Current State of the Art to Detect Fake News in Social Media: Global

Trendings and Next Challenges

Alvaro Figueira

, Nuno Guimaraes

and Luis Torgo

CRACS / INESCTEC and University of Porto, Rua do Campo Alegre, Porto, Portugal

Faculty of Computer Science, Dalhousie University, Halifax, Canada

Keywords:

Fake News, Detection Systems, Survey, Next Challenges.

Abstract:

Nowadays, false news can be created and disseminated easily through the many social media platforms, re-

sulting in a widespread real-world impact. Modeling and characterizing how false information proliferates

on social platforms and why it succeeds in deceiving readers are critical to develop efﬁcient algorithms and

tools for their early detection. A recent surge of researching in this area has aimed to address the key issues

using methods based on machine learning, deep learning, feature engineering, graph mining, image and video

analysis, together with newly created data sets and web services to identify deceiving content. Majority of

the research has been targeting fake reviews, biased messages, and against-facts information (false news and

hoaxes). In this work, we present a survey on the state of the art concerning types of fake news and the solu-

tions that are being proposed. We focus our survey on content analysis, network propagation, fact-checking

and fake news analysis and emerging detection systems. We also discuss the rationale behind successfully

deceiving readers. Finally, we highlight important challenges that these solutions bring.

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) ba-

sis. In addition, the easy accessibility to these plat-

forms whether it’s by a computer, tablet or mobile, al-

lows 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 networks like Twitter and Facebook has contri-

buted to the emergence of malicious users. In particu-

lar, users that infect the network with the propagation

of misinformation or rumours. This actions combi-

ned with the fact that 67% of adults consume some

type of news in social media (20% on a frequent ba-

sis) (Gottfried and Shearer, 2017) have already caused

real-world consequences (Snopes, 2016).

However, unreliable content or, how it is now re-

ferred – ”fake news” –, is not a recent problem. Alt-

hough the term gained popularity in the 2016 US pre-

sidential 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 news-

paper four days before the general elections. The goal

was to destabilize the elections in favour of the con-

servative party with a directive from Moscow to Bri-

tish communists referring an Anglo-Soviet treaty and

inspiring ”agitation-propaganda” in the armed forces

(Norton-Taylor, 1999). Another example happened

after the ”Hillsborough accident”, where 96 people

died crushed due to overcrowding and lack of secu-

rity. Reports from an illustrious newspaper claimed

that, as people were dying, some fellow drunk sup-

porters stole from them and beat police ofﬁcers that

were trying to help. Later, such claims were proven

false (Conn, 2016).

The veriﬁed impact of fake news in society

throughout the years and the inﬂuence that social net-

works currently have today forced high reputation

companies, such as Google and Facebook, to start

working on a method to mitigate the problem (Hern,

2017; Hern, 2018). The scientiﬁc community has also

been increasing the activity on the topic. In fact, if we

search in Google Scholar

for ”fake news”, we will

ﬁnd a signiﬁcantly high number of results that have

https://scholar.google.com

332

Figueira, A., Guimaraes, N. and Torgo, L.

Current State of the Art to Detect Fake News in Social Media: Global Trendings and Next Challenges.

DOI: 10.5220/0007188503320339

In Proceedings of the 14th International Conference on Web Information Systems and Technologies (WEBIST 2018), pages 332-339

ISBN: 978-989-758-324-7

an increase of 7K publications, when compared with

the number obtained in the previous year.

Nevertheless, the problem of fake news is still a

reality since the solution is anything but trivial. More-

over, 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 evalu-

ate the current systems/proposals in a real-world en-

vironment.

2 LITERATURE REVIEW

There are several approaches to tackle the problem

of unreliable content on social media. Some authors

opt by analyzing the patterns of propagation, others

by creating supervised systems to classify unreliable

content, and others by focusing on the characteristics

of the accounts that share this type of content. In addi-

tion, some works also focus on developing techniques

for fact-checking claims or focus on speciﬁc case stu-

dies.

2.1 Account Analysis

Regarding the analysis of social media accounts, the

current state of the art has being focusing on trying to

identify bot or spammer accounts.

Castillo et al. (Castillo et al., 2011) target the de-

tection 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 fea-

tures (Message, User, Topic, and Propagation) on a

Decision Tree Model that achieved an accuracy of

86% from a balanced dataset. A more recent work

(Erahin et al., 2017) used an Entropy Minimization-

Discretization technique that combines numerical fe-

atures with assessing fake accounts on Twitter.

