ArticlePDF Available

Abstract and Figures

The proliferation of false information on social networks is one of the hardest challenges in today's society, with implications capable of changing users perception on what is a fact or rumor. Due to its complexity, there has been an overwhelming number of contributions from the research community like the analysis of specific events where rumors are spread, analysis of the propagation of false content on the network, or machine learning algorithms to distinguish what is a fact and what is "fake news". In this paper, we identify and summarize some of the most prevalent works on the different categories studied. Finally, we also discuss the methods applied to deceive users and what are the next main challenges of this area.
Content may be subject to copyright.
A Brief Overview on the Strategies
to Fight Back the Spread
of False Information
Álvaro Figueira1, Nuno Guimaraes1and Luis Torgo2
1CRACS-INESCTEC and University of Porto, Porto, Portugal
2Faculty of Computer Science, Dalhousie University,
Halifax, Nova Scotia, Canada
Received 09 January 2019;
Accepted 07 June 2019
The proliferation of false information on social networks is one of
the hardest challenges in today’s society, with implications capable
of changing users perception on what is a fact or rumor. Due to its
complexity, there has been an overwhelming number of contributions
from the research community like the analysis of specific events where
rumors are spread, analysis of the propagation of false content on the
network, or machine learning algorithms to distinguish what is a fact
and what is “fake news”. In this paper, we identify and summarize some
of the most prevalent works on the different categories studied. Finally,
we also discuss the methods applied to deceive users and what are the
next main challenges of this area.
Keywords: false information, social networks.
Journal of Web Engineering, Vol. 18 4-6, 319–352.
doi: 10.13052/jwe1540-9589.18463
2019 River Publishers
320 Á. Figueira et al.
1 Introduction
The large increase of social media users in the past few years has led to
an overwhelming quantity of information available in daily (or even
hourly) basis. In addition, the easy accessibility to these platforms
whether it’s by a computer, tablet or mobile, allows the consumption
of information at a distance of a click. Therefore, traditional and
independent news media urge to adopt social media to reach a broader
audience and gain new clients/consumers.
The ease of creating and disseminating content in social net-
works like Twitter and Facebook has contributed to the emergence
of malicious users. In particular, users that infect the network with
the propagation of misinformation or rumors. These actions combined
with the fact that 67% of adults consume some type of news in social
media (20% on a frequent basis) [24] have already caused real-world
consequences [56].
However, unreliable content or, how it is now referred – “fake
news” –, is not a recent problem. Although the term gained popularity in
the 2016 US presidential election, throughout the years newspapers and
televisions have shared false content resulting in severe consequences
for the real world. For example, in 1924 a forged document known
as “The Zinoviev Letter” was published on a well known British
newspaper four days before the general elections. The goal was to
destabilize the elections in favour of the conservative party with a
directive from Moscow to British communists referring an Anglo-
Soviet treaty and inspiring “agitation-propaganda” in the armed forces
[39]. Another example happened after the “Hillsborough accident”,
where 96 people died crushed due to overcrowding and lack of security.
Reports from an illustrious newspaper claimed that, as people were
dying, some fellow drunk supporters stole from them and beat police
officers that were trying to help. Later, however, such claims were
proven false [15].
The verified impact of fake news in society throughout the years
and the influence that social networks currently have today forced high
reputation companies, such as Google and Facebook, to start working
on a method to mitigate the problem [28, 29]. The scientific community
has also been increasing the activity on the topic. In fact, if we search
A Brief Overview on the Strategies to Fight Back the Spread 321
Figure 1 Number of hits per year in Google Scholar for the term “fake news”.
in Google Scholar1for “fake news”, we will find a significantly high
number of results that have an increase of 7K publications, when
compared with the number obtained in the previous year. Figure 1
illustrates the growth of publication regarding the term “fake news”
over the years.
Nevertheless, the problem of fake news is still a reality since the
solution is anything but trivial. Moreover, research on the detection of
such content, in the context of social networks, is still recent. Therefore,
in this work we attempt to summarize the different and most promising
branches of the problem as well as the preliminary proposed solutions
in the current literature.
In addition, we present a perspective on the next steps in the research
with a focus on the need to evaluate the current systems/proposals in a
real-world environment.
In the next section, we will present a review of the state of the art on
the problem of unreliable information namely the different approaches
that are being studied as well as the different kinds of data that is being
used. In Section 3 we discuss the problem of unreliable information.
Next, we introduce some feature guidelines for future research on
322 Á. Figueira et al.
unreliable content by pointing some limitations on the current solutions
proposed in Section 2. Finally, in Section 5, we present the main
conclusions of this work.
2 Literature Review
From many well-known examples in the last years, we know that users
share on social media first-hand reports (accounts) of large-dimension
on-going events, like natural disasters, public gatherings and debates,
active shootings, etc.
However, in some occasions not all of what is reported is real.
Twitter started to help their users by determining who “is credible” by
adding a verified account indicator, which confirms if that account “of
public interest” is authentic [27].
Nevertheless, even though the accounts for most genuine users are
not verified, many of their social media posts may still be factual.
Identifying which users are not credible is thus an important and on-
going challenging problem. For example, ‘Bots’are often used to spread
spam or to rapidly create connections to other users. Bots make it
possible to gain followers or “friends” in order to make the user appear
more popular or influential [19]. Moreover, they can also flood social
media with fake posts to influence the public opinion.
Misinformation (false information) is information that may or may
not have been debunked at the time of sharing while rumors are pieces
of information which are unverified at the time of sharing and seem to
be relevant to a given situation. According to [34], rumors’purpose is
to make sense and manage risk situations when there is ambiguity or
a potential threat. Curiously, a very common form of disinformation is
the dissemination of a “rumor” disguised as a fact.
Misinformation is also conveyed in ‘politics’in the form of “Astro-
turf campaigns”, a collective action where several different accounts
spread and promote a false message during a political campaign.
Interestingly, most of the times these actions are planned to carefully
shift the public opinion [47].
Another form of creating information threats is by the creation of
“sybils” to gain importance or authority in the spread of misinformation.
A Brief Overview on the Strategies to Fight Back the Spread 323
Sybils are similar accounts, in the sense of having a very similar account
name, with the intention to fool users by making them believe the
account is from some well-known person, friend or entity. Generally,
the fake account tries to connect with the friends of the real user/entity. If
it is granted then the Sybil account can take advantage of the real user’s
reputation to more easily spread disinformation. In most cases, once a
Sybil account is created, hackers may utilize a set of bots to publish
fake messages on social media and reach a much wider, eventually
focussed/specific, audience [50].
Another form of misleading information is ‘hashtag hijacking’,
which occurs when a set of people use common hashtags in a context
that substantially differs from the original intent [64]. Notice that on
social media, astroturfing and hashtag-hijacking are the main modes of
drifting the public opinion [31].
There are several approaches to tackle the problem of unreliable
content on social media. For example, some authors opt by analyzing
the patterns of propagation [35, 49, 60], others by creating supervised
systems to classify unreliable content [5, 42, 59], and others by focusing
on the characteristics of the accounts that share this type of content
[7, 9, 17]. In addition, some works also focus on developing techniques
for fact-checking claims [12, 52] or focus on specific case studies
[2, 13, 26, 57].
In the following subsections, we present a narrative literature
review on the current state of the art. This literature review relies
on previous knowledge of the authors on the distinct sub-areas of
unreliable information on social media. This knowledge was com-
plemented with searches in two different academic search engines:
Google Scholar2and Semantic Scholar3. The queries used in both
engines included the key terms “fake news”, “misinformation online”,
“false information” and were combined with the terms “analysis”,
“detection” and “propagation”. Only computer science papers were
considered. Literature reviews were excluded from the process. We
relied on the search engines to provide the most relevant papers.
However, some selection was made to balance the different sub-areas.
324 Á. Figueira et al.
Figure 2 Structure of the state of the art on unreliable content.
This selection was first based on the publishing date (more recent papers
were prioritized) and second on the number of citations. In the following
subsections, we will provide further detail on the approaches previously
mentioned (illustrated hierarchically in 2) and the selected papers for
these approaches.
