do not believe it is appropriate to study a social phenomenon
merely as a given natural object that has not previously been configured by any type of
conceptualization, especially if the intention is to analyse the consequences of this object
and to derive from this research responses that are related to control, normative regulation
or criminalization.
14	 European Journal of Criminology 00(0)
In any case, and broaching the second question that we aimed to discuss in this
article, we must emphasize that the empirical data available right now, despite their
limitations, do not allow us to establish a causal relationship between this concept of
fake news and the harmful consequences that would theoretically make it a threat and
that would justify criminalization. Thus, given what we know right now about the
phenomenon, the proposals for criminalization, as well as classifying it as a threat,
may be remarkably disproportionate. As the United Nations Special Rapporteur on
Freedom of Expression has recently emphasized, when a state is preparing to restrict
freedom of expression in some way, which includes freedom of information, it is not
enough that the objective pursued be legitimate. ‘It must establish a direct and immediate
connection between the expression and the threat said to exist’, and, furthermore,
this restriction ‘must be the least intrusive instrument among those which might
achieve the desired result’(Kaye, 2020: 6). It should also be borne in mind that restricting
freedom of expression can have the well-known chilling effect (Miró-Llinares and
Gómez-Bellvís, 2020), which is particularly harmful because freedom of expression is
a fundamental right that is indispensable for other fundamental rights, such as the right
to information or to free public opinion, without which there can be no democratic
society (Alcácer-Guirao, 2012).
Furthermore, research on the concept of fake news is being conducted empirically
using lists of stories and media that have been identified as fake news by actors in the
field of communication, ignoring any reflection on the fakeness of the news. Thus, it
seems necessary, firstly, to state clearly that we are still in an initial phase of empirical
research that has not been able to confirm the threatening nature of fake news, and, secondly,
to call for new areas of empirical research on the phenomenon to avoid turning
research into a vehicle for the legitimization of the criminalizing discourses that circulate
in the field of communication and the political sphere.
Funding
We confirm that we received no financial support for the research, authorship, and/or publication
of this article.
ORCID iD
Jesús C. Aguerri https://orcid.org/0000-0002-7730-8527
Notes
1.	 It should be noted that some predictions did point to Donald Trump as a possible winner, such
as those made by survey aggregator FiveThirtyEight: https://fivethirtyeight.com/features/
the-real-story-of-2016/.
2.	 Opensources.co is a project by Merrimack College that offers a list of news sites categorized
as: fake, satire, hate and clickbait. This classification is based on the content of the publications,
their ‘About us’ section, the fonts used and the writing style. The project is not yet
available, but some information about the project can be found at URL (accessed 28 January
2021): https://github.com/BigMcLargeHuge/opensources.
3.	 Buzzfeed is a media outlet that, during the 2016 US presidential elections, carried out a series
of reports on the dissemination of fake news on Facebook. During the year following the
Miró-Llinares and Aguerri	 15
elections, the journalist responsible for the investigations, Craig Silverman, continued to
update the list of websites in various reports until it reached 233 websites.
4.	 PolitiFact.com is a portal composed of journalists and is primarily dedicated to the verification
of news in NorthAmerica. In December 2016, this organization began to collaborate with
Facebook on the verification of news that circulated through the social network. From this
experience, they developed a list of 324 entries of the websites from which fake news on the
social network came most frequently.
5.	 FactCheck.org is a project that acts as a ‘consumer advocate’ for US voters. Also, as a result
of its collaboration with Facebook, in July 2017 it published a Misinformation Directory of
websites dedicated to the dissemination of fake news – URL (accessed 28 January 2021):
https://www.factcheck.org/2017/07/websites-post-fake-satirical-stories/.
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