Benevuto et al.(Benevenuto et al., 2010) develo-

ped a model to detect spammers by building a manual

annotated dataset of 1K rcors 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 similar problem is the detection of bot accounts.

Chu et al. (Chu et al., 2012) 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 de-

cision making). The performance of the system was

evaluated using accuracy, which reached 96% in the

”Human” class. Another similar work (Dickerson

et al., 2014), introduced a methodology to differen-

tiate accounts into two classes: humans and bots.

Gilani et al. (Gilani et al., 2017) presented a so-

lution to a similar goal: to distinguish automated ac-

counts from human ones. However, they introduced

the notion that ”automated” is not necessarily bad.

Using a dataset containing a large quantity of user ac-

counts, the authors divided and categorized each entry

into 4 different groups regarding the popularity (fol-

lowers) of the account. The evaluation was conducted

using the F1-measure. The results obtained fall bet-

ween 77% and 91% .

2.2 Content Analysis

A work by Antoniadis et al. (Antoniadis et al., 2015)

tried to identify misinformation on Twitter. The aut-

hors annotated a large dataset of tweets and develo-

ped 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 com-

pared with the model that uses all available features.

An approach also using social feedback was presen-

ted by Tacchini et al. (Tacchini et al., 2017). The

authors claim that by analyzing the users who liked a

small set of posts containing false and true informa-

tion, they can obtain a model with an accuracy near

80%.

Perez-Rosas (P

erez-Rosas et al., 2017) 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 (Potthast

et al., 2017) which analyses the writing style of hyper-

partisan (extremely biased) news. The authors adopt a

meta-learning approach (”unmasking”) from the aut-

horship veriﬁcation problem. The results obtained

Current State of the Art to Detect Fake News in Social Media: Global Trendings and Next Challenges

333

show models capable of reaching 78% in F1-measure

in the task of classifying hyper-partisan and main-

stream 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%.

2.3 Network Propagation

In Shao (Shao et al., 2016) the authors expose a met-

hod describing the extraction of posts that contai-

ned 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 the users that propagate

fake news are much more active on social media than

the 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. (Tambuscio

et al., 2015) describes the relations between fake news

believers and fact-checkers. The study modiﬁes and

resorts to a model commonly used in the analysis of

disease spreading, where the misinformation is ana-

lyzed as a virus. Nodes on the network can be belie-

vers of fake news, fact-checkers or susceptible (neu-

tral) users. Susceptible nodes can be infected by fake

news believers although they can ”recover” when con-

fronted with fact-checking nodes. By testing their ap-

proach 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 mes-

sage.

A similar approach is proposed in (Litou et al.,

2016) 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.

2.4 Fact-Checking

Another way to tackle the problem of false informa-

tion is through fact-checking. Due to the enormous

quantity of information spread through social net-

works, the necessity to automatize this task has be-

come crucial. Automated fact-checking aims to ve-

rify claims automatically through consultations and

extraction of data from different sources. Then, ba-

sed on the strength and stance of reputable sources

regarding the claim, a classiﬁcation is assigned (Co-

hen et al., 2011). This methodology, despite being in

development is very promising.

2.4.1 Stance Detection

In earlier research, stance detection has been deﬁned

as the task of a given fragment of text agrees, dis-

agrees or is unrelated to a speciﬁc target topic. Ho-

wever, in the context of fake news detection, stance

detection has been adopted as a primary step to de-

tect 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 wri-

ting about that topic. Therefore, stance detection can

be applied to understand 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

(Pfohl et al., 2016; Sholar et al., 2017)

The fake news challenge

promotes the identiﬁca-

tion 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 con-

sists in identifying if the body of text agrees, dis-

agrees, discusses or its unrelated with the headline.

Several approaches were presented using the dataset

provided. The authors in (Mrowca and Wang, 2017)

present several approaches using a conditioned bidi-

rectional LSTM (Long Short Term Memory) and the

baseline model (GradientBoosted Classiﬁer provided

by the authors of the challenge) with an additional va-

riation of features. As for the features, Bag of Words

and GloVe vectors were used. In addition, global fea-

tures like binary co-occurrence in words from the he-

adline and the text, polarity words and word grams

were used. The best result achieved was using bidi-

rectional LSTM with the inclusion of the global featu-

res mentioned. The improvement regarding the base-

line was 9.7%. Other works with similar approaches

were proposed (Pfohl et al., 2016; Sholar et al., 2017)

however, results do not vary signiﬁcantly.