2.1 Unreliable Accounts Detection
Castillo et al. [9] target the detection of credibility in Twitter events.
The authors created a dataset of tweets regarding specific trending
topics. Then, using a crowd sourcing approach, they annotated the
dataset regarding the credibility of each tweet. Finally, they used
four different sets of features (Message, User, Topic, and Propaga-
tion) on a Decision Tree Model that achieved an accuracy of 86%
from a balanced dataset. A more recent work [17] used an Entropy
Minimization-Discretization technique that combines numerical fea-
tures with assessing fake accounts on Twitter.
Benevuto et al. [7] developed a model to detect spammers by
building a manual annotated dataset of 1K records of spam and non-
spam accounts. Then, they extracted attributes regarding content and
user behaviour. The system was capable of detecting correctly 70% of
the spam accounts and 96% of non-spam.
A Brief Overview on the Strategies to Fight Back the Spread 325
A similar problem is the detection of bot accounts. Chu et al. [11]
distinguished accounts into three different groups: humans, bots and
cyborgs. Using a human-labelled dataset of 6K users, they built a
system with four distinct areas of analysis (entropy measures, spam
detection, account properties, and decision making). The performance
of the system was evaluated using accuracy, which reached 96% in the
“Human” class. Another similar work [16], introduced a methodology
to differentiate accounts into two classes: humans and bots.
Gilani et al. [22] presented a solution to a similar goal: to distin-
guish automated accounts from human ones. However, they introduced
the notion that “automated” is not necessarily bad. Using a dataset
containing a large quantity of user accounts, the authors divided and
categorized each entry into 4 different groups regarding the popularity
(followers) of the account. The evaluation was conducted using the
F1-measure. The results obtained fall between 77% and 91%.
Therefore, regarding the analysis of social media accounts, the
majority of the state of the art has been focusing on trying to identify
bot or spammer accounts. However not all users that spread unreliable
accounts are bots or spammers. Thus, it would be important to analyze
those type of accounts also (i.e. human operated accounts that spread
unreliable content on social networks). However such studies, to best
of our knowledge, are still in a very early stage.
2.2 Content Analysis
Another major area of study is the analysis of large quantities of fake
news spread through social networks. Vosoughi et al. [66] presented a
study of the differences between propagation of true and false news.
The work focused on the retweet propagation of false, true, and mixed
news stories for a period of 11 years. The findings were several.
First, false news stories peaks were at the end of 2013, 2015 and
2016. Then, through the analysis of retweets of false news stories, the
authors concluded that falsehood reaches a significantly larger audience
than the truthful. In addition, tweets containing false news stories are
spread by users with fewer followers and friends, and that are less
active than users who spread true news stories. Another work [65]
326 Á. Figueira et al.
studied the agenda-setting relationships between online news media,
fake news, and fact checkers. In other words, if each type of content
is influenced by the agenda of others. The authors found out that
certain issues were transferred to news media due to fake news (more
frequently in fake stories about international relations). Furthermore,
fake news also predicted the issue agenda of partisan media (more in
the liberal side than the conservative). Other relevant findings are the
reactive approach of fake news media to traditional and emerging media
and the autonomy of fact-checking websites regarding online media
2.3 Case Studies
Some works focus on analyzing the dissemination of false information
regarding a particular event. One of those is related to the Boston
Marathon in 2013, where two homemade bombs were detonated near
the finish of the race [13]. For example, in [26] the authors performed
an analysis on 7.9 million tweets regarding the bombing. The main
conclusions were that 20% of the tweets were true facts whether 29%
were false information (the remaining were opinions or comments),
it was possible to predict the virality of fake content based on the
attributes of the users that disseminate it, and accounts created with the
sole purpose of disseminating fake content often opt by names similar
with official accounts or names that explore the sympathy of people (by
using words like “pray” or “victim”). Another work has the analysis
focused on the the main rumors spread on Twitter after the bombings
occurred [57].
A different event tackled was the US Presidential Election in 2016.
For example, the authors in [2] combined online surveys with informa-
tion extracted from fact-checking websites to perceive the impact of
fake news in social media and how it influenced the elections. Findings
suggest that articles containing fake news pro-Trump were shared three
times more than articles pro-Clinton and the average American adult has
seen at least one fake news stories on the month around the election.
Another work [8] studied the influence of fake news and well know
news outlets on Twitter during the election. The authors collected
approximately 171 million tweets in the 5 months prior to the elections
A Brief Overview on the Strategies to Fight Back the Spread 327
and showed that bots diffusing unreliable news are more active than
the ones spreading other types of news (similar to what was found in
[2]). In addition, the network diffusing fake and extreme bias news is
denser than the network diffusing center and left-leaning news. Other
works regarding this event are presented in [33, 51].
Other works address similar events such as Hurricane Sandy [5],
the Fukushima Disaster [63] and the Mumbai Blasts in 2011 [25].
2.4 Network Propagation
In Shao [49] the authors expose a method describing the extraction of
posts that contained links to fake news and fact-checking web pages.
Then, they analyzed the popularity and patterns of the activity of the
users that published these type of posts. The authors concluded that
users that propagate fake news are much more active on social media
than users that refute the claims (by spreading fact-checking links). The
authors’ findings also suggest that there is a small set of accounts that
generate large quantities of fake news in posts.
Another work by Tambuscio et al. [60] describes the relations
between fake news believers and fact-checkers. The study resorts to
a model commonly used in the analysis of disease spreading, modyfing
it and treating misinformation as a virus. Nodes on the network can
be believers of fake news, fact-checkers or susceptible (neutral) users.
Susceptible nodes can be infected by fake news believers although they
can “recover” when confronted with fact-checking nodes. By testing
their approach in 3 different networks, the authors concluded that fact-
checking can actually cancel a hoax even for users that believe, with a
high probability, in the message.
A similar approach is proposed in [35] where a Dynamic Linear
Model is developed to timely limit the propagation of misinformation.
The model differs from other works since it relies on the ability for
the user’s susceptibility to change over time and how it affects its
dissemination of information. The model categorizes users in 3 groups:
infected, protected and inactive, and validates the effectiveness of the
approach on a real-world dataset.
328 Á. Figueira et al.
2.5 Unreliable Content Detection
A work by Antoniadis et al. [5] tried to identify misinformation on
Twitter. The authors annotated a large dataset of tweets and developed
a model using the features from the Twitter text, the users, and the social
feedback it got (number of retweets, number of favourites, number of
replies). Finally, they assessed the capability of the model in detecting
misinformation in real time, i.e. in a priori way (when the social
feedback is not yet available). Evaluations on real-time only decay
in 3% when compared with the model that uses all available features.
An approach also using social feedback was presented by Tacchini et al.
[59]. The authors claim that by analyzing the users who liked a small
set of posts containing false and true information, they can obtain a
model with an accuracy near 80%.
Perez-Rosas [42] created a crowd-sourced fake news dataset in
addition to fake news available online. The dataset was built based on
real news. In other words, crowd-source workers were provided with
a real news story and were asked to write a similar one, but false.
Furthermore, they were asked to simulate journalistic writing. The best
model obtained a 78% accuracy in the crowd-sourced dataset and only
less 5% in a dataset obtained by fake news on the web.
Another example is the work of Potthast [45] which analyses the
writing style of hyper-partisan (extremely biased) news. The authors
adopt a meta-learning approach (“unmasking”) from the authorship
verification problem. The results obtained show models capable of
reaching 78% in F1-measure in the task of classifying hyper-partisan
and mainstream news, and 81% in distinguishing satire from the hyper-
partisan and mainstream news. However, we must note that using only
style-based features does not seem to be enough to distinguish fake
news since the authors’ best result was 46%.
2.6 Fake News Detection Systems
The majority of the implementations to detect fake news comes in
the form of a browser add-on. For example, the bs-detector [62] flags
content in social media in different categories such as clickbait, bias,
A Brief Overview on the Strategies to Fight Back the Spread 329
conspiracy theory and junk science. To make this evaluation, the add-
on uses OpenSources [40] which is a curated list of dubious websites.
A more advanced example is the Fake News Detector [10].This add-on
uses machine learning techniques in a ground truth dataset combined
with the “wisdom of the crowd” to be constantly learning and improving
the detection of fake news. An interesting system that also took the
shape of an add-on was the one developed by four colleges students
during a hackathon at Princeton University [4]. Their methodology
combined two different approaches: the first makes a real-time analysis
of the content in user’s feed. The other notifies the user when they are
posting or sharing doubtful content. The system is capable of analyzing
keywords, recognizes images and verified sources to accurately detect
fake content online. With confidence we can say that new systems are
being created with a frequency of more than a dozen a year. Most of
them uses the add-on approach, but many are not yet suited to be usable
by the normal people as they are clearly proof of concept prototypes.
2.7 Fact-Checking
Another way to tackle the problem of false information is through fact-
checking. Due to the enormous quantity of information spread through
social networks, the necessity to automatize this task has become
crucial. Automated fact-checking aims to verify claims automatically
through consultations and extraction of data from different sources.
Then, based on the strength and stance of reputable sources regarding
the claim, a classification is assigned [14]. This methodology, despite
being in development is very promising.
2.7.1 Stance Detection
In earlier research, stance detection has been defined as the task of a
given fragment of text agrees, disagrees or is unrelated to a specific
target topic. However, in the context of fake news detection, stance
detection has been adopted as a primary step to detect the veracity of a
news piece. Simply putting it, to determine the veracity of a news article,
one can look to what well-reputed news organizations are writing about
that topic. Therefore, stance detection can be applied to understand
330 Á. Figueira et al.
if a news written from an unknown reputation source is agreeing or
disagreeing with the majority of the media outlets. A conceptually
similar task to stance detection is textual entailment [43, 53]
The Fake News Challenge4promotes the identification of fake
news through the used of stance detection. More specifically, given
a headline and a body of text (not necessarily from different articles),
the task consists in identifying if the body of text agrees, disagrees,
discusses or its unrelated with the headline. Several approaches were
presented using the dataset provided. The authors in [38] present several
approaches using a conditioned bidirectional LSTM (Long Short Term
Memory) and the baseline model (GradientBoosted Classifier provided
by the authors of the challenge) with an additional variation of features.
As for the features, Bag of Words and GloVe vectors were used. In
addition, global features like binary co-occurrence in words from the
headline and the text, polarity words and word grams were used. The
best result achieved was using bidirectional LSTM with the inclusion of
the global features mentioned. The improvement regarding the baseline
was 9.7%. Other works with similar approaches were proposed [43, 53]
however, results do not vary significantly.
Stance detection is an important step towards the problem of fake
news detection. The fake news challenge seems to be a good starting
point to test possible approaches to the problem. Furthermore, the
addition of source reputation regarding topics (p.e. politics) can provide
useful insight to detect the veracity of a news.
2.7.2 Fact-checking as a Network Problem
The authors in [12] tackle fact-checking as a network problem. By
using the Wikipedia infoboxes to extract facts in a structured way, the
authors proposed an automatic fact-checking system which relies on the
path length and specificity of the terms of the claim in the Wikipedia
Knowledge Graph. The evaluation is conducted in statements (both
true and false) from the entertainment history and geography domains
(for example “xwas marry to y”, “ddirected f” and “cis the capital
of r”) and an independent corpus with novel statements annotated
A Brief Overview on the Strategies to Fight Back the Spread 331
by human raters. The results of the first evaluation showed that true
statements have higher truth values than false. In the second evaluation,
the values from human annotators and the ones predicted by the system
are correlated.
Another work by the same authors [52] use an unsupervised
approach to the problem. The Knowledge Stream methodology adapts
the Knowledge Network to a flow network since multiple paths may
provide more context than a single path and reusing edges and limiting
the paths where they can participate may limit the path search space.
This technique, when evaluated in multiple datasets, achieves results
similar to the state of the art. However, in various cases, it provides
additional evidence to support the fact-checking of claims.
2.8 Datasets For Unreliable News Analysis
One key feature to develop reliable systems capable of detecting false
information (and the accounts that propagate it) is the quality of the
datasets used. In this section, we present some examples of datasets
along with their characteristics.
2.8.1 OpenSources
OpenSources [40], as previously mentioned, is a curated dataset of
websites that publish different types of unreliable content. Each source
is analyzed by experts considering a strict methodology that includes
the title/domain analysis, the identification of the owners of the site
(through the “about us” section), verification of several articles (by
source quotation, or studies to back up the claims made) and verification
of writing style, aesthetic and social media presence (i.e. if the posts
from the sources’ social media page are propitious to clickbait). Each
website is characterized to a limit of three different labels. This labels
are fake, satire, bias, conspiracy, rumor, state (sources operating under
governmental control), junksci (also known as junk science), hate,
clickbait, political or reliable.
2.8.2 Fake News Corpus
The “Fake News Corpus” dataset [58] contains approximately 9.5M
news articles extracted from the source available in OpenSources
332 Á. Figueira et al.
combined with the New York Times and Webhose English News
Article articles datasets. The main reason is to balance the data, since
OpenSources does not have a significant number of “reliable” news.
The number of different domains included is 745 and the labels for
each article correspond to its source’s primary label in the OpenSources
2.8.3 Fake News Challenge Dataset
Although this dataset does not have annotations normally associated
with fake news datasets, it was used for the Fake News Challenge
Competition (mentioned in Section 2.7.1). The dataset has two different
types of files. The first is composed of two fields, the id and corpus
(body) of news. The second includes a news headline, a body id from
the first file and a label regarding the stance of the headline (i.e. if it
is related to the body). It is important to mention that each headline
has multiple labels for different news corpus. The number of body ids
for each headline is dynamic, ranging from 2 to 127. The main goal of
this dataset was to create systems that were able to identify if a body
agrees, disagrees, discusses or is unrelated regarding the headline. This
methodology can be extrapolated to identify if a dubious piece of news
is in accordance with what well establish news medium are publishing
about the topic.
This dataset is an extension on the work of Ferreira et al. [20] and
their Emergent Dataset which is described in the next subsection.
2.8.4 Emergent Dataset
The Emergent dataset [20] consists of rumors (in the format of claims)
extracted by journalists from multiple sources. These claims are then
manually linked to multiple news articles where each news article is
labelled by journalists regarding their stance on the original claim.
Finally, after multiple articles are collected, a veracity label is assigned.
Three possible labels are possible for each news headlines: “for”
(when the article is in accordance with the claim), “against” (when
the article’s statement is opposed to the claim) and “observing” (when
no assessment of veracity is made by the article). The veracity label is
initially set to “unverified”. However, with the aggregation of articles
A Brief Overview on the Strategies to Fight Back the Spread 333
regarding a claim this label is converted to “true”, “false” or remains
“unverified” if no evidence is found.
2.8.5 Kaggle Dataset
This dataset [48] was originally posted in Kaggle5, a platform for
machine learning and data scientists enthusiasts, where datasets are
published and machine learning competitions are held. The dataset
uses OpenSources and each post is assigned their sources’ label. The
dataset contains approximately 13 000 posts from 244 different sources.
Although this dataset follows the same methodology of the Fake News
Corpus dataset, it adds a new label (bs) and a spam score provided by the
bs-detector [62] application as well as some social feedback provided
by the Webhose API6.
2.8.6 Baly et al. Dataset
This dataset was used in the work of Baly et al. [6] and it was extracted
according to the information available in MediaBiasFactCheck.com7.
The labels provided on this website are manually annotated in two
different categories. First, each news source is annotated according to
its bias in a 7-point scale (“extreme-left”,“left”,“center-left”, “center”,
“center-right”, “right”, “extreme-right”). Then, each source veracity
is analyzed and categorized in one of three different labels (“low”,
“mixed”, “high”). The dataset was built crawling the website and
retrieving approximately 1050 annotated websites.
