# SOK: Fake News Outbreak 2021: Can We Stop the Viral Spread?

Tanveer Khan  Tampere University<EMAIL_ADDRESS>0000-0001-7296-2178 ,
Antonis Michalas  Tampere University<EMAIL_ADDRESS>0000-0002-0189-3520 and  Adnan Akhunzada  Technical University of Denmark
<EMAIL_ADDRESS>
###### Abstract.

Social Networks’ omnipresence and ease of use has revolutionized the
generation and distribution of information in today’s world. However, easy
access to information does not equal an increased level of public knowledge.
Unlike traditional media channels, social networks also facilitate faster and
wider spread of disinformation and misinformation. Viral spread of false
information has serious implications on the behaviours, attitudes and beliefs
of the public, and ultimately can seriously endanger the democratic processes.
Limiting false information’s negative impact through early detection and
control of extensive spread presents the main challenge facing researchers
today. In this survey paper, we extensively analyse a wide range of different
solutions for the early detection of fake news in the existing literature.
More precisely, we examine Machine Learning (ML) models for the identification
and classification of fake news, online fake news detection competitions,
statistical outputs as well as the advantages and disadvantages of some of the
available data sets. Finally, we evaluate the online web browsing tools
available for detecting and mitigating fake news and present some open
research challenges.

††doi: 10.1145/1122445.1122456††copyright: none

##  1\. Introduction

The popularity of Online Social Networks (OSNs) has rapidly increased in
recent years. Social media has shaped the digital world to an extent it is now
an indispensable part of life for most of us (Gazi and Çetin, 2017). Rapid and
extensive adoption of online services is influencing and changing how we
access information, how we organize to demand political change and how we find
partners. One of the main advantages and attractions of social media is the
fact that it is fast and free. This technology has dramatically reshaped the
news and media industries since becoming a dominant and growing source of news
and information for hundreds of millions of people (Kaplan, 2015). In the
United States today more people are using social media as a news source than
ever before (Suciu, [n. d.]). Social media has progressively changed the way
we consume and create news. The ease of producing and distributing news
through OSNs has also simultaneously sharply increased the spread of fake
news.

Fake news is not a new phenomenon; it existed long before the arrival of
social media. However, following the 2016 US presidential election it has
become a buzzword (Albright, 2016). There are numerous examples of fake news
trough history. A notable one from the antiquity is the Mark Anthony smear
campaign circa 44 BC (Posetti and Matthews, 2018). In more recent times,
examples include the anti-German campaign, German corpse factory in 1917
(Neander and Marlin, 2010) and the Reich Ministry of Public Enlightenment and
Propaganda established in 1933 by the Nazis to spread Nazi ideology and incite
violence against Jews (Bytwerk, 2010).

Although propaganda campaigns and spread of fabricated news may have been
around for centuries, their fast and effective dissemination only became
possible by means of a modern technology such as the internet. The internet
revolutionized fake news, regardless of how the misinformation is manifested:
whether we are talking about a rumor, disinformation, or biased, sloppy,
erroneous reporting. In a recent study (Wong, 2016), it was found that almost
50 percent of traffic taken from Facebook is fake and hyperpartisan, while at
the same time, news publishers relied on Facebook for 20 percent of their
traffic. In another study, it was found that 8 percent of 25 million Universal
Resource Locator (URLs) posted on social media were indicative of malware,
phishing and scams (Thomas, 2013).

Researchers in Germany conducted a study regarding fake news distribution in
the country and people’s attitudes and reactions towards it (Christian Reuter
and Schlegel, 2019). Based on the published results, 59 percent of
participants stated that they had encountered fake news; in some regions, this
number increased to almost 80 percent (The Law Library of Congress, 2019).
Furthermore, more than 80 percent of participants agreed fake news poses a
threat and 78 percent strongly believed it directly harms democracy.
Government institutions and powerful individuals use it as a weapon against
their opponents (Carson, 2019). In the 2016 US presidential election, a
significant shift in how social media was used to reinforce and popularize
narrow opinions was observed. In November of the same year, 159 million visits
to fake news websites were recorded (Allcott and Gentzkow, 2017), while the
most widely shared stories were considered to be fake (Silverman, 2016).
Similarly, it is believed that the distribution of fake news influenced the UK
European Union membership referendum (Grice, 2017).

However, fake news is not only about politics. During the recent fires in
Australia, several maps and pictures of Australia’s unprecedented bushfires
spread widely on social media. While users posted them to raise awareness, the
result was exactly the opposite since some of the viral maps were misleading,
spreading disinformation that could even cost human lives (Rannard, 2020). The
recent COVID-19 pandemic accelerated the rise of conspiracy theories in social
media. Some were alleging that the novel coronavirus is a bio-weapon funded by
Bill Gates to increase the selling of vaccines (for minds, [n. d.]).
Undoubtedly fake news threaten multiple spheres of life and can bring
devastation not only to economic and political aspects but peoples’ wellbeing
and lives.

###  1.1. An overview of this Survey

The main motivation behind our study was to provide a comprehensive overview
of the methods already used in fake news detection as well as bridge the
knowledge gap in the field, thereby helping boost interdisciplinary research
collaboration. This work’s main aim is to provide a general introduction to
the current state of research in the field.

We performed an extensive search of a wide range of existing solutions
designed primarily to detect fake news. The studies used deal with
identification of fake news based on ML models, network propagation models,
fact-checking methods etc. More precisely, we start by examining how
researchers formulate ML models for the identification and classification of
fake news, which tools are used for detecting fake news and conclude by
identifying open research challenges in this domain.

#### Comparison to Related Surveys

In a related work by Vitaly Klyuev (Klyuev, 2018), an overview of the
different semantic methods by concentrating on Natural Language Processing
(NLP) and text mining techniques was provided. In addition, the author also
discussed automatic fact-checking as well as the detection of social bots. In
another study, Oshikawa et al. (Oshikawa et al., 2018) focused on the
automatic detection of fake news using NLP techniques only. Two studies can be
singled out as being the closest to our work. First, Study by Collins et al.
(Collins et al., 2020) which examined fake news detection models by studying
the various variants of fake news and provided a review of recent trends in
combating malicious contents on social media. Second, a study by Shu et al.
(Shu et al., 2020a) which mostly focused on various forms of disinformation,
factors influencing it and mitigating approaches.

Although some similarities are inevitable, our work varies from the
aforementioned ones. We provide a more detailed description of some of the
approaches used and highlight the advantages and limitations of some of the
methods. Additionally, our work is not limited to NLP techniques, but also
examines types of detection models available, such as, knowledge-based
approaches, fact-checking (manual and automatic) and hybrid approaches.
Furthermore, our approach considers how the NLP techniques are used for the
detection of other variants of fake news such as rumors, clickbaits,
misinformation and disinformation. Finally, it also examines the governmental
approaches taken to combat fake news and its variants.

###  1.2. Organization

The rest of this paper is organized as follows: Section 2 discusses the most
important methods for detecting fake news, in Section 3, we detailed both the
automatic and manual assessment of news and analyzed different ways of
measuring the relevance, credibility and quality of sources. To automate the
process of fake news detection, the analysis of comprehensive data sets is of
paramount importance. To this end, in Section 4, we first discuss the
characteristics of online tools used for identifying fake news and then
compare and discuss different data sets used to train ML algorithms to
effectively identify fake news. The classification of existing literature,
identified challenges, future directions and existing governmental strategies
to tackle the problem of fake news detection are discussed in Section 5.
Finally, the concluding remarks are given in Section 6.

##  2\. Fake News Analysis

People are heavily dependent on social media for getting information and spend
a substantial amount of time interacting on it. In 2018, the Pew Research
Center revealed that 68 percent of Americans (Matsa and Shearer, 2018) used
social media to obtain information. On average, 45 percent of the world’s
population spend 2 hours and 23 minutes per day on social media and this
figure is constantly increasing (Asano, 2017). The biggest problem with
information available on social media is its low quality. Unlike the
traditional media, at the moment, there is no regulatory authority checking
the quality of information shared on social media. The negative potential of
such unchecked information became evident during the the 2016 US presidential
election111https://www.independent.co.uk/life-style/gadgets-and-
tech/news/tumblr-russian-hacking-us-presidential-election-fake-news-internet-
research-agency-propaganda-bots-a8274321.html. In short, it is of paramount
importance to start considering fake news as a critical issue that needs to be
solved.