Stance detection is an important step towards the

problem of fake news detection. The fake news chal-

lenge 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.

http://www.fakenewschallenge.org/

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2.4.2 Fact-checking as a Network Problem

The authors in (Ciampaglia et al., 2015) tackle fact-

checking as a network problem. By using the Wiki-

pedia infoboxes to extract facts in a structured way,

the authors proposed an automatic fact-checking sy-

stem 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 ”x was marry to

y”, ”d directed f ” and ”c is the capital of r”) and an in-

dependent corpus with novel statements annotated by

human raters. The results of the ﬁrst evaluation sho-

wed that true statements have higher truth values than

false. In the second evaluation, the values from hu-

man annotators and the ones predicted by the system

are correlated.

Another work by the same authors (Shiralkar

et al., 2017) use an unsupervised approach to the pro-

blem. The Knowledge Stream methodology adapts

the Knowledge Network to a ﬂow network since mul-

tiple 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. Howe-

ver, in various cases, it provides additional evidence

to support the fact-checking of claims.

2.5 Fake News Analysis

Another major area of study is the analysis of large

quantities of fake news spread through social net-

works. Vosoughi et al. (Vosoughi et al., 2018) presen-

ted 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 false-

hood 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 (Vargo et al., 2017)

studied the agenda-setting relationships between on-

line 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 cer-

tain issues were transferred to news media due to fake

news (more frequently in fake stories about internati-

onal relations). Furthermore, fake news also predicted

the issue agenda of partisan media (more in the libe-

ral side than the conservative). Other relevant ﬁndings

are the reactive approach of fake news media to tradi-

tional and emerging media and the autonomy of fact-

checking websites regarding online media agendas.

2.6 Case Studies

Some works focus on analyzing the dissemination of

false information regarding a particular event. One of

those related to the Boston Marathon in 2013, where

two homemade bombs were detonated near the ﬁnish

of the race (CNN, 2013). For example, in (Gupta

et al., 2013) the authors performed an analysis on 7.9

million tweets regarding the bombing. The main con-

clusions were that 20% of the tweets were true facts

whether 29% were false information (the remaining

were opinions or comments), it was possible to pre-

dict 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 rumours spread on

Twitter after the bombings occurred (Starbird et al.,

2014).

A different event tackled was the US Presidential

Election in 2016. For example, the authors in (Al-

lcot and Gentzkow, 2017) combined online surveys

with information extracted from fact-checking websi-

tes 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. Anot-

her work (Bovet and Makse, 2018) studied the in-

ﬂuence of fake news and well know news outlets on

Twitter during the election. The authors collected ap-

proximately 171 million tweets in the 5 months prior

to the elections and showed that bots diffusing unre-

liable news are more active than the ones spreading

other types of news (similar to what was found in

(Allcot and Gentzkow, 2017)). In addition, the net-

work diffusing fake and extreme bias news is den-

ser than the network diffusing center and left-leaning

news. Other works regarding this event are presented

in (Kollanyi et al., 2016; Shao et al., 2017).

Other works address similar events such as Hur-

ricane Sandy (Antoniadis et al., 2015), the Fukus-

hima Disaster (Thomson et al., 2012) and the Mumbai

Blasts in 2011 (Gupta, 2011).

Current State of the Art to Detect Fake News in Social Media: Global Trendings and Next Challenges

335

2.7 Fake News Detection Systems

The majority of the implementations to detect fake

news comes in the form of a browser add-on. For ex-

ample, the bs-detector (The Self Agency, 2016) ﬂags

content in social media in different categories such

as clickbait, bias, conspiracy theory and junk science.

To make this evaluation, the add-on uses OpenSour-

ces

which is a curated list of dubious websites. A

more advanced example is the Fake News Detector

(Chaves, 2018). 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 inte-

resting system that also took the shape of an add-on

was the one developed by four colleges students du-

ring a hackathon at Princeton University (Anant Goel,

2017). 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, recog-

nizes 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 then uses the add-

on approach, but many are not yet system to be usable

by the normal people as they are yet proof of concept

prototypes.

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 be-

cause of the mass distribution an dissemination capa-

bilities 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, ta-

king an approach of addressing ﬁrst its different sub-

problems.

The detection of bot/spam accounts, the machine

learning and deep learning approaches to the de-

tection of fake content or even the network analysis

to understand how this type of content can be iden-

tiﬁed is diffuse and generally yet quite difﬁcult to be

understood by a general public.