2.8.7 BuzzFeed Dataset
On August 8, 2017, BuzzFeed published a study referring to the partisan
websites and Facebook pages in US politics and how they are prolifer-
ating after 2016 US presidential elections [55]. The dataset used for the
analysis includes a collection of partisan Facebook pages and websites.
These pages present elements of extremely biased opinions from the
left and right political side. Although not all news stories included
on the sources are false, they have an extremely biased opinion and
334 Á. Figueira et al.
were aggressively promoted and spread on Facebook. In addition to the
Facebook page name and website, this dataset also includes information
about the registered date of the websites and if these websites were
linked to others (using Google Analytics and Google AdSense). In
addition, engagement metrics regarding the difference Facebook pages
(likes, reactions, shares and comments) is also presented. Each website
is classified as “right” or “left” according to its political tendency.
2.8.8 BuzzFeed-Webis Fake News Corpus 2016
This dataset presents a set of 1627 news from 9 different publishers
for 7 weekdays prior to the 2016 US presidential elections [46]. From
the 9 publishers, 3 are mainstream (ABC, CNN, Politico) and 6 are
hyperpartisan: 3 on the left (addicting info, occupy democrats, the other
98) and 3 on the right (eagle rising, freedom daily, right-wing news).
Each news article was evaluated by journalists regarding their veracity
in 4 classes: “mixture of true and false”, “mostly true”, “mostly false”,
and “no factual content”.
2.8.9 PHEME Dataset
PHEME Dataset was first introduced by Zubiaga et al. [68]. The authors
collected rumors from Twitter based on 9 newsworthy events classified
by journalists. Then, after capturing a large set of tweets for each event,
a threshold was defined and only tweets that had a substantial number
of retweets were considered for annotation. The annotation process was
conducted by journalists and each tweet was annotated as “proven to
be false”, “confirmed as true” or “unverified”.
2.8.10 LIAR Dataset
The LIAR Dataset presented and described in [67] is, to the best of
our knowledge, the largest human annotated dataset for false infor-
mation analysis. The dataset is extracted from PolitiFact and includes
12.8K human annotated claims. Each claim is labelled with one of
the six following veracity degrees: pants fire, false, barely-false, half-
true, mostly-true and true. The dataset was also sampled to check
for coherency. The agreement rate obtained using Cohen’s Kappa
was 0.82.
A Brief Overview on the Strategies to Fight Back the Spread 335
2.8.11 SemEval Dataset
One of the most recent datasets was created for the 2019 SemEval
Task on Hyperpartisan News Detection [32]. This task consisted in
detecting articles that followed a hyperpartisan argumentation. In other
words, if it displays “blind, prejudiced, or unreasoning allegiance to
one party, faction, cause, or person” [41]. This dataset is divided into
two different types of annotations: by publisher (where the source
of the news is annotated according to BuzzFeed journalists or the
MediaBiasFactCheck website) and by article (annotated using multiple
evaluations in a crowdsourcing fashion). The dataset has a total of
750 000 articles labelled according to their publisher and 645 articles
labelled by Crowdsourcing workers. The labels by publisher are the
same assigned in The labels assigned to
the articles are just “true” (if the article is hyper-partisan independently
of the political side) and “false”.
2.8.12 NBC Twitter Propaganda Accounts Dataset
This dataset was collected by NBC news and refers to the 200k
removed accounts that were publishing and spreading propaganda
with the ultimate goal of influencing the 2016 US Presidential elec-
tions [44]. The list of users was released by the United States
Congress thus it was possible to restore a part of the tweet data
and users that were suspended by Twitter. The data is separated in
users and tweets with information extracted from the Twitter API
such as the number of followers, tweets, and favourites for the
accounts and number of retweets, hashtags and mentions for the
tweets. The main difference between this dataset and other is that it
refers only to a type of tweets/users representing only one type of
2.8.13 Dataset Comparison
With a large diversity of datasets to tackle different tasks regarding the
problem of detecting unreliable information, it is important to summa-
rize the main characteristics of each dataset (like the content type, the
last modification or what was their initial purpose). Table 1 presents an
overall comparison among all datasets previously mentioned.
336 Á. Figueira et al.
Table 1 Comparison between several state of the art datasets in the classification of false information
Content Last Number Human
Name Type Modification Entries annotations? Initial Task Labels
2017-04-28 833 Yes Online Information
fake, satire, bias,
conspiracy, rumor, state,
junksci ,hate, clickbait,
unreliable, political,
Fake News
Corpus [58]
2018-02-13 9408908 No Machine Learning/
Detection Systems
fake, satire, bias,
conspiracy, rumor, state,
junksci, hate, clickbait,
unreliable, political,
Fake News
Challenge [18]
2017-06-15 75390
Probably yes
(it is not
Stance Detection agree, disagree, discusses,
Emergent [20] News
2016 262794 Yes Stance Detection stance labels: for, against,
observing veracity labels:
unverified, true, false
Dataset [48]
2016-11-25 12999 No Machine Learning/
Detection Systems
fake, satire, bias,
conspiracy, state, junksci,
hate, bs
Baly et al. [6] News
2018-08-24 1066 Yes Machine Learning/
Detection Systems
Factuality: Low, Mixed,
High Bias: Extreme-Left,
Left, Center-Left, Center,
Center-Right, Right,
A Brief Overview on the Strategies to Fight Back the Spread 337
Table 1 Continued
Content Last Number Human
Name Type Modification Entries annotations? Initial Task Labels
BuzzFeed [55] Facebook
2017-03-31 677 Yes Exploratory
left, right
2016-09-27 1627 Yes Machine Learning/
Detection Systems
mixture of true and false,
mostly true, mostly false,
no factual content
PHEME [68] Twitter Posts 2016-2-01 breaking
news 9 con-
threads 330
tweets 4842
Yes Exploratory
thread: rumor, non-rumor
rumor: true, false,
unverified tweets: support,
denies, underspecified
responses: agree,
disagrees, comments
LIAR [67] Claims 2017 12800 Yes Machine Learning/
Detection Systems
pants-fire, false, barely
false, half-true, mostly
true, true
SemEval [32] News
2018/05/04 publisher
750 000
article 645
Partially Machine Learning/
Detection Systems
article-hyperpartisan :
true,false publisher-bias:
left, left-center,least,right-
NBC Twitter
2017-09-26 tweets
accounts 513
No News story None
338 Á. Figueira et al.
3 Discussion
Fake news is nothing new. It has been shown that even before the term
has become trending, the concept has been active in different occasions.
We might say that fake news is only a threatening problem these
days because of the mass distribution an dissemination capabilities
that current digital social networks have. Due to these problems, and
particularly to the problems that consequently emerge from it for the
society, the scientific community started tackling the problem, generally
taking an approach of addressing first its different sub-problems.
The machine learning and deep learning approaches to the detection
of fake content, or even to the correspondent network analysis to
understand how this type of content can be identified, is diffuse and
generally yet quite difficult to be understood by a general public.
Regarding the bot/spam detection, although we believe that they
play an important role on the diffusion of fake news and misinformation,
they do not represent all the accounts that spread this type of content.
In some cases, the spreaders of misinformation are cyborg accounts
(humanly operated but that also include some automatic procedures),
as the authors in [11] refer. Another case which has been less discussed
in the current literature are the human operated accounts that spread mis-
information. Common examples are users who are highly influenced by
extreme biased news and that spread that information intentionally to
their followers. One could argue that this is the effect of the propagation
of unreliable content through the network. However, the probability of
having misinformation in our feed through the propagation of close
nodes in our network is higher than from the original node that spread
the content. Therefore, the evaluation of this accounts can be of major
importance when implementing a solid system for an everyday use.
Another important aspect in adapting the current theory, to a system
that informs users about which news are credible and which are
misinformation, is the effect that such system may have on more skeptic
or biased users. In fact, the “hostile media phenomenon” can affect the
use of these systems if these are not equipped with the capability of
justifying the credibility of the content. Hostile media phenomenon
states that users who already have an opinion on a given subject can
interpret the same content (regarding that subject) in different ways. The
A Brief Overview on the Strategies to Fight Back the Spread 339
concept was first studied in [1] with news regarding the Beirut massacre.
Consequently, just like news media, such systems can be criticized by
classifying a piece of news as fake by users who are in favor of the
content for analysis. This leads us to the problem of the current detection
approaches in the literature. For example, deep learning approaches and
some machine learning algorithms are black-box systems that, given an
input (in this case, a social media post), they output a score or a label (in
this case a credibility score or a fake/true label). Therefore, explaining
to a common user why the algorithm predicted such label/score can be a
hard task. Furthermore, without some type of justification, such systems
can be discredited. To tackle this problem in a real-world environment,
the major focus after developing an accurate system must be to be
capable to explain how it got to the result.
The new systems that detect, fight against, and prevent spreading of
fake news/misleading news are every day increasing. Generically, we
may say that all the stakeholders are delving into the subject, either from
a theoretical or applyed, empirical, or even ad-hoc way. Most of these
attempts use basic Artificial Intelligence from open source resources,
in some cases paying for it (AIaaS). The automatic identification of
Bots, fake accounts and deceiving information usually requires services
like Entity Recognition, Sentiment Analysis, Relevance Identification,
Importance Indexes, etc. In Table 2 we present some of the services
available in the major providers of AI as a service.
These providers are nowadays in a privileged position to
define ’facts’ and reality according to their trained data sets and
machine learning algorithms. This constitutes a dangerous situation
because we are handing to these providers the power to define the
4 Future Guidelines to Tackle Unreliable Content
We do believe that the analysis and effect of detection systems on
the perception and beliefs of users towards fake news and all sorts
of misinformation should be the next important step to be studied by
the scientific community. Accordingly, we suggest some guidelines to
approach the problem.
340 Á. Figueira et al.
Table 2 Comparison between several AIaaS providers
MS Azure IBM Watson Google Cloud AWS Text Razor
Service [36] [30] [23] [3] [61]
Named Entity
Yes Ye s Yes Yes Yes
Yes Yes Yes Yes No
Event Detection Yes Yes Yes Yes No
between entities
No Yes No No Yes
No Yes No No No
No No Yes No Yes
Yes Yes Yes Yes No
Key Phrases
Yes Ye s Yes Yes Yes
No No Yes Yes Yes
Word Sentiment No Yes Yes No No
Syntax Analysis No No Yes Yes Yes
Does it have a
free version?
Yes Ye s Yes No Ye s
In Section 2 we divided the current literature into eight different
sections: unreliable account detection, content detection, network prop-
agation analysis, fact-checking, content analysis, case studies, “fake
news” detection systems and the datasets available to perform some of
the tasks mentioned. In this section, we identify some of the limitations
of current studies and future guidelines to tackle them.
Regarding unreliable accounts, the majority of works in literature
have a focus on the detection of bots and spammers. However, not all
unreliable accounts are restrained to these two groups. In addition, to
the best of our knowledge, there are very few studies that analyze or try
to detect accounts that publish or propagate unreliable content. One of
the few examples we found was the work of Shu et al. [54] where they
analyze the role of users profiles features for the classification of fake
A Brief Overview on the Strategies to Fight Back the Spread 341
news stories. Nevertheless, the task remains the same (i.e. classification
of unreliable content). Thus, one of the suggestions for future research
in this area is the analysis and classification of accounts according to
the content that they spread. This problem can be modeled as a multi-
classification task, where a social network account is classified with a
single label. These labels can be similar to the ones used from OpenBias
and MediaBias ( for example “bias”, “fake”, “clickbait”). However,
these labels are assigned to the accounts and not to posts/articles.
Another way to define the problem is has a multi-label classification
task where for each account, several labels can be assigned (based on
the hypothesis that accounts can propagate several types of unreliable
With respect to content analysis, and to the best of our knowledge,
only the work in [5] makes a clear comparison of the presence/absence
of social feedback features when evaluating detection systems. In fact,
if the major motivation from these studies is to develop a system capable
of detecting unreliable content then, in a real world scenario, this system
must be capable of detecting this information the earliest possible.
Therefore, it is important to consider scenarios where no social feedback
is present and how the different models would behave in those condi-
tions. In addition, supervised systems in the unreliable content/“fake
news” area should evaluate their performance based on metrics that
considered how much information (and consequently time to retrieve
that information) was used. In other words, it is necessary to obtain
social feedback (or other types of features that take time to retrieve) to
have a good prediction on a post, in a real scenario, this would “allow”
the propagation of the unreliable content through the network. Thus,
when evaluating the system, there should be some penalization for
information and time that took to gather those features. We do believe
that future research on this area should adapt time and information as
two important metrics for assessing the quality/reliability of a system
since these two variable are crucial in a real-world scenario.
Approaches presented in the fact-checking section are still at
an early stage. However, some concerns arise from current studies.
Namely, in stance detection, there are three main problems: the lack
342 Á. Figueira et al.
of interpretability of the models [37], the slow training time and the
possibility of the models being time-dependent. Once again, con-
sidering a real-world detection system, interpretability plays a key
role for social network users with who suffer from confirmation bias
(i.e. tendency to read, search and interpret information in a way that
confirms its beliefs) to understand why certain content is unreliable.
Time-dependent models and the slow training time are connected since
the slow training time is not a major problem if the model is time
independent. However, an analysis of the currently built deep models
presented in Section 2.7.1 with recently extracted data is necessary
to test this hypothesis. Therefore, an evaluation of the current models
of stance detection with new and recent data should be performed to
comprehend if the deep learning models developed are not overfitting to
a particular dataset (like Emergent or the Fake News Challenge dataset).
In the scenario where these models are time-dependent, slow training
can be an issue in the implementation of a real-time unreliable detection
Table 1 shows that there is a large diversity of datasets available
for the task of detecting unreliable content (and other similar tasks
like stance detection). However, there are also some limitations that
must be tackled. The first limitation is the generalization of unreliable
websites provided by OpenSources and MBFC to unreliable articles.
In other words, datasets like the Fake News Corpus, Kaggle Dataset
and SemEval are annotated automatically based on the assumption
that if a certain website is annotated with a specific label, then all
articles of that website are labeled the same. This methodology has
some limitations since, even websites labeled as false may provide true
content. Therefore, human annotated articles provide more confidence
in each entry than a generalization based on the source. However, there
is a trade-off between human annotated entries and the size of the
dataset since human annotation requires a large set of resources (for
example for paying crowd-sourcing annotators or recruit a large set of
volunteers) and large datasets are hard to label in a reasonable amount
of time. A better solution seems to be tackling individual claims that
were debunked by websites like Snopes and PolitiFact like the LIAR
dataset. Thus, it would be interesting for future data extraction that
A Brief Overview on the Strategies to Fight Back the Spread 343
instead of only using the claims from those websites, to extract the
original content from tweets and/or the original website to provide a
multi-source and more enriched dataset.
5 Conclusion
Fake news is nowadays of major concern. With more and more users
consuming news from their social networks, such as Facebook and
Twitter, and with an ever-increasing frequency of content available, the
ability to question the content instead of instinctively sharing or liking it
is becoming rare. The search for instant gratification by posting/sharing
content that will allow social feedback from peers has reached a status
where the veracity of information is left to the background.
Industry and scientific communities are trying to fix the problem,
either by taking measures directly into the platforms that are spreading
this type of content (Facebook, Twitter, Google, for example), devel-
oping analysis and systems capable of detecting fake content using
machine and deep learning approaches, or even by developing software
to leverage social network users in distinguishing what is fake from
what is real.
However, observing the various approaches taken so far, mainly by
the scientific community but also some rumors about actions taken by
Facebook and Google, we might say that mitigating or removing fake
news comes with a cost [21]: there is the danger to having someone
establishing the limits of reality, if the not the reality itself.
The trend to design and develop systems that are based on
open source resources, frameworks or APIs which facilitate entity
recognition, sentiment analysis, emotion recognition, bias recognition,
relevance identification (to name just a few), and which may be
freely available, or available at a small price, gives an escalating
power to those service-providers. That power consists on their internal
independent control to choose their machine learning algorithms, their
pre-trained data and, ultimately, in a control over the intelligence that
is built on the service provided by their systems.
Therefore, the saying “the key to one problem usually leads to
another problem” is again true. However, we have not many choices at
344 Á. Figueira et al.
the moment. Right now, the focus is to create systems that hamper or
stop the proliferation of fake news and give back to the people, not only
real information, but also a sentiment of trust in what they are reading.
Meanwhile, we need to be prepared to the next challenge, which will
be for the definition of what is important, or even more, what is real.
Nuno Guimaraes thanks the Fundac¸˜
ao para a Ciˆ
encia e Tecnologia
(FCT), Portugal for the Ph.D. Grant (SFRH/BD/129708/2017).
The work of L. Torgo was undertaken, in part, thanks to funding from
the Canada Research Chairs program.
[1] RashaAAbdulla, Bruce Garrison, Michael Salwen, Paul Driscoll,
Denise Casey, Coral Gables, and Society Division. The credibility
of newspapers, television news, and online news. 2002.
[2] Hunt Allcot and Matthew Gentzkow. SOCIAL MEDIA AND
[3] Amazon. Amazon comprehend.
comprehend/. Accessed: 2018-03-12.
[4] Qinglin Chen Mark CraftAnant Goel, Nabanita De. Fib – lets stop
living a lie., 2017. Accessed:
[5] SotiriosAntoniadis, Iouliana Litou, and Vana Kalogeraki. A Model
for Identifying Misinformation in Online Social Networks.
9415:473–482, 2015.
[6] Ramy Baly, Georgi Karadzhov, Dimitar Alexandrov, James Glass,
and Preslav Nakov. Predicting factuality of reporting and bias
of news media sources. In Proceedings of the Conference on
Empirical Methods in Natural Language Processing, EMNLP’18,
Brussels, Belgium, 2018.
[7] F Benevenuto, G Magno,T Rodrigues, and VAlmeida. Detecting
spammers on twitter. Collaboration, electronic messaging, anti-
abuse and spam conference (CEAS), 6:12, 2010.
A Brief Overview on the Strategies to Fight Back the Spread 345
[8] Alexandre Bovet and Hernan A. Makse. Influence of fake news
in Twitter during the 2016 US presidential election. pages 1–23,
[9] Carlos Castillo, Marcelo Mendoza, and Barbara Poblete. Infor-
mation Credibility on Twitter. 2011.
[10] Rogerio Chaves. Fake news detector. https://fakenewsdetector.
org/en, 2018. Accessed: 2018-06-18.
[11] Zi Chu, Steven Gianvecchio, Haining Wang, and Sushil Jajodia.
Detecting automation of Twitter accounts: Are you a human,
bot, or cyborg? IEEE Transactions on Dependable and Secure
Computing, 9(6): 811–824, 2012.
[12] Giovanni Luca Ciampaglia, Prashant Shiralkar, Luis M. Rocha,
Johan Bollen, Filippo Menczer, and Alessandro Flammini. Com-
putational fact checking from knowledge networks. PLoS ONE,
10(6):1–13, 2015.
[13] CNN. What we know about the boston bombing and its after
things-we-know, 2013. Accessed: 2018-06-12.
[14] Sarah Cohen, Chengkai Li, JunYang, and CongYu. Computational
Journalism: a call to arms to database researchers. Proceedings of
the 5th Biennial Conference on Innovative Data Systems Research
(CIDR 2011) Asilomar, California, USA., (January):148–151,
[15] David Conn. How the sun’s ’truth’ about hillsborough unravelled.
suns-truth-about-hillsborough-unravelled, 2016. Accessed: 2018-
[16] John P. Dickerson, Vadim Kagan, and V. S. Subrahmanian.
Using sentiment to detect bots on Twitter: Are humans more
opinionated than bots? ASONAM 2014 – Proceedings of the
2014 IEEE/ACM International Conference on Advances in Social
Networks Analysis and Mining, (Asonam):620–627, 2014.
[17] Buket Ers¸ahin, ¨
Ozlem AktaÅŸ, Deniz Kilmc¸ , and Ceyhun Akyol.
Twitter fake account detection. 2nd International Conference on
Computer Science and Engineering, UBMK 2017, pages 388–392,
346 Á. Figueira et al.
[18] Stance detection dataset for fnc-1., 2017. Accessed:
[19] Emilio Ferrara, Onur Varol, Clayton Davis, Filippo Menczer, and
Alessandro Flammini. The rise of social bots. Commun. ACM,
59(7):96–104, June 2016.
[20] William Ferreira andAndreas Vlachos. Emergent: a novel data-set
for stance classification. In Proceedings of the 2016 Conference of
the North American Chapter of the Association for Computational
Linguistics: Human Language Technologies, pages 1163–1168.
Association for Computational Linguistics, 2016.
[21] ´
Alvaro Figueira and Luciana Oliveira. The current state of fake
news: challenges and opportunities. Procedia Computer Science,
121(December):817–825, 2017.
[22] Zafar Gilani, Ekaterina Kochmar, and Jon Crowcroft. Classifica-
tion of Twitter Accounts into AutomatedAgents and Human Users.
Proceedings of the 2017 IEEE/ACM International Conference on
Advances in Social Networks Analysis and Mining 2017
[23] Google. Cloud natural language.
natural-language/. Accessed: 2018-03-12.
[24] B Y Jeffrey Gottfried and Elisa Shearer. News Use Across Social
Media Platforms 2017. Pew Research Center, Sept 2017(News
Use Across Social Media Platforms 2017):17, 2017.
[25] Aditi Gupta. Twitter Explodes with Activity in Mumbai Blasts!
A Lifeline or an Unmonitored Daemon in the Lurking?, (September 2017):1–17, 2011.
[26] Aditi Gupta, Hemank Lamba, and Ponnurangam Kumaraguru.
$1.00 per RT #BostonMarathon #PrayForBoston: Analyzing fake
content on twitter. eCrime Researchers Summit, eCrime, 2013.
[27] Twitter Help.About verified accounts.
managing-your-account/about-twitter-verified-accounts, 2018.
Accessed: 2018-05-14.
[28] Alex Hern. Google acts against fake news on search engine.
launches-major-offensive-against-fake-news, 2017. Accessed:
A Brief Overview on the Strategies to Fight Back the Spread 347
[29] Alex Hern. New facebook controls aim to regulate political ads and
fight fake news.
2018. Accessed: 2018-04-13.
[30] IBM. Ibm cloud docs natural language understanding.
understanding/getting-started. html. Accessed: 2018-03-12.
[31] Hamid Karimi, Courtland VanDam, Liyang Ye, and Jiliang Tang.
End-to-end compromised account detection. In 2018 IEEE/ACM
International Conference on Advances in Social Networks Anal-
ysis and Mining (ASONAM), pages 314–321. IEEE, 2018.
[32] Johannes Kiesel, Maria Mestre, Rishabh Shukla, Emmanuel Vin-
cent, David Corney, Payam Adineh, Benno Stein, and Martin
Potthast. Data for PAN at SemEval 2019 Task 4: Hyperpartisan
News Detection, November 2018.
[33] Bence Kollanyi, Philip N. Howard, and Samuel C. Woolley. Bots
and Automation over Twitter during the First U.S. Election. Data
Memo, (4):1–5, 2016.
[34] Sejeong Kwon, Meeyoung Cha, Kyomin Jung, Wei Chen, and
Yajun Wang. Prominent features of rumor propagation in online
social media. In 2013 IEEE 13th International Conference on
Data Mining, pages 1103–1108. IEEE, 2013.
[35] Iouliana Litou, Vana Kalogeraki, Ioannis Katakis, and Dim-
itrios Gunopulos. Real-time and cost-effective limitation of
misinformation propagation. Proceedings - IEEE International
Conference on Mobile Data Management, 2016-July:158–163,
[36] Microsoft. Text analytics api documentation. https://docs.micro
Accessed: 2018-03-12.
[37] Tim Miller. Explanation in artificial intelligence: Insights from the
social sciences. CoRR, abs/1706.07269, 2017.
[38] Damian Mrowca and Elias Wang. Stance Detection for Fake News
Identification. pages 1–12, 2017.
348 Á. Figueira et al.
[39] Richard Norton-Taylor. Zinoviev letter was dirty trick by mi6.
news6, 1999. Accessed: 2018-06-07.
[40] OpenSources. Opensources - professionally curated lists of online
sources, available free for public use.,
2018. Accessed: 2018-05-03.
[41] PAN. Hyperpartisan news detection. “
val19/semeval19-web/”. [Accessed: 2019-03-14].
[42] Ver´
onica P´
erez-Rosas, Bennett Kleinberg,Alexandra Lefevre, and
Rada Mihalcea. Automatic Detection of Fake News. 2017.
[43] Stephen Pfohl, Oskar Triebe, and Ferdinand Legros. Stance Detec-
tion for the Fake News Challenge with Attention and Conditional
Encoding. pages 1–14, 2016.
[44] Ben Popken. Twitter deleted 200,000 russian troll tweets. read
them here., 2018. [Online; accessed 13-March-2019].
[45] Martin Potthast, Johannes Kiesel, Kevin Reinartz, Janek Beven-
dorff, and Benno Stein. A Stylometric Inquiry into Hyperpartisan
and Fake News. 2017.
[46] Martin Potthast, Johannes Kiesel, Kevin Reinartz, Janek Beven-
dorff, and Benno Stein. Buzzfeed-webis fake news corpus 2016,
February 2018.
[47] Jacob Ratkiewicz, Michael D Conover, Mark Meiss, Bruno
Gonc¸alves, Alessandro Flammini, and Filippo Menczer Menczer.
Detecting and tracking political abuse in social media. In Fifth
international AAAI conference on weblogs and social media, 2011.
[48] Megan Risdal. Getting real about fake news. https://www.kaggle.
com/mrisdal/fake-news, 2016. Accessed: 2019-03-14.
[49] Chengcheng Shao, Giovanni Luca Ciampaglia, Alessandro Flam-
mini, and Filippo Menczer. Hoaxy:A Platform for Tracking Online
Misinformation. pages 745–750, 2016.
[50] Chengcheng Shao, Giovanni Luca Ciampaglia, Onur Varol,
Alessandro Flammini, and Filippo Menczer. The spread of fake
news by social bots. arXiv preprint arXiv:1707.07592, pages
96–104, 2017.
A Brief Overview on the Strategies to Fight Back the Spread 349
[51] Chengcheng Shao, Giovanni Luca Ciampaglia, Onur Varol,
Kaicheng Yang, Alessandro Flammini, and Filippo Menczer. The
spread of low-credibility content by social bots. 2017.
[52] Prashant Shiralkar, Alessandro Flammini, Filippo Menczer, and
Giovanni Luca Ciampaglia. Finding Streams in Knowledge
Graphs to Support Fact Checking. 2017.
[53] John Merriman Sholar, Shahil Chopra, and Saachi Jain. Towards
Automatic Identification of Fake News : Headline-Article Stance
Detection with LSTM Attention Models. 1:1–15, 2017.
[54] Kai Shu, Xinyi Zhou, Suhang Wang, Reza Zafarani, and Huan
Liu. The Role of User Profiles for Fake News Detection.
[55] Craig Silverman, Jane Lytvynenko, Lam Vo, and Jeremy Singer-
Vine. Inside the partisan fight for your news feed, 2017. [Online;
accessed 13-March-2019].
[56] Snopes. Fact-check: Comet ping pong pizzeria home to child
abuse ring led by hillary clinton.
check/pizzagate-conspiracy/, 2016. Accessed: 2018-04-13.
[57] Kate Starbird, Jim Maddock, Mania Orand, Peg Achterman,
and Robert M Mason. Rumors, False Flags, and Digital Vigi-
lantes: Misinformation on Twitter after the 2013 Boston Marathon
Bombing. iConference 2014 Proceedings, 2014.
[58] Maciej Szpakowski. Fake news corpus.
several27/FakeNewsCorpus, 2018.
[59] Eugenio Tacchini, Gabriele Ballarin, Marco L. Della Vedova,
Stefano Moret, and Luca deAlfaro. Some Like it Hoax: Automated
Fake News Detection in Social Networks. pages 1–12, 2017.
[60] Marcella Tambuscio, Giancarlo Ruffo,Alessandro Flammini, and
Filippo Menczer. Fact-checking Effect on Viral Hoaxes: AModel
of Misinformation Spread in Social Networks. pages 977–982,
[61] TextRazor. Text razor – extract meaning from your text. Accessed: 2018-03-12.
[62] LLC. The Self Agency. B.s. detector – a browser extension that
alerts users to unreliable news sources.,
2016. Accessed: 2018-06-18.
350 Á. Figueira et al.
[63] Robert Thomson, Naoya Ito, Hinako Suda, Fangyu Lin, Yafei Liu,
Ryo Hayasaka, Ryuzo Isochi, and Zian Wang. Trusting Tweets :
The Fukushima Disaster and Information Source Credibility on
Twitter. Iscram, (April):1–10, 2012.
[64] Courtland VanDam and Pang-Ning Tan. Detecting hashtag hijack-
ing from twitter. In Proceedings of the 8th ACM Conference on
Web Science, pages 370–371. ACM, 2016.
[65] Chris J Vargo, Lei Guo, and Michelle A Amazeen. The agenda-
setting power of fake news: A big data analysis of the online
media landscape from 2014 to 2016. New Media & Society, page
146144481771208, 2017.
[66] Soroush Vosoughi, Deb Roy, and Sinan Aral. The spread of true
and false news online. Science, 359(6380):1146–1151, 2018.
[67] William Yang Wang. “liar, liar pants on fire”: A new benchmark
dataset for fake news detection. In Proceedings of the 55th
Annual Meeting of the Association for Computational Linguis-
tics (Volume 2: Short Papers), pages 422–426. Association for
Computational Linguistics, 2017.
[68] Arkaitz Zubiaga, Geraldine Wong Sak Hoi, Maria Liakata, Rob
Procter, and Peter Tolmie. Analysing how people orient to and
spread rumours in social media by looking at conversational
threads. In PloS one, 2016.
Álvaro Figueira graduated in “Mathematics Applied to Computer
Science” from Faculty of Sciences (UP) in 1995. He got his MSc
in “Foundations of Advanced Information Technology” from Imperial
A Brief Overview on the Strategies to Fight Back the Spread 351
College, London, in 1997, and his PhD in Computer Science from
UP, in 2004. Prof. Figueira is currently an Assistant Professor with
tenure at Faculty of Sciences in University of Porto. His research
interests are in the areas of web mining, community detection, web-
based learning and social media automated analysis. He is a researcher
in the CRACS/INESCTEC research unit where he has been lead-
ing international projects involving University of Texas at Austin,
University of Porto, University of Coimbra and University of Aveiro,
regarding the automatic detection of relevance in social networks.
Nuno Guimaraes is currently a PhD student in Computer Science
at the Faculty of Sciences University of Porto and a researcher at
the Center for Research in Advanced Computing Systems (CRACS –
INESCTEC). His PhD is focused on the analysis and detection of
unreliable information on social media. He had previously worked as
a researcher in REMINDS project whose goal was to detect journal-
istically relevant information on Social Media. Nuno completed his
master’s and bachelor’s degree in Computer Science at the Faculty of
Sciences of the University of Porto. In his master’s thesis, he developed
a novel way to create time and domain dependent sentiment lexicons
in an unsupervised fashion.
352 Á. Figueira et al.
Luis Torgo is a Canada Research Chair (Tier 1) on Spatiotemporal
Ocean Data Analytics and a Professor of Computer Science at the
Faculty of Computer Science of the Dalhousie University, Canada. He
also holds appointments as an Associate Professor of the Department of
Computer Science of the Faculty of Sciences of the University of Porto,
Portugal, and as an invited professor of the Stern Business School of
the New York University where he has been teaching in recent years
at the Master of Science in Business Analytics. Dr. Torgo has been
doing research in the area of Data Mining and Machine Learning since
1990, and has published over 100 papers in several forums of these
areas. Dr. Torgo is the author of the widely acclaimed Data Mining
with R book published by CRC Press in 2010 with a strongly revised
second edition that appeared in 2017. Dr.Torgo is also the CEO and one
of the founding partners of KNOYDA a company devoted to training
and consulting within data science.
... This rumour circulated Due to the severity of the problem, efforts to mitigate the diffusion of fake news were conducted by the research community. These efforts are evident by the increasing number of publications each year regarding the term "fake news" [8]. However, studies that focus on the detection of fake news in social networks use data retrieved in a short period of time or specific to a particular event (e.g., elections). ...
Full-text available
The negative impact of false information on social networks is rapidly growing. Current research on the topic focused on the detection of fake news in a particular context or event (such as elections) or using data from a short period of time. Therefore, an evaluation of the current proposals in a long-term scenario where the topics discussed may change is lacking. In this work, we deviate from current approaches to the problem and instead focus on a longitudinal evaluation using social network publications spanning an 18-month period. We evaluate different combinations of features and supervised models in a long-term scenario where the training and testing data are ordered chronologically, and thus the robustness and stability of the models can be evaluated through time. We experimented with 3 different scenarios where the models are trained with 15-, 30-, and 60-day data periods. The results show that detection models trained with word-embedding features are the ones that perform better and are less likely to be affected by the change of topics (for example, the rise of COVID-19 conspiracy theories). Furthermore, the additional days of training data also increase the performance of the best feature/model combinations, although not very significantly (around 2%). The results presented in this paper build the foundations towards a more pragmatic approach to the evaluation of fake news detection models in social networks.
... Impersonators who try to connect with a real user's friends and take advantage of its reputation [31]. ...
Full-text available
This review discusses the dynamic mechanisms of misinformation creation and spreading used in social networks. It includes: (1) a conceptualization of misinformation and related terms, such as rumors and disinformation; (2) an analysis of the cognitive vulnerabilities that hinder the correction of the effects of an inaccurate narrative already assimilated; and (3) an interdisciplinary discussion on different strategies for coping with misinformation. The discussion encompasses journalistic, educational, governmental and computational viewpoints on the topic. The review also surveys how digital platforms handle misinformation and gives an outlook on opportunities to address it in light of the presented viewpoints.
... The fake news problem led to an overall increase on the number of studies published in the topic (Figueira et al., 2019). ...
Full-text available
Fake news in social media has quickly become one of the most discussed topics in today's society. With false information proliferating and causing a significant impact in the political, economical, and social domains, research efforts to analyze and automatically identify this type of content have being conducted in the past few years. In this paper, we attempt to summarize the principal findings on the topic of fake news in social media, highlighting the main research path taken and giving a particular focus on the detection of fake news and bot accounts.
Full-text available
The massive spread of digital misinformation has been identified as a major threat to democracies. Communication, cognitive, social, and computer scientists are studying the complex causes for the viral diffusion of misinformation, while online platforms are beginning to deploy countermeasures. Little systematic, data-based evidence has been published to guide these efforts. Here we analyze 14 million messages spreading 400 thousand articles on Twitter during ten months in 2016 and 2017. We find evidence that social bots played a disproportionate role in spreading articles from low-credibility sources. Bots amplify such content in the early spreading moments, before an article goes viral. They also target users with many followers through replies and mentions. Humans are vulnerable to this manipulation, resharing content posted by bots. Successful low-credibility sources are heavily supported by social bots. These results suggest that curbing social bots may be an effective strategy for mitigating the spread of online misinformation.
Full-text available
The dynamics and influence of fake news on Twitter during the 2016 US presidential election remains to be clarified. Here, we use a dataset of 171 million tweets in the five months preceding the election day to identify 30 million tweets, from 2.2 million users, which contain a link to news outlets. Based on a classification of news outlets curated by, we find that 25% of these tweets spread either fake or extremely biased news. We characterize the networks of information flow to find the most influential spreaders of fake and traditional news and use causal modeling to uncover how fake news influenced the presidential election. We find that, while top influencers spreading traditional center and left leaning news largely influence the activity of Clinton supporters, this causality is reversed for the fake news: the activity of Trump supporters influences the dynamics of the top fake news spreaders.
Full-text available
The authenticity of Information has become a longstanding issue affecting businesses and society, both for printed and digital media. On social networks, the reach and effects of information spread occur at such a fast pace and so amplified that distorted, inaccurate or false information acquires a tremendous potential to cause real world impacts, within minutes, for millions of users. Recently, several public concerns about this problem and some approaches to mitigate the problem were expressed. In this paper, we discuss the problem by presenting the proposals into categories: content based, source based and diffusion based. We describe two opposite approaches and propose an algorithmic solution that synthesizes the main concerns. We conclude the paper by raising awareness about concerns and opportunities for businesses that are currently on the quest to help automatically detecting fake news by providing web services, but who will most certainly, on the long term, profit from their massive usage.
Conference Paper
Full-text available
Online social networks (OSNs) have seen a remarkable rise in the presence of surreptitious automated accounts. Massive human user-base and business-supportive operating model of social networks (such as Twitter) facilitates the creation of automated agents. In this paper we outline a systematic methodology and train a classifier to categorise Twitter accounts into 'automated' and 'human' users. To improve classification accuracy we employ a set of novel steps. First, we divide the dataset into four popularity bands to compensate for differences in types of accounts. Second, we create a large ground truth dataset using human annotations and extract relevant features from raw tweets. To judge accuracy of the procedure we calculate agreement among human annotators as well as with a bot detection research tool. We then apply a Random Forests classifier that achieves an accuracy close to human agreement. Finally, as a concluding step we perform tests to measure the efficacy of our results.
Full-text available
The proliferation of misleading information in everyday access media outlets such as social media feeds, news blogs, and online newspapers have made it challenging to identify trustworthy news sources, thus increasing the need for computational tools able to provide insights into the reliability of online content. In this paper, we focus on the automatic identification of fake content in online news. Our contribution is twofold. First, we introduce two novel datasets for the task of fake news detection, covering seven different news domains. We describe the collection, annotation, and validation process in detail and present several exploratory analysis on the identification of linguistic differences in fake and legitimate news content. Second, we conduct a set of learning experiments to build accurate fake news detectors. In addition, we provide comparative analyses of the automatic and manual identification of fake news.
Conference Paper
Social media, e.g. Twitter, has become a widely used medium for the exchange of information, but it has also become a valuable tool for hackers to spread misinformation through compromised accounts. Hence, detecting compromised accounts is a necessary step toward a safe and secure social media environment. Nevertheless, detecting compromised accounts faces several challenges. First, social media activities of users are temporally correlated which plays an important role in compromised account detection. Second, data associated with social media accounts is inherently sparse. Finally, social contagions where multiple accounts become compromised, take advantage of the user connectivity to propagate their attack. Thus how to represent each user's network features for compromised account detection is an additional challenge. To address these challenges, we propose an End-to-End Compromised Account Detection framework (E2ECAD). E2ECAD effectively captures temporal correlations via an LSTM (Long Short-Term Memory) network. Further, it addresses the sparsity problem by defining and employing a user context representation. Meanwhile, informative network-related features are modeled efficiently. To verify the working of the framework, we construct a real-world dataset of compromised accounts on Twitter and conduct extensive experiments. The results of experiments show that E2ECAD outperforms the state of the art compromised account detection algorithms
Lies spread faster than the truth There is worldwide concern over false news and the possibility that it can influence political, economic, and social well-being. To understand how false news spreads, Vosoughi et al. used a data set of rumor cascades on Twitter from 2006 to 2017. About 126,000 rumors were spread by ∼3 million people. False news reached more people than the truth; the top 1% of false news cascades diffused to between 1000 and 100,000 people, whereas the truth rarely diffused to more than 1000 people. Falsehood also diffused faster than the truth. The degree of novelty and the emotional reactions of recipients may be responsible for the differences observed. Science , this issue p. 1146