In spite of the overwhelming evidence supporting the need to detect fake news,
there is, as yet, no universally accepted definition of fake news. According
to (Lazer et al., 2018), “fake news is fabricated information that mimics news
media content in form but not in organizational process or intent”. In a
similar way, fake news is defined as “a news article that is intentionally and
verifiable false” (Shu et al., 2017). Some articles also associate fake news
with terms such as deceptive news (Allcott and Gentzkow, 2017), satire news
(Rubin et al., 2015), clickbait (Chen et al., 2015), rumors (Zubiaga et al.,
2018), misinformation (Kucharski, 2016), and disinformation (Kshetri and Voas,
2017). Hence, these terms are used interchangeably in this survey.

The following forms of misuse of information have been considered as variants
of fake news in the existing literature (Tandoc Jr et al., 2018; Rubin et al.,
2015):

  * •

Clickbait: Snappy headlines that easily capture user attention without
fulfilling user expectations since they are often tenuously related to the
actual story. Their main aim is to increase revenue by increasing the number
of visitors to a website.

  * •

Propaganda: Deliberately biased information designed to mislead the audience.
Recently, an increased interest has been observed in propaganda due to its
relevance to the political events (Rubin et al., 2015).

  * •

Satire or Parody: Fake information published by several websites for the
entertainment of users such as “The Daily Mash” website. This type of fake
news typically use exaggeration or humor to present audiences with news
updates.

  * •

Sloppy Journalism: Unreliable and unverified information shared by journalists
that can mislead readers.

  * •

Misleading Headings: Stories that are not completely false, but feature
sensationalist or misleading headlines.

  * •

Slanted or Biased News: Information that describes one side of a story by
suppressing evidence that supports the other side or argument.

For years, researchers have been working to develop algorithms to analyze the
content and evaluate the context of information published by users. Our review
of the existing literature is organised in the following way: subsection 2.1,
examines approaches to identifying different types of user accounts such as
bots, spammers and cyborgs. It is followed by subsection 2.2, where different
methods used for identifying rumors and clickbaits are discussed. In
subsection 2.3, the users’ content and context features are considered while
in subsection 2.4, different approaches for the early detection of fake news
by considering its propagation are discussed.

###  2.1. User Account Analysis

According to a report published in 2021 Twitter alone has 340 million users,
11.7 million registered apps, delivers 500 million tweets a day and 200
billion tweets a year (Aslam, 2021). It’s popularity has made it an ideal
target for bots, or automated programs (Kaur et al., 2018). Recently, it was
reported that around 5-10 percent of Twitter accounts are bots and responsible
for the generation of 20-25 percent of all tweets (of Eastern Finland, 2019).
Some of the bots are legitimate, comply with Twitter objectives, and can
generate a substantial volume of benign tweets like blogs and news updates.
Other bots, however, can be used for malicious purposes such as a malware that
gathers passwords or a spam that adds random users as friends and expects to
be followed back (IONOS, 2018). Such bots have a more detrimental effect
particularly when spreading fake news. The significance of differentiating the
legitimate bots from the malicious ones emerged from the fact that malicious
bots can also be used to mimic human behaviour in a negative way.

Researchers examined bots, in a number of existing publications (Cresci et
al., 2017; Gibert et al., 2020; Edwards et al., 2014; Lee et al., 2011; Wu et
al., 2013; Stone-Gross et al., 2009). Gilani et al. (Gilani et al., 2017)
focused on classifying Twitter accounts into “human” and “bots” and analyzing
the impact each has on Twitter. The proposed technique was based on previous
work by “Stweeler” (gilani, 2018) for the collection, processing, analysis,
and annotation of data. For the identification of bots, human annotation was
used, where participants differentiated bots from humans and generated a
reliable data set for classification. The process provided an in-depth
characterization of bots and humans by observing differences and similarities.
The finding stated that the bots’ removal from Twitter causes serious
repercussions for content production and information dissemination and also
indicated that bots count on re-tweeting, redirecting users via URLs, and
uploading media. However, the imprecision in the existing algorithm revealed
by the authors and the manual collection of data limited the ability to
analyse accounts.

Similarly, Giachanou et al. (Giachanou and Ghanem, 2019) investigated whether
the Twitter account author is human or a bot and further determined the gender
of a human account. For this purpose, a linear Support Vector Machines (SVM)
classifier was trained to analyse words, character grams, and stylistic
features. For the identification of human gender, a stochastic gradient
descent classifier was used to assess the sentiment of tweets, words, and
character grams and point wise mutual information features – the importance of
terms per gender. The data set used consisted of tweets in English and
Spanish. The experiments illustrated the accuracy of bot detection, i.e 0.906
for bots in English and 0.856 for Spanish. Similarly, for the identification
of gender, the accuracy for English tweets amounted to 0.773 and 0.648 for
Spanish tweets. In the long run, the bot detection model outperformed the
gender detection model.

Another account type that can be generated on Twitter is a Cyborg. Cyborg
refers to a human-assisted bot or bot-assisted human (Chu et al., 2010).
Cyborgs have characteristics of both human-generated and bot-generated
accounts and as such require a level of human engagement. These accounts
faciltate posting various information more frequently, rapidly and long-term
(DFRLab, 2016). Differentiating a cyborg from a human can be a challenging
task. The automated turing test (Von Ahn et al., 2004) used to detect
undesirable or bot programs is not capable of differentiating cyborgs from
humans. However, Jeff Yan (Yan, 2006) proposed that a cyborg might be
differentiated by comparing the characteristics of a machine and human
elements of a cyborg. Similarly, Chu et al. (Chu et al., 2012) differentiate
between bot, cyborg and human accounts by taking into account tweet content,
tweeting behaviour and features of the account.

OSNs also serve as platforms for the rapid creation and spread of spam.
Spammers act similarly to bots and are responsible for posting malicious
links, prohibited content and phishing sites (Perez, 2018; Michalas and
Murray, 2017). Traditional methods of detecting spammers that utilize network
structure are classified into three categories:

  * •

Link-based, where the number of links is used as a measure of trust. These
links are considered to be built by legitimate users (Lee et al., 2011).

  * •

Neighbor-based, which treats links as a measure of homophily, the tendency for
linked users to share similar beliefs and values (Hu et al., 2013; Rayana and
Akoglu, 2015; Li et al., 2016).

  * •

Group-based, which recognizes that spammers often work in groups to coordinate
attacks (Jindal and Liu, 2007). Group-based methods detect spammers by taking
advantage of the group structure hidden in the social network. Additionally,
spammers behave differently from legitimate users so they can be treated as
outliers (Gao et al., 2010; Akoglu et al., 2015).

Current efforts for detection of social spammers utilize the structure and
behavioural patterns of social spammers in an attempt to discover how their
behaviour can be differentiated from legitimate users (Lim et al., 2010; Chu
et al., 2010; Ye et al., 2016; Li et al., 2015; Xue et al., 2013; Yang et al.,
2014). However, spammers often find ways to create a link with legitimate
users, making it more difficult to detect specific spamming patterns. Wu et
al. (Wu et al., 2017a) tackled this problem by taking into account both
content and network structure. They proposed “Sparse Group Modelling for
Adaptive Spammer Detection (SGASD)” that can detect both types of spammers –
those within a group and individuals.

Another challenging task is detection of camouflaged content polluters on
OSNs. Content polluters – spammers, scanners and fraudsters – first establish
links with a legitimate user and then merge the malicious with real content.
Due to insufficient label information available for camouflaged posts in
online media, the use of these manipulated links and contents as camouflage
makes detecting polluters very difficult. In order to tackle this challenge,
Wu et al. (Wu et al., 2017b) studied how camouflaged content polluters can be
detected and proposed a method called “Camouflaged Content Polluters using
Discriminate Analysis (CCPDA)” which can detect content polluters using the
patterns of camouflaged pollution.

Chris et al. (Grier et al., 2010) spam detection analysis juxtaposed two
different types of Twitter accounts – a “professional spamming account” whose
sole purpose is to distribute spam, versus “accounts compromised by spammers”.
The authors found that accounts currently sending spam had been compromised by
spammers; once legitimate, they became controlled by spammers. Furthermore, to
detect spam activity on Twitter, a directed social graph model (Wang, 2010)
based on friend and follower relationships was proposed. Different classifier
techniques were used to distinguish between the spammer and normal behaviour
and determined that the Naive Bayes classifier performs better with respect to
F-measure.