Regarding the bot/spam detection, we do believe

that even they play an important role on the diffusion

http://www.opensources.co/

of fake news and misinformation, they do not repre-

sent all the accounts that spread this type of content.

In some cases, the spreaders of misinformation are

cyborg accounts (humanly operated but that also in-

clude some automatic procedures), as the authors in

(Chu et al., 2012) refer. Another case which has been

less discussed in the current literature are the human

operated accounts that spread misinformation. Com-

mon 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 con-

tent through the network. However, the probability of

having misinformation in our feed through the propa-

gation of close nodes in our network is higher than

from the original node that spread the content. The-

refore, 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 pheno-

menon” can affect the use of these systems if these

are not equipped with the capability of justifying the

credibility of the content. Hostile media phenome-

non states that users who already have an opinion on

a given subject can interpret the same content (regar-

ding that subject) in different ways. The concept was

ﬁrst studied in (Abdulla et al., 2002) with news re-

garding the Beirut massacre. Consequently, just like

news media, such systems can be criticized by clas-

sifying 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 li-

terature. For example, deep learning approaches and

some machine learning algorithms are black-box sys-

tems that, given an input (in this case, a social media

post), they output a score or a label (in this case a cre-

dibility score or a fake/true label). Therefore, explain

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 dis-

credited. To tackle this problem in a real-world envi-

ronment, the major focus after developing an accurate

system must be to be capable to explain how it got to

the result.

3.1 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

WEBIST 2018 - 14th International Conference on Web Information Systems and Technologies

336

fake news and all sorts of misinformation should be

the next important step to be studied by the scientiﬁc

community. Accordingly, we suggest some guideli-

nes to approach the problem.

Recently, we have observed an increasing distrust

on the press by the common citizen. Several reasons

can be pointed such as the president of the United Sta-

tes calling fake news to mainstream news media such

as CNN or NBC

or even the mainstream media it-

self retracting a large number of stories and fail to

highlight the importance of certain entities or events

such as Donald Trump, Jeremy Corbin or Brexit.

Therefore, a misinformation detection system that

only scores the credibility of a social media post, jus-

tifying it by the absence/presence of similar informa-

tion on traditional news media outlets may not be

enough to convince the majority of users that con-

sume this type of information, and to change their

beliefs and acknowledge the veracity or falsehood of

the content. In addition, if it is used, the selection

of mainstream news media sources to perform such

comparison, then is must be balanced (at least with

some normalization of its intensity/frequency) regar-

ding some possible bias. Moreover, it is necessary to

add features to the system to allow more information

to be provided alongside the credibility score. Such

examples could include the reputation of the original

source (that created the post), and analyze and present

the social feedback of the users that voice it, again in

a weighted manner.

The credibility of the original source can be ana-

lyzed and explained through the history of previous

published posts that include misinformation and were

debunked by fact-checking entities such as Snopes

or PolitiFact

. In addition, information should be

provided by the system on the possibility of an origi-

nal source be a bot or cyborg. This can be done by the

analysis of the posting frequency and near accounts

(followers and friends). Such analysis can have a

more impact on changing user beliefs than a simple

score or label.

Regarding the social feedback, one can look at the

propagation of the content through shares/retweets,

comments/replies and favorites/likes. However, an

important factor must come into play which is the

”echo chamber” effect. This refers to the problem

of users with the same interests are aggregated toget-

her in social circles, and the opposing ideas are re-

jected and disapproved by the majority. In Facebook,

for example, when looking at comments/replies to a

https://twitter.com/realDonaldTrump/status/

1006891643985854464

https://www.snopes.com/

http://www.politifact.com/

post inside one of this echo chambers, the majority of

comments are in agreement with the post. This factor

combined with the number of likes, shares, etc, may

lead the user to a false impression that the informa-

tion is true. Moreover, Facebook by default increases

the ranking of the comments based on the number of

replies and likes that the comments got as well as if

the comments come from the user’s friends. This sce-

nario is extremely propitious to inﬂuence the opinion

of a user, therefore contributing to the formation and

expansion of these ”echo chambers”.

Therefore, in a real-world fake news detection sy-

stem, information put on social feedback should be

ranked and presented to the user based on the ”bias”

of the social media accounts that are engaging with

the post being analyzed. In addition, social feedback

from users who are more neutral or equally active on

both sides of the political spectrum should be prioriti-

zed since they could present a more careful and unbi-

ased view of the subject or even act as fact-checkers

of more suspicious claims. It is our hypothesis that

the way social feedback is presented may inﬂuence

users’ beliefs regarding the credibility score (even not

affecting the score directly).

For the sake of clarity, let us consider the dom-

ain of politics where there are two main groups: con-

servatives (c) and liberals (l). Let us also consider a

social media post which is false and favours the li-

beral side. A user that likes, spreads, replies, and is

connected to liberal content accounts should have a

lower rank on posts that favour his political views. In

the same way, an opposing user which displays the

same behaviour regarding conservative content must

also have a smaller rank with respect to the content

that opposites their political views. However, users

who are capable of engaging with content from both

political views in an equal and neutral position should

have a higher rank. A simple function f that can be

used to score the ranking of an user u may be given

by:

1 − |p

u,l

− p

u,c

where p refers to an intermediate score of user u

engagement in liberal (l) or conservative content (c).

This probability can be computed using the type of

posts that users propagate, like or reply/comment. In

addition (and expanding what is done in (Tacchini

et al., 2017)), the compliance between the post and

the user comments/replies can also determine the ten-

dency of a user. This can be analyzed recurring, for

example, to sentiment analysis tools.

Analyzing the source account of the content and

ranking the presentation of social feedback (prioriti-

Current State of the Art to Detect Fake News in Social Media: Global Trendings and Next Challenges

337

zing neutral but active users) might increase skeptic

to trust in the fake news detection system.

4 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 con-

tent (Facebook, Twitter, Google, for example), deve-

loping analysis and systems capable of detecting fake

content using machine and deep learning approaches,

or even by developing software to leverage social net-

work 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 rumours about actions taken by Facebook and

Google, we might say that mitigating or removing

fake news comes with a cost (Figueira and Oliveira,

2017): there is the danger to having someone establis-

hing 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 analy-

sis, 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 po-

wer consists on their internal independent control to

choose their machine learning algorithms, their pre-

trained data and, ultimately, in a control over the in-

telligence 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. Ho-

wever, we have not many choices at the moment. It is

a too much important endeavor to create systems that

hamper of 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. Mean-

while, we need to be prepared to the next challenge,

which will be for the deﬁnition of what is real, what

is important, or even, what is real.

ACKNOWLEDGEMENTS

Project ”TEC4Growth - Pervasive Intelligence, En-

hancers and Proofs of Concept with Industrial

Impact/NORTE-01-0145-FEDER-000020” is ﬁnan-

ced by the North Portugal Regional Operational Pro-

gramme (NORTE 2020), under the PORTUGAL

2020 Partnership Agreement, and through the Euro-

pean Regional Development Fund (ERDF).

REFERENCES

Abdulla, R. A., Garrison, B., Salwen, M., Driscoll, P., Ca-

sey, D., Gables, C., and Division, S. (2002). The

credibility of newspapers, television news, and online

news.

Allcot, H. and Gentzkow, M. (2017). Social Media and Fake

News in the 2016 Election.

Anant Goel, Nabanita De, Q. C. M. C. (2017). Fib - lets stop

living a lie. https://devpost.com/software/ﬁb. Acces-

sed: 2018-06-18.

Antoniadis, S., Litou, I., and Kalogeraki, V. (2015). A Mo-

del for Identifying Misinformation in Online Social

Networks. 9415:473–482.

Benevenuto, F., Magno, G., Rodrigues, T., and Almeida, V.

(2010). Detecting spammers on twitter. Collabora-

tion, electronic messaging, anti-abuse and spam con-

ference (CEAS), 6:12.

Bovet, A. and Makse, H. A. (2018). Inﬂuence of fake news

in Twitter during the 2016 US presidential election.

pages 1–23.

Castillo, C., Mendoza, M., and Poblete, B. (2011). Infor-

mation Credibility on Twitter.

Chaves, R. (2018). Fake news detector. https://

fakenewsdetector.org/en. Accessed: 2018-06-18.

Chu, Z., Gianvecchio, S., Wang, H., and Jajodia, S. (2012).

Detecting automation of Twitter accounts: Are you a

human, bot, or cyborg? IEEE Transactions on Depen-

dable and Secure Computing, 9(6):811–824.

Ciampaglia, G. L., Shiralkar, P., Rocha, L. M., Bollen, J.,

Menczer, F., and Flammini, A. (2015). Computational

fact checking from knowledge networks. PLoS ONE,

10(6):1–13.

CNN (2013). What we know about the boston bombing

and its aftermath. https://edition.cnn.com/2013/04/18/

us/boston-marathon-things-we-know. Acessed: 2018-

06-12.

Cohen, S., Li, C., Yang, J., and Yu, C. (2011). Computatio-

nal Journalism: a call to arms to database researchers.

Proceedings of the 5th Biennial Conference on Inno-

vative Data Systems Research (CIDR 2011) Asilomar,

California, USA., (January):148–151.

Conn, D. (2016). How the sun’s ’truth’ about hills-

borough unravelled. https://www.theguardian.com/

football/2016/apr/26/how-the-suns-truth-about-

hillsborough-unravelled. Acessed: 2018-06-07.

WEBIST 2018 - 14th International Conference on Web Information Systems and Technologies

338

Dickerson, J. P., Kagan, V., and Subrahmanian, V. S. (2014).

Using sentiment to detect bots on Twitter: Are humans

more opinionated than bots? ASONAM 2014 - Pro-

ceedings of the 2014 IEEE/ACM International Confe-

rence on Advances in Social Networks Analysis and

Mining, (Asonam):620–627.

Erahin, B., Akta,

O., Kilmc¸, D., and Akyol, C. (2017). Twit-

ter fake account detection. 2nd International Confe-

rence on Computer Science and Engineering, UBMK

2017, pages 388–392.

Figueira,

A. and Oliveira, L. (2017). The current state of

fake news: challenges and opportunities. Procedia

Computer Science, 121(December):817–825.

Gilani, Z., Kochmar, E., and Crowcroft, J. (2017).

Classiﬁcation of Twitter Accounts into Automated

Agents and Human Users. Proceedings of the 2017

IEEE/ACM International Conference on Advances in

Social Networks Analysis and Mining 2017 - ASO-

NAM ’17, pages 489–496.

Gottfried, B. Y. J. and Shearer, E. (2017). News Use Across

Social Media Platforms 2017. Pew Research Center,

Sept 2017(News Use Across Social Media Platforms

2017):17.

Gupta, A. (2011). Twitter Explodes with Activity in Mum-

bai Blasts! A Lifeline or an Unmonitored Daemon

in the Lurking? Precog.Iiitd.Edu.in, (September

2017):1–17.

Gupta, A., Lamba, H., and Kumaraguru, P. (2013). $1.00

per RT #BostonMarathon #PrayForBoston: Analy-

zing fake content on twitter. eCrime Researchers Sum-

mit, eCrime.

Hern, A. (2017). Google acts against fake news on

search engine. https://www.theguardian.com/

technology/2017/apr/25/google-launches-major-

offensive-against-fake-news. Accessed: 2018-04-13.

Hern, A. (2018). New facebook controls aim to re-

gulate political ads and ﬁght fake news. https://

www.theguardian.com/technology/2018/apr/06/

facebook-launches-controls-regulate-ads-publishers.

Accessed: 2018-04-13.

Kollanyi, B., Howard, P. N., and Woolley, S. C. (2016).

Bots and Automation over Twitter during the First

U.S. Election. Data Memo, (4):1–5.

Litou, I., Kalogeraki, V., Katakis, I., and Gunopulos, D.

(2016). Real-time and cost-effective limitation of mis-

information propagation. Proceedings - IEEE Inter-

national Conference on Mobile Data Management,

2016-July:158–163.

Mrowca, D. and Wang, E. (2017). Stance Detection for

Fake News Identiﬁcation. pages 1–12.

Norton-Taylor, R. (1999). Zinoviev letter was dirty trick

by mi6. https://www.theguardian.com/politics/1999/

feb/04/uk.politicalnews6. Acessed: 2018-06-07.

erez-Rosas, V., Kleinberg, B., Lefevre, A., and Mihalcea,

R. (2017). Automatic Detection of Fake News.

Pfohl, S., Triebe, O., and Legros, F. (2016). Stance De-

tection for the Fake News Challenge with Attention

and Conditional Encoding. pages 1–14.

Potthast, M., Kiesel, J., Reinartz, K., Bevendorff, J., and

Stein, B. (2017). A Stylometric Inquiry into Hyper-

partisan and Fake News.

Shao, C., Ciampaglia, G. L., Flammini, A., and Menczer,

F. (2016). Hoaxy: A Platform for Tracking Online

Misinformation. pages 745–750.

Shao, C., Ciampaglia, G. L., Varol, O., Yang, K., