# An ontological analysis of misinformation in online social networks

Izzat Alsmadi
Department of Computing and Cyber Security
Texas A&M, University
San Antonio, TX 78259
<EMAIL_ADDRESS>
Iyad Alazzam
Department of Computer Information System
Yarmouk University
Irbid, Jordan
<EMAIL_ADDRESS>
Mohammad A. Al-Ramahi
Department of Computing and Cyber Security
Texas A&M, University
San Antonio, TX 78259
<EMAIL_ADDRESS>
Use footnote for providing further information about author (webpage,
alternative address)— _not_ for acknowledging funding agencies.

###### Abstract

The internet, Online Social Networks (OSNs) and smart phones enable users to
create tremendous amount of information. Users who search for general or
specific knowledge may not have these days problems of information scarce but
misinformation. Misinformation nowadays can refer to a continuous spectrum
between what can be seen as "facts" or "truth", if humans agree on the
existence of such, to false information that everyone agree that it is false.
In this paper, we will look at this spectrum of information/misinformation and
compare between some of the major relevant concepts. While few fact-checking
websites exist to evaluate news articles or some of the popular claims people
exchange, nonetheless this can be seen as a little effort in the mission to
tag online information with their "proper" category or label.

_Keywords_ Misinformation; Online Social Networks; Machine Learning, Cyber
Analytics

##  1 Introduction

The continuous fear of the spread of misinformation risks sacrificing the
values of freedom of speech as a core element in democracy. Many efforts to
propose solutions to the spread of misinformation are based on enforcing some
forms of censorship on who can post and what can be posted. This is already
implemented in many authoritarian countries around the world who try to
control public media under information censorship/accuracy claims. As one
possible compromise, OSNs should encourage their users to avoid re-posting
misinformation and should help them identify such misinformation. In a
previous work, a model is proposed for OSNs to use and promote engagement
metrics that motivate and promote positive publicity and engagement, Alsmadi
et al. (2016), Cho et al. (2016).
Facts, and truth are examples of terms that we used to refer to something that
is certain or indisputable. Fact can be more often used with science, while
"truth" can have belief or religion aspects. As such, what can be seen as the
"truth" for someone from a certain religion, can be seen as "false truth" for
someone from another religion. Religions and political orientations or
beliefs, can easily make two persons disagree completely on a specific
subject, news, claim, etc. This also implies an important aspect related to
misinformation, is that in many cases, people unintentionally spread
misinformation, thinking and believing that it is not. The statement "people
hear what they want to hear" shows that in any type of information or
misinformation you will find some audience who will listen to or believe in
such information/misinformation. "Belief in fake news is associated with
psychology, dogmatism, religious fundamentalism, and reduced analytic
thinking", Bronstein et al. (2019).
But why these days misinformation is a big subject and concern ? What are the
changes that happened in the last few years that may have triggered such issue
?

Their is no doubt that one of the main factors is the growth of Online Social
Networks (OSNs) such as Twitter, Facebook, YouTube, Instagram, LinkedIn,
Google+, Reddit, Snapchat, Tiktok, etc. In those websites literally all humans
around the world became content generators or producers. To a large extent,
their is no control on who can post and what. This is a significant change in
comparison with what we had 50 or 100 years ago where governments and specific
news agencies or groups control the media of TVs, newspapers, websites, etc.
To a large extent, OSNs are driven by marketing and encourage users to
interact more and generate more content, regardless of the credibility of such
content. OSNs rely on the merchandising of a click-and-share engagement that,
as a result, encourages the circulation of contents that are sticky, and
“spreadable”, Gerbaudo (2012), Venturini (2019). For OSN celebrities, even
negative publicity can have some positive impacts, Berger et al. (2010).

Researches showed that people are vulnerable to the spread and exposure of
misinformation because of psychological and sociological factors. Different
factors such as age, political or religious orientations can impact their
vulnerability, Frenda et al. (2011), Karduni et al. (2019).
In this paper we evaluated different misinformation perspectives and
classifications. For example, misinformation can be broadly classified based
on creator intention to intentional versus unintentional, Forbes (2002), Wu et
al. (2016), Brown (2018), Rapti (2019). Misinformation can also have transient
and temporary effect and lasting while some other misinformation can have
longer lasting and impacts. One major example of misinformation with a large
impact is that which surrounded US elections 2020. Another way to evaluate
misinformation literature is to investigate actions towards misinformation,
(e.g. detection, correction, prevention, etc.), Chen et al. (2015a). Machine
learning rule is usually related to automation and replacing human efforts.
Machine learning algorithms are proposed in many papers for the automatic
detection of misinformation (e.g. Alsmadi and OBrien (2019), Vicario et al.
(2019), Al-Ramahi and Alsmadi (2020), Alsmadi and OBrien (2020)).

##  2 Misinformation versus accurate information/facts

The prevalence of misinformation in online social networks in several domains
like science, political and health emerges new challenges. For example,
propagation of rumors or misinformation versus authentic information during
COVID-19 Pandemic was statistically significant Tiwari et al. (2020).
Therefore, a key challenge is to come up with good mechanisms/techniques to
correct misinformation on social media. Such correction mechanisms are crucial
before this misinformation firmly established as accurate information and
facts in receiver’s mind. Examples of those mechanisms include:

  * •

One of these mechanisms is crowd-sourcing technique or volunteer fact-checkers
that can act as social media observers or agents Vraga and Bode (2017). In
this context, Vraga et al. (2019) explored whether observational correction on
social media by emphasizing and contradicting the logical myths in
misinformation using can lead people to change their minds against
controversial issues in different domains like health, science, and politics.

  * •

Expert fact-checker to validate information posted Collins et al. (2020).
Results also showed adding a “Questioned” or “misleading” tag to misleading
information on social media like false headlines makes this information to be
perceived as less accurate Clayton et al. (2020). Machine learning and text
mining to automatically detect and filter fake and insincere contents in
social media Al-Ramahi and Alsmadi (2020).

  * •

Hybrid expert-machine that blends crowd and machines that showed satisfactory
results in detecting fake news Collins et al. (2020).

Due to its sensitivity, in healthcare domain, many researchers have been
attracted to examine mechanisms to identify health misinformation on social
media. In this regard, 1) Kim et al. (2020) proposed an approach based on eye
tracking to accurately determine the size of attention people give to a
misinformation as well as an adjustment message, and how that can be affected
by the adjustment approach adopted. 2) Kim and Walker (2020) found that
tracing replies that deliver correct information is more effective against
using keywords to search for COVID-19 misinformation regarding a cure and
antibiotics.

To tackle the problem of proliferation of health misinformation in online
social networks, Trethewey (2020) suggests the following strategies:

  * •

Careful dissemination of medical information: it is very important that
medical research findings to be introduced in an precise, impartial and
appropriate manner so audience like news media reporters and public people can
understand and act with them properly.

  * •

Expert-fact checking: Experts could verify tweets posted and approve them with
supporting reply supplemented with evidence.

  * •

Social media campaigns: there is a need to cooperate with influencers in
social media such as ‘mommy bloggers’ to conduct campaigns to promote specific
health information that can help spreading correct and data driven medical
information to the target audiences.

  * •

Greater public engagement: public or expert health organizations can promote
and manage campaigns of public health led by experts in different fields to
engage, advice and educate public as well as highlight misinformation in
different topics in their areas of expertise.

  * •

Fostering a fact-checking culture: it is critical to develop and encourage a
philosophy of fact-checking among public and motivate them to doubt the health
information communicated in social networks and see if that information is
supported by a scientific evidence.

  * •

Doctors as advocates: doctors and healthcare providers should be motivated to
be proactive and share information supported by scientific evidence to the
public through social networks outlets, like Facebook and Twitter Wahbeh et
al. (2020).

###  2.1 Open issues

Attempts to addressing misinformation in social media need to carefully
contemplate the misinformation context as well as the audience targeted in
establishing efficient interventions that could be adopted by the public Vraga
et al.[2019]. For example, identifying emerging health misinformation using
volunteer fact checker necessitates proper context-specific keywords to
acquire enough number of related potential posts and adequate advice from
official healthcare persons to reduce the variation of responses Kim and
Walker [2020].

##  3 Misinformation versus disinformation

Two distinct tracks are present within popular dictionaries Buckland (1991)
and journalistic literature Budd (2011) on disinformation and misinformation,
for example the provenances to which designed to detect adhere. Disinformation
and Misinformation may either be viewed as alternatives or differentiated in
terms of meanings and deception. Misinformation can be described as accidental
untruthful, imprecise or deceptive info and disinformation can be defined as
untruthful, imprecise or deceptive info proposed to misinform. Within
journalism, the general tendency appears designate to deal the two terms as
alternatives and mostly stick to the definition of misinformation to express
all kinds of fake, ambiguous, incorrect, and misleading details Thorson
(2016), Wardle et al. (2018). Instead of all incorrect or inaccurate material
(i.e. planned, unintentional, deceptive, deceiving, and so forth), the usage
of "misinformation" underpins an appreciation of the distinction between
reality and falsity between information and misinformation. Information is the
real component to be maintained, covered, strengthened and disseminated.
Misinformation is the incorrect component of the information to be prevented,
combated, hidden and stopped. There is no difference between deliberate and
deliberately deceptive and accidental mis-representative, for example honest
errors, imprecision as a result of unawareness when disinformation and
misinformation are regarded as synonyms. Therefore, all kinds of fraud are
viewed fairly and the aim is to defend against all of them. It is more popular
to consider misinformation and disinformation as two different terms rather
than dealing with them as synonyms in the conceptual and analytical accounts
of disinformation and misinformation Fallis (2015), Floridi (2013). In terms
of motives and potential deception, the difference among disinformation and
misinformation is cast: In general, misinformation is described as incorrect
content, and then disinformation is described as that segment of
misinformation that is in false, imprecise, or ambiguous, Notice that if
disinformation is described as the deliberately deceptive component of
misinformation, then in terms of motives and intention, there are no criteria
for misinformation. For example, it is not possible to specify that
misinformation is unintentional, Misleading as disinformation is part of
Misinformation as deliberate misleading. Intentional deception should not be a
subcategory of unintentional deception. Additionally, since misinformation is
frequently mentioned in the sense of honest errors, prejudice, mysterious
imprecision, and a distinction is maintained between misinformation and
disinformation, it is fair to describe the two definitions as entirely
different conceptions, wherever disinformation is not segment of
misinformation Burrell (2016).
Misinformation is characterized as accidental misleading, inaccuracy, or
falsehood, whereas deliberate misleading, inaccuracy, or falsehood is defined
as disinformation. Intentions are also the distinctive characteristics among
misinformation and disinformation: the unintentional vs the deliberate (non-
accidental) misleading, imprecision, and/or falsehood Capurro and Hjørland
(2003). Misinformation is a false assertion that leads individuals astray by
concealing the right truth. Deception, misunderstanding, falsehoods are often
referred to Zhang et al. [2016]. This causes feelings of distrust that
ultimately disrupt relations, which breach perceptions negatively, Wu et al.
(2019), Marshall and Drieschova (2018).
Disinformation is an incorrect part of information which is purposely
circulated to confuse viewers Galitsky (2015). While disinformation and
misinformation mutually apply to faulty or forged facts, the aim is to make a
major difference between them with no intention; misinformation is applied to
mislead whereas disinformation is applied with the intention Kumar et al.
(2016). Disinformation, which is inaccurate or misleading information, is a
subset of misinformation. It is purposely distributed to trick others online,
and its effect it has continued to expand Galitsky (2015). In the truthful yet
incorrect conviction that the spread imprecise truths remain real,
misinformation is communicated. Disinformation, however, describes incorrect
truths which are considered to purposefully mislead viewers and listeners. In
particular, Misinformation x is misleading or incorrect knowledge that is
intentionally meant to mislead. Disinformation is incorrect information
designed to confuse particularly rhetoric provided to a competitor force or
the press through a government department. People’s acceptance of
misinformation or misleading facts depends on their past convictions and views
Libicki (2007). The key characteristics of disinformation have been
highlighted by scientists Fallis (2009) are:

  * •

Disinformation is often the result of a deception operation that is carefully
orchestrated and technically advanced.

  * •

Disinformation could not come from the source who aims to mislead directly.

  * •

Disinformation is frequently written to contain adjusted photographs.

  * •

Disinformation may be very broadly spread or aimed at particular individuals
or organizations.

  * •

The ultimate objective is often a person or a collection of individuals.

##  4 Misinformation for specific event versus specific type

Misinformation, while very popular in politics, nonetheless, can be seen in
other areas such as: economics, education, culture, environments, heath and
others Lewandowsky et al. (2017), Treen et al. (2020). In the U.S., in
particular, 2016 election was a landmark for misinformation. Influence on the
election from several actors were reported, Cosentino (2020a), Cosentino
(2020b).
Authors in Marwick and Lewis (2017) indicated that the reason behind the
exploitation of fake news was a combination of political ideology and economic
interests along with eager for publicity. Significant worldwide attention was
given to online political campaigning through election times. OSNs such as
Twitter and Facebook deleted many accounts that could be affiliated with
bot/troll accounts Gleicher (2018).

Beyond U.S. elections, many political or crisis times witnessed similar
situations of large scale sharing of misinformation and possible use of
bot/troll accounts. Followings are examples of such recent events:

  * •

University of Missouri protests, 2015-2016, Prier (2017a), Prier (2017b).

  * •

French elections, Bulckaert (2018), Vilmer et al. (2018), Morgan (2018),
Douglas (2018).

  * •

Brexit, Bastos and Mercea (2019), Narayanan et al. (2017), Marshall and
Drieschova (2018).

  * •

Anti-immigration narrative, Poole et al. (2019)

  * •

Estonia cyber attacks, Herzog (2017), Van Niekerk (2018).

  * •

COVID19, van der Linden et al. (2020), Apuke and Omar (2021), Orso et al.
(2020).

##  5 Misinformation versus biased information

Misinformation can be categorized into three different categories Wani et al.
(2021):

  * •

It can be totally wrong.

  * •

It can be a distributed perception with no genuine evidence or.

  * •

It can carry misleading responses where targeted truths are provided in order
to obtain some rhetoric of deception. Biased information is used when partial
data is published in order to promote one side of a debate.

The study Vosoughi et al. (2018) observed that unreliable social media
information spreads much more rapidly than fact-based content. It has been
noted that content truthfulness is not a motivating factor for the
dissemination of information; rather, individuals prefer to spread the news
based on their community favoritism, prejudice, or attention. Previous
research efforts showed that the distribution of unreliable social media
information has a substantial effect on terrorism Oh et al. (2013), political
campaigning Bovet and Makse (2019), Shao et al. (2018), and crises management
Lukasik et al. (2016).

Information bias, already a widely obvious problem in the field of media and
social sciences, has also inspired a lot of empirical studies in recent years.
Many media inquiries concentrate their focus on identifying information bias
on specific topics such as elections, immigration, conflicts, or racism
Harrison (2006).

In Leban et al. (2014) nine categories of news bias are identified:

  * •

Bias in topic coverage

  * •

Bias in speed reporting

  * •

Similarity of content

  * •

Events coverage Similarity

  * •

Geographical bias

  * •

Bias in Newswire citation

  * •

Analysis of variations in paper length

  * •

Analysis of grammatical distinctions

  * •

Variations in readability.

Researchers in the area of automated news bias detection often turns its
emphasis to the study of sentiment and the mining of opinions in the news.
Authors in Leban et al. (2014) indicated that the geographical
differences/similarities between the examined news publishers are related to
most forms of detected bias. For example, European news outlets put more
emphasis on explaining events happening in Europe, while US publishers put
more emphasis on events happening in the U.S. It is possible to find a similar
trend that is related to news agency citations. Associated Press is often
quoted by US news outlets, although European publishers tend to cite European
news agencies. Among the tabloid outlets such as Daily Mail or Stern Magazine,
they have also found a clear bias to write longer names, shorter articles and
to use more descriptive language using multiple adjectives and adverbs. We
also noticed a bigger percentage of adverbs and adjectives on websites, as
expected.

##  6 Spread of misinformation articles and fact-checking articles

Can we see different patterns of we see how misinformation spread versus
credible information ? One problem related to misinformation correction is
that in many cases the reach of a fact-check about a misinformation or claim
will be less than the reach of original claim or misinformation. It is found
that in many cases, bots rather than humans spread misinformation, Shao et al.
(2017),Schlitzer (2018), Alsmadi and O’Brien (2020) while at the same time,
the effort of human-based fact-checking websites is limited due to many
factors such as the limitation or availability of expertise and resources.
Some references indicate that users who spread misinformation may refer to
fact-checking links that falsify such claims. Nonetheless, those users will
still distribute such misinformation, Shao et al. (2018).

Online Social Networks (OSNs) are rich platforms to spread misinformation fast
as a result of the tension between aggregation of information and spread of
misinformation, Acemoglu et al. (2010), Alsmadi and OBrien (2019), Amoruso et
al. (2020).

The type of information/misinformation has a major factor in the spread of
misinformation. By far, misinformation related to politics spread much faster
than any other type of misinformation. Political participation will be
associated with sharing or spreading misinformation when it conforms to
individuals’ beliefs, Valenzuela et al. (2019). In this scope, US election in
2016 (and 2020) were milestones where the subject of misinformation in OSNs
evolved rapidly.

Aside from the political domain, large scale or world-wide crises such as
Corona virus pandemic are usually surrounded by lots of misinformation driven
by lack of credible information. For COVID-19 in particular, misinformation is
related to several aspects including, virus origin, how it can reach and
spread among humans, possible treatments, etc.

Some papers investigated users based on their cognitive decisions to spread
misinformation and the amount of effort they may do to fact-check such
information before spreading it, Greenberg et al. (2013), Castillo et al.
(2011), Lupia (2013), Swire et al. (2017). Researchers studied also the impact
of top OSNs influencers in spreading misinformation. Misinformation
distribution agents may not need to be top influencers, they could be social
bots or forceful agents who emerge as dominant voices in a dispute over
claims, Acemoglu et al. (2010), Groshek et al. (2018), Shao et al. (2018).

The spread of misinformation can be investigated from the different possible
users intentions or goals behind such act (e.g. unintentional, misleading
readers, inciting clicks for revenue or supporting/manipulating public
opinions). People response to misinformation can be different and vary
between:

  * •

Positive response to support and participate in spreading such misinformation.

  * •

Neutral response to ignore the misinformation without any further personal
analysis or response.

  * •

Negative response to respond back to those who spread or originate
misinformation.

In terms of diffusion and survival, how long the spread of misinformation can
survive? Can misinformation be persistent despite the fact that many fact-
checking posts exist to respond to and falsify such misinformation with proof
or evidence? Observations from US elections in 2016 and 2020 show that some
misinformation continue to evolve and find their audience despite the response
from fact-checking websites or posts. Nonetheless, researchers investigated
persistence as one factor to differentiate between the spread of information
versus misinformation, Wang et al. (2018).

##  7 Believing versus sharing misinformation

What is the percentage of people who share misinformation with good intention
unknowing that its not an accurate information ? If they share such
misinformation intentionally what motivates them to do so? Understanding the
motivations behind sharing misinformation may help us evaluating the impact as
well as methods to detect and mitigate such misinformation.

Most studies assume that people do not realize the information they share is
false, Metzger et al. (2021), Talwar et al. (2019), Duffy et al. (2020), Chen
et al. (2015b), Chadwick and Vaccari (2019). Four main reasons are identified
in literature behind information sharing in general, Chen et al. (2015b):
entertainment, socializing, information seeking and self-expression and status
seeking.
We can look at those motivations from two dimensions: intentional and
unintentional.

  * •

Unintentional sharing of misinformation: The following reasons can be observed
in people unintentional spread of misinformation:

    * –

Sharing of information is a social activity. Users in Online Social Networks
(OSNs) share their self-generated content or re-share contents from others as
part of their social reach and interaction with their networks.

    * –

Publicity and social reach: People eager to share more and more information
may override their eagerness and willingness to vet for information
credibility. They are looking for more publicity, impact and attention. Users
in OSNs receive daily large volumes of information.

    * –

Lack of resources: They may re-share information they received with their
networks as part of their social interactions with little time and resources
that they have to fact-check such information. Additionally, the continuous
share of information among social networks makes it hard in many cases to
trace and verify the content originator.

    * –

Emotions: People may share misinformation when they are angry or upset as part
of reaction to a crisis or negative news Han et al. (2020)

  * •

Intentional sharing of misinformation: This has been associated with political
or religious orientations, self-disclosure, online trust, and social media
fatigue, Talwar et al. (2019). People may share misinformation intentionally
also to discuss it, neutralize it or correct it, Rossini et al. (2020).

Political participation is positively associated with misinformation sharing,
specially when it comes to misinformed users Valenzuela et al. (2019),
Boulianne (2019). Those users receive information only through their political
party media or channels. While different references indicated that in the U.S.
misinformation is more correlated with right-leaning partisanship, yet they do
come from left party as well, Nikolov et al. (2020).

##  8 Information/Misinformation: Binary versus multi-class

Most research projects and applications of misinformation would like to deal
with information/misinformation as a binary problem. However, in many cases,
specially in OSNs context, judging information/misinformation from a binary
perspective is not realistic since in most cases, the subject content cannot
be explicitly classified as absolutely true or absolutely false. Generally,
they can be a combination of misinformation and true news. For such reasons,
many research publications (e.g. Rasool et al. (2019), Kaliyar et al. (2019) )
argued that a multi-class classification is more realistic.

Another issue related to machine-based classification of misinformation is
related to the different meaning and interpretation of misinformation. We see
a wide range of terms used to label some kind of misinformation such as: fake
news, rumors, hoaxes, insincere questions, satire, click-baits, stance, etc.
In each one of those different classifications of misinformation, a machine
learning algorithm will have to classify the subject content based on its
specific target, regardless whether the information is correct or not. For
example, in Quora classification, an insincere question can be a question that
is raised not looking for an answer but rather pass a message (e.g. mocking a
person, faith or culture). Additionally, a click-bait is a content in a
website in which the title of that content has nothing to do with the actual
content. In those examples, the machine learning algorithm will have to decide
whether the content is "relevant" to the target, rather than if its correct
content or not.

##  9 Summary and Conclusion

In this paper we evaluated misinformation evolving terminologies and
perspectives. Users through OSNs and smart phones can now create their own
contents, respond or re-share other users’ content. Each user can be literally
a news channel, through their OSN page. Their is a need to redefine terms
related not only to information/misinformation but also to news and news
outlets. In many cases, contents generated by users will have a mixture of a
piece of news (that can be correct) and users’ own personal reflection or
annotation. Classically, TV channels and newspapers used to be main sources of
news. Nowadays news outlets are enormous and so credibility is at a serious
risk.

People rely heavily on search engines to search for information. Search
engines have no built-in methods to check for information credibility. Even
worse, they do classify search retrieved results based on popularity rather
than based on credibility-related metrics. Without building reliable methods
to detect, tag and warn against inaccurate information, we will be risking
civilization history.

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