Huge momentum has been observed where user-generated content is exploited in
micro-blogs for predicting real-world phenomena such as prices and traded
stock volume on financial markets (Cresci et al., 2016). Research efforts in
this domain targeted sentiment metrics as a predictor for stock prices (Bollen
et al., 2011; Chen et al., 2014; Gabrovšek et al., 2017), company tweets and
the topology of the stock network (Mao et al., 2012; Ruiz et al., 2012) and
used weblogs pointing to the relationship between companies (Kharratzadeh and
Coates, 2012). Cresci et al. (Cresci et al., 2019) demonstrated the use of
twitter stock micro-blogs as a platform for bots and spammers to practice
cash-tag piggybacking – an activity for promoting low-value stocks by
exploiting the popularity of real high-value stocks. They employed a spambot
detection algorithm to detect accounts that issue suspicious financial tweets.
Nine million tweets from five main US financial markets, which presented
stocks with unusually high social significance compared to their low financial
relevance, were investigated with respect to their social and financial
significance. These tweets were compared with financial data from Google
finance. The results indicated that 71 percent of users were classified as
bots and that high discussion of low-value financial stocks was due to a
massive number of synchronized tweets.

Twitter currently has no defined policy for addressing automated malicious
programs operating on its platform. However, it is expected that these
malicious accounts will be deleted in the near future (Schwartz, 2018). A
survey of the literature has identified numerous studies (Erşahin et al.,
2017; Lee et al., 2010; Gilani et al., 2017; Davis et al., 2016; Antoniadis et
al., 2015; Weimer et al., 2007; Adewole et al., 2017; Wanas et al., 2008;
Weerkamp and De Rijke, 2008; Morris et al., 2012) that describe the important
characteristics which can be used for the identification of bots on Twitter.
Despite these attempts, limitations still exist in employing these
characteristics for detecting fake news, especially, early detection of fake
news during it’s propagation. Other methods, such as network propagation, have
to be utilized for this purpose.

###  2.2. Identifying Rumors and Clickbaits

Social media is like a blank sheet of paper on which anything can be written
(Yaraghi, 2019), and people easily become dependent on it as a channel for
sharing information. This exactly is the reason why social media platforms
(e.g. Twitter and Facebook) are highly scrutinized for the information shared
on them (Haralabopoulos et al., 2015). These platforms have undertaken some
efforts to combat the spread of fake news but have largely failed to minimize
its effect. For instance, in the United States, 60 percent of adults who
depend on social media for news consumption are sharing false information
(Wong, 2019). In April 2013, two explosions during the Boston Marathon gained
tremendous notoriety in the news and on social media, and the tragedy was
commented on in millions of tweets. However, many of those tweets were rumors
(controversial factual claims) and contained fake information, including
conspiracy theories. Similarly, a survey published by Kroll – a business
intelligence and investigating firm – states that 84 percent of companies feel
threatened by the rise of rumors and fake news fuelled by social media
(Binham, 2019). On Weibo, rumors were detected in more than one-third of
trending topics (Zhao et al., 2015). The spread of rumors on social media has
also become an important issue for companies worldwide. Still, there is no
clear policy defined by social media administrators to verify shared content.
Below, we discuss different techniques that have been proposed by researchers
to address this problem.

Traditionally, human observers have been used to identify trending rumors.
Currently, research is focused on building an automated rumor identification
tool. For this purpose, a rumor detection technique (Zhao et al., 2015) was
designed. In this technique, two types of clusters were generated: posts
containing words of inquiry such as “Really”, “What”, “Is it true?” were
grouped into one cluster. These inquiries were then used to detect rumor
clusters. Similarly, posts without words of inquiry were grouped into another
cluster. Similar statements were extracted from both clusters. The clusters
were then ranked, based on their likelihood of containing these words. Later,
the entire cluster was scanned for disputed claims. These experiments,
performed with Twitter data, resulted in earlier and effective detection of
rumors (almost 50 rumor clusters were identified). However, there is still
considerable space to improve these results (Zhao et al., 2015). For instance,
the manual collection of inquiry words could be improved by training a
classifier and the process of ranking could be improved by exploring more
features for the rumor cluster algorithm.

People can share fake information on social media for various reasons. One of
those is to increase readership, which is easily achievable by using
clickbait. Clickbait is a false advertisement with an attached hyperlink. It
is specifically designed to get users to view and read the contents inside the
link (Anna Escher, 2016). These advertisements attract users with catchy
headlines but contain little in the way of meaningful content. A large number
of users are lured by clickbait. Monther et al. (Aldwairi and Alwahedi, 2018)
provided a solution to protect users from clickbait in the form of a tool that
filters and detects sites containing fake news. In categorizing a web page as
a source of fake news, they considered several factors. The tool navigates the
content of a web page, analyzes the syntactical structure of the links and
searches for words that might have a misleading effect. The user is then
notified before accessing the web page. In addition, the tool searches for the
words associated with the title in the links and compares it with a certain
threshold. It also monitors punctuation marks such as question and exclamation
marks used on the web page, marking it as a potential clickbait. Furthermore,
they examined the bounce rate factor–percentage of visitors who leave a
website, associated with the web page. Where the bounce rate factor was high,
the content was marked as a potential source of misleading information.

A competition was organised with the aim of building a classifier rating the
extent to which a media post can be described as clickbait. In the clickbait
competition, the data set was generated from Twitter and consisted of 38,517
Twitter posts from 27 US news publishers (Potthast et al., 2018). Out of
38,517 tweets, 19,538 were available in the training set and 18,979 were
available for testing. For each tweet, a clickbait score was assigned by five
annotators from Amazon Mechanical turk. The clickbait scores assigned by human
evaluators were: 1.0 heavily clickbaity, 0.66 considerably clickbaity, 0.33
slightly clickbaity and 0.0 not clickbaity. The goal was to propose a
regression model that could determine the probable clickbaitiness of a post.
The evaluation metric used for the competition was Mean Squared Error (MSE).
In this competition, Omidvar et al. (Omidvar et al., 2018) proposed a model
using the deep learning method and won the challenge. They achieved the lowest
MSE for clickbait detection by using a bi-directional Gated Recurrent Unit
(biGRU). Instead of solving the clickbait challenge using a regression model,
Yiewi zhou (Zhou, 2017) reformulated the problem as a multi-classification. On
the hidden state of biGRU, a token level self-attentive mechanism was applied
to perform multi-classification. This self attentive Neural Network (NN) was
trained without performing any manual feature engineering. They used 5 self-
attentive NNs with a 80-20 percent split and obtained the second lowest MSE
value. Similarly, Alexey Grigorev (Grigorev, 2017) proposed an ensemble of
Linear SVM models to solve the clickbait problem and achieved the third lowest
MSE value. In addition to the given data set, they gathered more data from
multiple Facebook groups that mostly contained clickbait posts by using the
approach described in “identified clickbaits using ML” (Thakur, 2016).

###  2.3. Content and Context Analysis for Fake News

The rapid dissemination of fake news is so pernicious that researchers
resolved towards trying to automate the process by using ML techniques such as
Deep Neural Networks (DNN). However, the Black box problem – a lack of
transparency in decision-making in the NN – obscures reliability. Nicole et
al. (O’Brien et al., 2018) addressed the deep learning “black-box problem” for
fake news detection. A data set composed of 24,000 articles was created,
consisting of 12,000 fake and 12,000 genuine articles. The fake articles were
collected from Kaggle while genuine ones were sourced from The Guardian and
The New York Times. The study concluded that DNNs can be used to detect the
language patterns in fabricated news. Additionally, the algorithm can also be
used for detecting fake news in novel topics.

Another technique to tackle the deep learning “black-box problem” in fake news
detection is CSI (capture, score and integrate) – a three-step system which
incorporates the three basic characteristics of fabricated news (Ruchansky et
al., 2017). These characteristics include text, source, and the response
provided by users to articulate missing information. In the first step, a
Recurrent Neural Network (RNN) is used to capture the momentary pattern of
user activity. The second step estimates the source of suspicions related to
user behaviour. The third, hybrid step involves integration of steps one and
two and is used to predict fake articles. The experiments were performed on
real-world data sets and demonstrated a high level of accuracy in predicting
fake articles. Still, a bottleneck in using a computationally intensive model
is posed by the lack of a manually labelled fake news data set. William Yang
Wang (Wang, 2017) addressed the limited availability of labelled data sets for
combating fake news using statistical approaches and chose a contemporary
publicly available data set called LIAR. This data set was utilized to
investigate fabricated news using linguistic patterns. The results were based
on an evaluation of several approaches, including Logistic Regression (LR),
the Convolution Neural Network (CNN) model, Long Short-Term Memory (LSTM)
networks and SVM. They concluded that combination of meta-data with text
significantly improves the detection of fake news. According to the authors,
this body of information can also be used to detect rumors, classify stance
and carry out topic modeling, argument mining, and political Natural Language
Processing (NLP) research. Table LABEL:tab:botsandclickbaits present a summary
of the different approaches proposed for both the account as well as content
and context analysis of fake news.

In 2017 a competition named Fake News Challenge (FNC) was held with the aim to
use Artificial Intelligence (AI) techniques to combat the problem of fake
news. During the initial phase, stance detection was used. It refers to the
relative stance to any issue, claim or topic made by two pieces of relevant
text (what other organizations say about the topic). A two-level scoring
system was applied – 25 percent weight was assigned if the text was deemed to
be related or unrelated to its headline and 75 percent weight was assigned on
the basis of labelling the related pairs as agrees, disagrees, discusses or
unrelated. In this competition, the top team submitted an ensemble model for a
Deep Convolution Neural Network (DCNN) and Gradient-Boosted Decision Tree
(GBDT) with a weighted average of 50/50 (Sean Baird, 2017). The DCNN and GBDT
separately did not achieve perfect accuracy. However, the combination of both
approaches correctly detected the stance of each headline with a score of
82.01. Similarly, approach proposed by team Athene (Hanselowski, 2017)
achieved a score of 81.97 and won second place in the competition. They used
an ensemble approach involving multi-layer perception and applied MLP and Bag-
of-Word (BoW) features to the challenge. The team in third place, Riedel et
al. (Riedel et al., 2017), proposed a stance detection system for FNC Stage 1.
For the input text, they used two BoW representations. A MLP classifier was
used with one hidden layer having 100 units. For the hidden layer, a Rectified
Linear Unit (RELU) activation function was used while the final linear layer
utilized a soft-max. They achieved an accuracy of 81.72.

At a different competition named Web Search and Data Mining (WSDN) 2019, fake
news was detected by classifying the titles of articles. Using a given title
for any fake news article ‘A’ and a title for another incoming news article
‘B’, people were asked to classify the incoming article into one of three
categories: agrees, disagrees and unrelated (Risdal, 2017). The winner of this
competition Lam Pham (Pham, 2019), who achieved 88.298 percent weighted
accuracy on the private leader boards and 88.098 percent weighted accuracy on
the public leader boards. This ensemble approach incorporated NNs and gradient
boosting trees. In addition, Bidirectional Encoder Representation from
Transformer (BERT) was used for encoding news title pairs, transforming and
incorporating them into a new representational space. The approach by Liu et
al. won a second place (Liu et al., 2019) by proposing a novel ensemble
framework based on the Natural Language Interference (NLI) task. Their
proposed framework for fake news classification consisted of three-level
architecture with a 25 BERT model along with a blending ensemble strategy in
the first level followed by 6 ML models and finally a single LR for the final
classification. Yang et al. (Yang et al., 2019) also considered this problem
as a NLI task and considered both the NLI model as well as the BERT. They
trained the strongest NLI models, Dense RNN, Dense CNN, ESIM, Gate CNN
(Dauphin et al., 2017) and decomposable attention, and achieved an accuracy of
88.063 percent.

###  2.4. Network Propagation and Detection of Fake News

One of the OSNs main strong points is facilitating the propagation of
information between users. The information of interest to users is further
shared with relatives, friends, etc (Kong et al., 2019). In order to detect
the propagation of fake news at its early stage, it is crucial to be able to
understand and measure the information propagation process. The influence of
propagation on OSNs and their impact on network structure was studied in
(Saxena et al., 2015; Hong et al., 2011). Ye et al. (Ye and Wu, 2010) study
revealed that more than 45.1 percent of information shared by a user on social
media is further propagated by his/her followers. Furthermore, approximately
37.1 percent of the information shared is propagated up to 4 hops from the
original publisher.

Liu and Wu (Liu and Wu, 2018) used the data network features and introduced a
popular network model for the early detection of fake news. They addressed the
limitation of low accuracy of early fake news detection by classifying news
propagation paths as a multivariate time series. Characteristics of each user
involved in spreading news were represented by a numerical vector. Then a time
series classifier was built by combining CNN and RNN. This classifier was used
for fake news detection by capturing the local and global variations of
observed characteristics along the propagation path. This model is considered
as more robust, as it relies on common user characteristics which are more
reliable and accessible in the early stage of news propagation. The
experiments were performed on two real-world data sets based on Weibo (Ma et
al., 2016) and Twitter (Ma et al., 2017). The proposed model detected fake
news within 5 minutes of its spread with 92 percent accuracy for Weibo and 85
percent accuracy for Twitter data sets.

Sebastian et al. (Tschiatschholek et al., 2018) examined the ways to minimize
the spread of fake news at an early stage by stopping its propagation in the
network. They aggregated user flagging, a feature introduced by Facebook that
allows users to flag fake news. In order to utilize this feature efficiently,
the authors developed a technique called ‘DETECTIVE’ which uses Bayesian
Inference to learn flagging accuracy. Extensive experiments were performed by
using a publicly available data set (Leskovec and Mcauley, 2012) from
Facebook. The results indicated that even with minimum user engagement
DETECTIVE can leverage crowd signals to detect fake news. It delivered better
results in comparison to existing algorithms, i.e. NO-Learn and RANDOM.

The dissemination of misinformation on OSNs has a particularly undesirable
effect when it comes to public emergencies. Dynamic Linear Threshold (DLT)
model (Litou et al., 2016) was developed to attempt and limit this type of
information. It analyzes the user’s probability, based on an analysis of
competing beliefs, of propagating either credible or non-credible news.
Moreover, an optimization problem based on DLT was formulated to identify a
certain set of users that could be responsible for limiting the spread of
misinformation by initiating the propagation of credible information.

A study by Garcia et al. (Garcia et al., 2017) focused on examining reputation
(Dimitriou and Michalas, 2012, 2014; Michalas and Komninos, 2014; Bakas et
al., 2020), popularity and social influence on Twitter using digital traces
from 2009 to 2016. They evaluated the global features and specific parameters
that make users more popular, keep them more active and determine their social
influence. Global measures of reputation were calculated by taking into
account the network information for more than 40 million users. These new
global features of reputation are based on the D-core decomposition method
(Giatsidis et al., 2013) and The Twitter’s bow-tie structure (Broder et al.,
2000) in 2009. The results indicated that social influence is more related to
popularity then reputation, and global network metrics such as social
reputation are more accurate predictors for social influence than local
metrics such as followers, etc.

Soroush et al. (Vosoughi et al., 2018) collected and studied twitter data from
2006 to 2007 in order to classify it as true or false news. News is classified
as true or false based on information collected from six independent fact-
checking organizations. They generated a data set that consisted of
approximately 126,000 tweets, tweeted by 3 million twitter users approximately
4.5 million times. They found that fake news was more novel and inspired
surprise, fear, and disgust in replies, while true news inspired trust,
sadness, anticipation, and joy. As people prefer to share novel information,
false news spreads more rapidly, deeply and broadly than true news. According
to Panos et al. (Constantinides et al., 2018), rapid dissemination of
information on social media is due to information cascade. Liang Wu and Huan
Liu (Wu and Liu, 2018) also classified twitter messages using diffusion
network information. Instead of using content features, they focused on the
propagation of Twitter messages. They proposed trace miner, a novel approach
that uses diffusion network information to classify social media messages.
Trace miner accepts message traces as inputs and outputs its category. Table
LABEL:tab:networkandcontent presents a detailed summary of the studies used in
network as well as content and context analysis.

After reviewing the studies discussed above, it became evident there is no
‘one size fits all’ when it comes to fake news detection. Extensive research
is still required to fully understand the dynamic nature of this problem.

##  3\. Fact Checking

The rapid spread of fraudulent information is a big problem for readers who
fail to determine whether a piece of information is real or fake. Since fake
news is a big threat to society and responsible for spreading confusion, it is
necessary to have an efficient and accurate solution to verify information in
order to secure the global content platform. To address the problem of fake
news, the American media education agency Poynter established the
International Fact-Checking Network (IFCN) in 2015, which is responsible for
observing trends in fact-checking as well as providing training to fact-
checkers. A great deal of effort has already been devoted to providing a
platform where fact-checking organizations around the world can use a uniform
code of principles to prevent the spread of fake news. Two fact-checking
organizations, Snopes and Politifact, developed a fake news detection tool
useful in classifying fake news levels in stages. However, this tool requires
a lot of manual work. There is a profound need for a model that can
automatically detect fake news.

Giovanni et al. reduced the complex manual fact-checking task to a simple
network analysis problem (Ciampaglia et al., 2015), as such problems are easy
to solve computationally. The proposed approach was evaluated by analyzing
tens of thousands of statements related to culture, history, biographical and
geographical information using a public knowledge graph extracted from
Wikipedia. They found that true statements consistently receive higher support
in comparison to false ones and concluded that applying network analytics to
large-scale knowledge repositories provides new strategies for automatic fact-
checking. Below, we examine two facets of fact checking problem. In subsection
3.1 we look into computational approaches to automatic fact checking, whereas
in subsection 3.2, we concentrate on the issue of trust and credibility of the
information and the source providing it.

###  3.1. Towards Automatic Fact Checking

Computational approaches to fact-checking are considered key to tackling the
massive spread of misinformation. These approaches are scalable and effective
in evaluating the accuracy of dubious claims. In addition, they improve the
productivity of human fact-checkers.

One of the proposed approaches is an unsupervised network flow-based approach
(Shiralkar et al., 2017), which helps to ascertain the credibility of a
statement of fact. The statement of fact is available as a set of three
elements that consist of the subject entity, the object entity, and the
relation between them. First, the background information of any real-world
entity is viewed on a knowledge graph as a flow of the network. Then, a
knowledge stream is built by computational fact-checking which shows the
connection between the subject and object of a set. The authors evaluated
network flow model on actual and customized fact data sets and found it to be
quite effective in separating true and false statements.

A study by Baly et al. (Baly et al., 2018) examined on the factuality and bias
of claims across various news media. They collected features from articles of
the target news websites, their URL structures, the web traffic they attract,
their twitter accounts (where applicable) as well as Wikipedia pages. These
features were then used to train the SVM classifier for bias and factuality
separately. The evaluation, showed that the articles’ features achieved the
best performance on factuality and bias, Wikipedia features were somewhat
useful for bias but not for factuality, and Twitter and URL features faired
better in factuality than bias.

A different approach for an automatic fake news detection (Pérez-Rosas et al.,
2017) was based on several exploratory analyses to identify the linguistic
differences between legitimate and fake news. It involved the introduction of
two novel data sets, the first collected using both manual and crowdsource
annotation, and the second generated directly from the web. Based on these,
first several exploratory analyses were performed to identify the linguistic
properties most common for fake news. Secondly, a fake news detector model
based on these extracted linguistic features was built. They concluded that
the proposed system performed better than humans in certain scenarios with
respect to more serious and diverse news sources. However, human beings
outperformed the proposed model in the celebrity domain.

OSNs are also used as a vector for the diffusion of hoaxes. Hoaxes spread
uncontrollably as propagation of such news depends on very active users. At
the same time, news organizations devote a great deal of time and effort to
high-quality fact-checking of information online. Eugenio et al. (Tacchini et
al., 2017) used two classification algorithms: LR and Boolean Crowd Sourcing
(BCS) for classifying Facebook posts as hoaxes or non-hoaxes based on users
who “liked” it. On a data set of 15,500 posts and 909,236 users, they obtained
a classification accuracy of more than 99 percent. The proposed technique even
worked for users who “liked” both hoax and non-hoax posts. Similarly, Kumar et
al. (Kumar et al., 2016) studied the presence of hoaxes in Wikipedia articles
based on a data set consisting of 20K hoaxes explicitly and manually labeled
by Wikipedia editors. According to their findings, hoaxes have very little
impact and can be easily detected. A multi-modal hoax detection system that
merges diverse modalities – the source, text, and the image of a tweet was
proposed by Maigrot et al. (Maigrot et al., 2016). Their findings suggested
that using only source or text modality ensures high performance in comparison
to using all the modalities. Marcella et al. (Tambuscio et al., 2015) focused
on the diffusion of hoaxes on OSNs by considering hoaxes as viruses in which a
normal user, once infected, behaves as a hoax-spreader. The proposed
stochastic epidemic model can be interpreted as a Susceptible-Infected-
Susceptible (SIS) or Susceptible-Infected-Recovered (SIS) model – the infected
user can either be a believer (someone who believes the fake news) or a fact-
checker (checking the news before believing it). The model was implemented and
tested on homogeneous, heterogeneous and real networks. Based on a wide range
of values and topologies, the fact-checking activity was analysed and then a
threshold was defined for fact-checking probability (verifying probability).
This threshold was used to achieve the complete removal of fake news based on
the number of fact-checkers considering the news as fake or real. A study by
Shao et al. focused on the temporal relation between the spread of
misinformation and fact-checking, and the different ways in which both are
shared by users. They proposed Hoaxy (Shao et al., 2016) – a model useful in
the collection, detection, and analysis of this type of misinformation. They
generated a data set by collecting data from both fake news (71 sites,
1,287,768 tweets, 171,035 users and 96,400 URLs) and fact-checking (6 sites,
154,526 tweets, 78,624 users and 11,183 URLs) sources. According to their
results, fact-checking data sharing lags behind misinformation by 10-20 hours.
They suggested that social news observatories could play an important role by
providing the dynamics of real and fake news distribution and the associated
risks.

###  3.2. Trust and credibility

The ease of sharing and discovering information on social media results in a
huge amount of content published for target audiences. Both participants
(those who share and consume) must check the credibility of shared content.
Social media also enables its users to act simultaneously as content producers
and consumers. The content consumer has more flexibility in what content to
follow. For the content producer, it is necessary to check and evaluate the
source of information. If a user is interested in receiving information
regarding a particular topic of interest from a specific source, his primary
task is to check the credibility, relevance, and quality of that source.
Different ways of checking credibility include:

  * •

Looking for other users who have subscribed to such information (Canini et
al., 2011).

  * •

Assessing both the expertise (support and recommendations from other
professionals) (Ericsson et al., 2018; Wang et al., 2015) and user
credibility.

  * •

Assessing the credibility of the sources (examining the content and peer
support) (Rieh and Danielson, 2007).

Researchers have proposed different techniques for identifying credible and
reputable sources of information. Canini et al. (Canini et al., 2011) proposed
an algorithm based on both the content and social status of the user. Weng et
al. merged the web page ranking technique and topic modelling to compute the
rank of a Twitter user (Weng et al., 2010). Cha et al. (Cha et al., 2010)
studied the factors that specify user influence. A random walk approach
(Perozzi et al., 2014) was proposed for separating credible sources from
malicious ones by performing network feature analysis.

TweetCred, a real-time web-based system, was developed to evaluate the
credibility of tweets (Gupta et al., 2014). It assigns a credibility score to
each tweet on a user time line rating from 1 (low credibility) to 7 (high
credibility). The credibility score is then computed using a semi-supervised
ranking algorithm trained on a data set consisting of an extensive set of
features collected from previous work (Pérez-Rosas et al., 2017) and manually
labelled by humans. The TweetCred evaluation was performed based on its
usability, effectiveness, and response time. An 80 percent credibility score
was calculated and displayed within 6 seconds. Additionally, 63 percent of
users either agreed or disagreed with the generated score by 1-2 points.
Irrespective of its effectiveness, the results were still influenced by user
personalization and the context of tweets which did not involve factual
information.

A different research model was developed – based on perceptions related to
news authors, news sharers, and users – to test verification behaviours of
users. (Torres et al., 2018). The aim was to study the validation of content
published by users on Social Networking Sites (SNSs). The results were
assessed using a three-step analysis to evaluate the measurement model,
structural model, and common method bias. It focused on the epistemology of
declarations of interpersonal trust to examine factors that influence user
trust in disseminated news on SNSs. To test the full model, the researchers
used SmartPLS 2.0. The evaluation showed that the variety in social ties on
SNSs increases trust among network participants and trust in the network
reduces news verification behaviours. However, the evaluation disregards the
importance of the nature of news connected with the recipient.

Trust is an important factor to be considered when engaging in social
interaction on social media. When measuring trust between two unknown users,
the challenging task is the discovery of a reliable trust path. In (Ghavipour
and Meybodi, 2018b), Ghavipour et al. addressed the problem of reliable trust
paths by utilizing a heuristic algorithm built on learning automata called
DLATrust. They proposed a new approach for aggregating the trust values from
multiple paths based on a standard collaborative filtering mechanism. The
experiments performed on Advogato – a well-known network data set for trust,
showed the efficiency and high accuracy in predicting the trust of reliable
paths between two indirectly connected users.

Liu and Wu et al. (Shu et al., 2018) studied the correlation between user
profiles and the fake news shared on social media. A real-world data set
comprising social context and news content was built for categorizing users
based on measuring their trust in fake news. Representative groups of both
experienced users (able to differentiate between real and fake news) and naive
users (unable to differentiate between real and fake news) were selected. They
proposed that the features relevant to these users could be useful in
identifying fake news. The results for identified user groups showed that the
distribution satisfies power-law distribution (Clauset et al., 2009) with high
$R^{2}$ scores. This result indicated a significant difference between
features of experienced and naive users. However, the paper left unexplored
the credibility and political bias of experienced users before characterizing
them for fake news detection.

The timely detection of misinformation and sharing of credible information
during emergency situations are of utmost importance. The challenge of
distinguishing useful information from misinformation during these events is
however still significant. Moreover, a lack of know-how about social networks
makes it even more challenging to discern the credibility of shared
information (Webwise, 2019). Antoniadis et al. (Antoniadis et al., 2015)
developed a detection model to identify misinformation and suspicious
behavioural patterns during emergency events on the Twitter platform. The
model was based on a supervised learning technique using the user’s profile
and tweets. The experiments were performed on a data set consisting of 59,660
users and 80,294 tweets. The authors filtered 81 percent of the tweets and
claimed that more than 23 percent were misrepresentations. Although the
proposed technique makes no distinction between intentional and unintentional
information (Balestrucci et al., 2019), it successfully achieved timely
detection.

In Table LABEL:tab:trustandandcredibility, we analyze trust and reputation
models in terms of the mechanism used, data set as well as the outcomes and
weaknesses of each model. In the existing literature, insufficient importance
is given to the sources responsible for spreading the fake news. Evaluating
the source is not straightforward process, as there are multiple variables to
be considered in source verification, such as affiliation and reputation of
the source, expertise in the domain, agreement or disapproval of other sources
etc. Moreover, the absence of a source makes information unreliable,
regardless of whether it is generated by an authentic source or not. Hence,
fake news evaluation requires a model capable of performing source tracking,
verification and validation.

##  4\. Tools and Data Resources

Social media popularity, the availability of the internet, the extreme growth
of user-generated website content, the lack of quality control and poor
governance all provide fertile ground for sharing and spreading false and
unverified information. This has led to continuous deterioration of
information veracity. As the significance of the fake news problem is growing,
the research community is proposing increasingly robust and accurate
solutions. Some of the proposed solutions are discussed below and their
characteristics are provided in Table LABEL:tab:Web-browsing-tools.

  * •

BS-Detector222https://gitlab.com/bs-detector/bs-detector: Available as a
browser extension for both Mozilla and Chrome. It searches for all the links
available on a webpage that are linked to unreliable sources and checks these
links against a manually compiled list of domains. It can classify the domains
as fake news, conspiracy theory, clickbait, extremely biased, satire, proceed
with caution, etc. The BS detector has been downloaded and installed around
about 25,000 times (Griswold, 2016).

  * •

FiB333https://devpost.com/software/fib: The distribution of content is as
important as its creation, FiB takes both post creation as well as
distribution into account. It verifies the authenticity of a post in real time
using AI. The AI uses keyword extraction, image recognition and source
verification to check the authenticity of posts and provide a trust score. In
addition, FiB tries to provide true information for posts that are deemed
false (Figueira and Oliveira, 2017).

  * •

Trusted News add-on444https://trusted-news.com/: Built in conjunction with
MetaCertProtocol powered by the Metacert organization to help users spot
suspicious or fake news. It is used to measure the credibility of website
content and flags content as good, questionable or harmful. It gives a wider
set of outputs, including marking website contents as malicious, satirical,
trustworthy, untrustworthy, biased, clickbait and unknown (Walsh, 2019).

  * •

SurfSafe555https://chrome.google.com/webstore/detail/surfsafe-join-the-
fight-a/hbpagabeiphkfhbboacggckhkkipgdmh?hl=en: There are different ways to
analyze fake news such as textual analysis, image analysis, etc. Ash Bhat and
Rohan Phadte focused on the analysis of fake news using images and generated a
data set which consists of images collected from 100 fact-checking and trusted
new sites. They developed a plug-in that checks the images against a generated
data set. The main idea is to check each new image against the generated image
data set. If the image is used in a fake context or modified, the information
as a whole is considered fake (Clark, 201b).

  * •

BotOrNot: A publicly available service used to assign a classification score
to a Twitter account. This score is assigned to an account on the basis of the
similarity it exhibits to the known characteristics of social bots. This
classification system leverages more than 1,000 features extracted from
contents, interaction patterns and available metadata (Davis et al., 2016).
These features are further grouped into six sub-classes:

    * –

Network features - built by extracting the statistical features for mentions,
retweets, and hashtag co-occurrence.

    * –

User features - based on twitter metadata such as creation time of account,
languages, locations.

    * –

Friend features - dependent on the statistics of social contacts such as
number of followers, posts, followees and so on.

    * –

Temporal features - recording the timing pattern for content generation and
distribution.

    * –

Content features - based on part-of-speech tagging.

    * –

Sentiment features - built by using a sentiment analysis algorithm that takes
into account happiness, emotion scores, etc.

  * •

Decodex666https://chrome.google.com/webstore/detail/decodex/kbpkclapffgmndlaifaaalgkaagkfdod?hl=fr:
An online fake news detection tool that alerts the user to the potential of
fake news by labeling the information as ‘satire’, ‘info’ and ‘no information’
(Giełczyk et al., 2019).

  * •

TrustyTweet777https://peasec.de/2019/trustytweet/: TrustyTweet is a browser
plug-in, proposed for twitter users to assess and increase media literacy. It
shifts the focus from fake news detection by labelling to supporting users to
make their own assessment by providing transparent, neutral and intuitive
hints when dealing with the fake news. TrustyTweet is based on gathering the
potential indicators for fake news, already identified and proven to be
promising in previous studies (Hartwig and Reuter, 2019).

  * •

Fake News Detector888https://github.com/fake-news-detector/fake-news-
detector/tree/master/robinho: The Fake News Detector is an open source project
used for flagging news. A user can flag news as either fake news, extremely
biased or clickbait. The user flagging activity is visible to other fake news
detector users who may flag it again. Once the news is flagged, it is saved in
the repository and accessible to Robhino – an ML robot trained on the inputs
provided by humans that flags news automatically as clickbait, fake news or
extremely biased news.

  * •

Fake News Guard999https://www.eu-startups.com/directory/fake-news-guard/:
Available as a browser extension, it can verify the links displayed on
Facebook or any page visited by the user. There is insufficient information
about the way this tool works, however the key idea is that the “Fake news
guard uses the AI technique along with network analysis and fact-checking”.

  * •

TweetCred101010http://twitdigest.iiitd.edu.in/TweetCred/: A web browser tool
used for assessing the credibility of tweets by using a supervised ranking
algorithm trained on more than 45 features. TweetCred assigns a credibility
score for each tweet on the user time line. Over the course of three months,
TweetCred was installed 1,127 times and computed the credibility score for 5.4
million tweets (Gupta et al., 2014).

  * •

LiT.RL News
Verification111111https://victoriarubin.fims.uwo.ca/2018/12/19/release-for-
the-lit-rl-news-verification-browser-detecting-clickbait-satire-and-falsified-
news/: A research tool that analyses the language used on web pages. The core
functionality of the News Verification browser is textual data analysis using
NLP and automatic classification using a SVM. It automatically detects and
highlights website news as clickbait, satirical fake news and fabricated news
(Rubin et al., 2019).

Detecting fake news on social media poses many challenges as most fake news is
intentionally written. Researcher are considering different information, such
as user behaviour, the engagement content of news, etc. to tackle the problem.
However, there is no data set available that could provide the information on
how fake news propagates, how different users interact with fake news, how to
extract temporal features which could help to detect it and what the impact of
fake news truly is.

In the previous section, we discussed the automatic detection of fake new
using ML models. ML models require high quality data set to be efficient. This
continues to be a major challenge when it comes to social media data due to
it’s unstructured nature, high dimensionality, etc. In order to facilitate
research in this field, a comprehensive guide to existing data sets is
required. Below we present the details for some of the more widely used ones:

  * •

CredBank: Collected by tracking more than 1 billion tweets between October
2014 and February 2015 (Mitra, 2016). It consists of tweets, events, topics
and an associated credibility judgment assigned by humans. The data set
comprises 60 million tweets which are further categorized into 1,049 real-
world events. Further, the data is spread into a streaming tweet file, topic
file, credibility annotation file and searched tweet file (Mitra and Gilbert,
2015).

  * •

LIAR: A publicly available fake news detection data set (Fernandes, 2019) that
can be used for fact-checking. It consists of 12,836 short statements labelled
manually by humans. In order to verify their truthfulness, each statement is
evaluated by the editor of POLITIFACT.COM. Each statement is labelled in any
of the following six categories: true, mostly-true, half-true, barely-true,
false, pants on fire (Wang, 2017).

  * •

Memetracker9: This data set (Leskovec et al., 2009a) recorded social media
activity and online mainstream content over a three-month period. They used a
Spinn3rAPI and collected 90 million documents from 165 million different
websites (Leskovec et al., 2009b). The data set they generated is 350GB in
size. First, they extracted 112 million quotes, which were further refined and
from which 22 million distinct phrases were collected.

  * •

FakeNewsNet121212https://github.com/KaiDMML/FakeNewsNet: A multi-dimensional
data repository consisting of social context, content and spatiotemporal
information (Shu et al., 2020b). The data set was constructed using
FakeNewsTracker, a tool used for collecting, analyzing as well as visualizing
fake news. In the given data set, the content consists of news, articles and
images while context consists of information related to the user, post,
response and network. The spatiotemporal information consists of spatial (user
profile with location, tweets with location) and temporal information
(timestamp for news and responses).

  * •

BuzzFeedNews131313https://www.buzzfeednews.com/article/craigsilverman/partisan-
fb-pages-analysis: This data set, recorded all the news published by 9 news
agencies on Facebook regarding the US election. The articles and news were
fact-checked by journalists from BuzzFeed. It contains 1,627 articles and 826
streams from hyperpartisan Facebook pages which publish misleading and false
information at an alarming rate (News, 2016).

  * •

BS Detector (Vieira, 2017): This data set was collected by using BS Detector,
a web browser extension for both Chrome and Mozilla. It is used to search all
the links linked to unreliable sources on a given web page. These links are
checked across a manually compiled list of domains.

  * •

BuzzFace: This data set (Santia and Williams, 2018) consists of 2,263 news
articles and 1.6 million comments. Buzzface is based on extending the BuzzFeed
data set by adding comments related to Facebook news articles. The news
articles were categorized as “mostly true”,“mixture of true and false”,
“mostly false” and “no factual comments”.

  * •

FacebookHoax: In this data set (Tacchini et al., 2017), facebook graph API is
used for the collection of data. It consists of 15,500 posts, of which 8,923
are hoaxes and the rest are non-hoaxes. These posts were collected from 32
pages: 14 conspiracy and 18 scientific pages. In addition, the data set also
includes the number of likes, which exceeds 2.3 millions.

  * •

Higgs-Twitter: This data set (De Domenico et al., 2013) consists of Twitter
posts related to the discovery of the new Higgs boson particle. The tweets
were collected using the Twitter API. It consists of all the tweets that
contain one of the following hashtags or keywords: cern, higgs, lhc, boson.
The data set consists of 527,496 users and 985,590 analysed tweets of which
632,207 were geo-located tweets.

  * •

Trust and Believe: This data set consists of 50000 Twitter users, all of whom
were politicians (Khan, 2021). For each user, a unique profile is created
containing 19 features. A total of 1000 user was manually annotated, with the
rest being classified using an active learning approach.

Table LABEL:tab:dataset presents a detailed summary of the available data sets
used for fake news detection in existing literature. Most are either small in
size or contain mainly uni-modal data. The existing multi-modal data sets,
unfortunately, still can’t be used as a benchmark for training and testing
models for fake news detection (Jindal et al., 2019). The next step is to
generate large and comprehensive data sets that would include resources from
which all relevant information could be extracted.

##  5\. Discussion and Challenges

Solving the problem of fake news detection and minimizing their impact on
society is one of the important issues being considered in the research
community. In this review, we analysed different studies using varying methods
for detecting fake news. With the aim of aiding future research we provide
their classification based on the social media platform used in Table
LABEL:tab:classificationofstudies.

Table 1. Classification of the Studies Surveyed based on the Platform Used – Facebook and Twitter Platform |  Research Papers
---|---
Twitter |  Grier et al. (Grier et al., 2010),Ye et al. (Ye and Wu, 2010), Chengcheng et al. (Shao et al., 2016), Soroush et al. (Vosoughi et al., 2018), Liang Wu and Huan liu (Wu and Liu, 2018), Hartwig et al. (Hartwig and Reuter, 2019), Davis et al. (Davis et al., 2016), Cha et al. (Cha et al., 2010), Weng et al. (Weng et al., 2010), Canini et al. (Canini et al., 2011), Thomas Kurt (Thomas, 2013), Holton et al. (Holton and Lewis, 2011), Antoniadis et al. (Antoniadis et al., 2015), Alessandro et al. (Balestrucci et al., 2019), Zhao et al. (Zhao et al., 2015), Gupta et al. (Gupta et al., 2014), Khan and Michalas (Khan and Michalas, 2020)
Facebook |  Tambuscio et al. (Tambuscio et al., 2015), Joon Ian Wong (Wong, 2016), Monther et al. (Aldwairi and Alwahedi, 2018), Potthast et al. (Potthast et al., 2017), Alexey Grigorev (Grigorev, 2017), Fake News Guard141414https://www.eu-startups.com/directory/fake-news-guard/, BuzzFace (Santia and Williams, 2018), FacebookHoax (Tacchini et al., 2017; Shu et al., 2020b), Sebastian et al. (Tschiatschholek et al., 2018), Detective (Leskovec and Mcauley, 2012)

Similarly, a study of the current literature on false news identification can
be divided into four paradigms: hybrid approach, feature-based, network
propagation and knowledge-based. The hybrid approaches employ both human and
ML approaches for the detection of fake news. In the feature-based method,
multiple features associated with a specific social media account are used to
detect fake news. This paradigm can further be divided into three sub-
categories – account-based, context and content-based and Text categorization.
These methods are explicitly discussed in section 2. The third paradigm,
network propagation, describes the potential methods for discovering, flagging
and stopping the propagation of fake news in its infancy. The final paradigm
entails supplementing AI models with human expert knowledge for decision-
making (see section 2). An overview of these paradigms is given in Figure 1.

for tree= grow=east, parent anchor=south east, child anchor=south west,
anchor=south, align=center, l sep+=2.5pt, s sep+=-5pt, inner sep=0pt, outer
sep=0pt, edge path= [draw, rounded corners=5pt, edge] (!u.parent anchor)
[out=0, in=180] to (.child anchor)edge label – (.south east);, for
root=ellipse,draw,parent anchor=east,, [Fake News
Detection [Knowledge
Based [Info
Retrieval [(Ferreira and Vlachos, 2016; Baly et al., 2018; Holton and Lewis,
2011; Gupta et al., 2014)]] [Semantics [(Ciampaglia et al., 2015; Tambuscio et
al., 2015; Shao et al., 2016; Shiralkar et al., 2017)]]] [, calign with
current edge [Network
Propagation [Link
Based[(Lee et al., 2011; Aldwairi and Alwahedi, 2018; Shiralkar et al.,
2017)]] [Neighbor
Based[(Hu et al., 2013; Rayana and Akoglu, 2015; Li et al., 2016)]] [Group
Based[(Jindal and Liu, 2007; Gao et al., 2010; Akoglu et al., 2015; Wu et al.,
2017a; Wu and Liu, 2018)]]]] [Feature
Based [Text
Categorization [(Potthast et al., 2017; Pérez-Rosas et al., 2017; Tacchini et
al., 2017; Wu et al., 2017a; O’Brien et al., 2018)]] [Context and
Content Based [(Zhao et al., 2015; Ruchansky et al., 2017; Wang, 2017, 2010)]]
[Account
Based [(Gilani et al., 2017; Cresci et al., 2017; Edwards et al., 2014; Lee et
al., 2011; Wu et al., 2013; Chu et al., 2010, 2012; Erşahin et al., 2017;
gilani, 2018; Giachanou and Ghanem, 2019)]]] [Hybrid
Approach [(Liu and Wu, 2018; Sean Baird, 2017; Hanselowski, 2017; Pham, 2019;
Liu and Wu, 2020; Wang, 2017; Yang et al., 2019; Khan and Michalas, 2020)]]]

Figure 1. Classification of the Existing Literature Based on Four Paradigms –
Hybrid Approach, Feature Based, Network Propagation, and Knowledge Based

Identifying and mitigating the spread of fake news and its variants presents a
set of unique challenges. Fake news dissemination is a part of coordinated
campaigns targeting a specific audience with the aim of generating a plausible
impact on either local or global level. Many companies as well as entire
countries were faced with the need to start building mechanisms to protect
citizens from fake news. In September 2019, Facebook announced it was
contributing $10 million to a fund to improve deepfake detection technologies
while several governments have taken different initiatives to defeat this
problem (Funke Daniel, 2019; Rusu and Herman, 2019; The Law Library of
Congress, 2019). Educational institutions and non-profit organizations have
also tried to mitigate the problem through advocacy and literacy campaigns.
Specifically, these institutions in collaboration with technology companies
have designed various techniques for detecting, flagging, and reporting fake
news (Carlson, 2017; Northman, 2019; Sardarizadeh, 2019; Read, 2019).

Table LABEL:tab:Approaches summarizes the actions that have been taken by
governments around the world in order to battle the spread of fake news.

Table 2. Approaches Taken by Governments to Tackle the Problem of Fake News Country |  Focus |  Approach/Action
---|---|---
Argentina |  Fact-checking resources for public |  – Commission created to verify fake news during national election campaign;
– Imposing sanctions for spreading fake news.
Sweden |  Foreign disinformation campaign |  – Media broadcasts and publications are governed by law;
– Educating citizens
Canada |  Foreign disinformation campaign |  – No specific law developed to prohibit the spread of fake news. Laws related to the criminal code or broadcasting distribution regulation may be relevant to spreading fake news.
China |  Election misinformation |  – Spreading fake news is a crime under China’s criminal law;
– Imposition of a fine and imprisonment;
– Reliable information is published to systematically rebut fake news
Egypt |  Media regulation |  – Three domestic laws have been passed to regulate information distribution and its accuracy;
– Imposing sanctions for spreading fake news
France |  Election misinformation |  – No specific law but there is general legislation against fake news;
– Imposing sanctions for spreading fake news
Germany |  Hate speech |  – A number of civil and criminal laws exist for fake news;
– Network enforcement act specific for fighting fake news
Israel |  Foreign disinformation campaign |  – High-level committee appointed by the president to examine the current law for threats and find ways to address them;
– Imposing sanctions for spreading fake news
Japan |  Media regulation |  – A law exists to counter fake news;
– Ministry of Communication and Internal Affairs work jointly to counter fake
news
Kenya |  Election misinformation |  – Computer misuse and cyber-crime act has been passed, not yet in force;
– Educating citizens
Malaysia |  Election misinformation |  – Malaysian anti-fake News Act 2018;
– A fact-checking portal is operated by government agencies;
– Imposing sanctions for spreading fake news
Nicaragua |  Media regulation |  – No specific law available, however some provisions can be found within the penal code and election law
Russia |  Election misinformation |  – Passed legislation that addresses the spread of fake news;
– Imposing sanctions for spreading fake news
Brazil |  Election misinformation |  – No law but the topic is under discussion in congress;
– Fines and imprisonment
United Kingdom |  Foreign disinformation campaign |  – No legislation to scrutinize or validate news on social media;
– Reliable information is published to systematically rebut fake news
United Arab Emirates |  Election misinformation |  – Sharing misinformation is a crime by law;
– Imposition of a fine
United States |  disinformation, misinformation |  – Proposed a federal law;
– State media literacy initiatives

The greatest obstacle in fake news detection is that the information spreads
through social media platforms like forest fire (especially if it’s
polarizing) which when not addressed, becomes viral in a matter of
milliseconds (Stahl, 2018). The implications of this instantaneous consumption
of information, on the other hand, are long-lasting. As a result, fake news
becomes indistinguishable from real information, and the ongoing trends are
difficult to recognize. We believe that fake news propagation can only be
successfully controlled through early detection (see section 2.4). Another
significant problem is that the rise in the influence of social media is
closely connected to the increase in the number of users. According to Figure
2, there are currently more than 3 billion users and by 2024 this number is
expected to exceed 4 billion, a development that will eventually lead to an
exponential rise in data (Tankovska, 2021). This data is most likely to be
potentially uncertain due to inconsistencies, incompleteness, noise and
unstructured nature. This complexity increases the velocity, variety, and
amount of data and will most probably jeopardize the legitimacy of the results
of any standard analytic processes and decisions that would be based on them.
Analysis of such data requires tailor-made advanced analytical mechanisms.
Designing techniques that could efficiently predict or evaluate future courses
of action with high precision thus remains very challenging.

Figure 2. Number of Social Users

To summarize, humans are susceptible to becoming victims of false information
due to their intrinsic way of processing and interpreting information being
influenced by cognitive biases – namely, by the Truth Bias, Naive Realism and
Confirmation Bias (Stahl, 2018). Consequently, all fake information floating
around can lead to false information which is capable of ruining the “balance
of news ecosystem”. The main challenge is that most users do not pay more
attention to the manipulated information, while those who are manipulating it
are systematically trying to create more confusion. The outcome of this
process is that the people’s ability to decipher real from false information
is further impeded (Shu et al., 2017; Rubin, 2017).

Can we stop the viral spread?, the answer obviously is Not yet and it is
because of the critical challenges surrounding the detection of fake news (see
Figure 3). Several efforts, however, have been put in place to help limit it
such as media literacy. Media literacy comprised of practices that enable
people to access and critically evaluate content across different media seems
like the only valid solution. Although this is, and always was a challenging
task, a coherent understanding, proper education, training, awareness and
responsible media engagement could change this (Bulger and Davison, 2018). In
the mean time, resisting disinformation and “fake news” culture should be
promoted and encouraged. In addition, cross-disciplinary collaboration (i.e.,
social psychology, political science, sociology, communication studies etc.)
can help and streamline findings across diverse disciplines to devise a
holistic approach for understanding the media environment structure and how it
operates.

Figure 3. Future Challenges

##  6\. Conclusion

Today, OSNs can be seen as platforms where people from all over the world can
instantly communicate with strangers and even influence people’s actions.
Social media has shaped the digital world to an extent that they now seem like
an indispensable part of our daily lives. However, social networks’ ease of
use has also revolutionized the generation and distribution of fake news. This
prevailing trend has had a significant impact on our societies.

In this survey paper, we studied the problem of fake news detection from two
different perspectives. Firstly, to assist users in identifying who they are
interacting with, we looked at different approaches in existing literature
used for the identification and classification of user accounts. To this end,
we analysed in depth both the users’ context (anyone) and content (anything).
For the early identification and mitigation of fake news, we studied different
approaches that focus on data network features. Recently proposed approaches
for measuring the relevance, credibility, and quality of sources were analysed
in detail.

Secondly, we approached the problem of automating fake news detection by
elaborating on the top three approaches used during fake news detection
competitions and looked at the characteristics of more robust and accurate
web-browsing tools. We also examined the statistical outputs, advantages, and
disadvantages of some of the publicly available data sets. As the detection
and prevention of fake news presents specific challenges, our conclusion
identified potential challenges and promising research directions.

## ACKNOWLEDGEMENT

This research has received funding from the EU research projects ASCLEPIOS
(No. 826093) and CYBELE (No 825355).

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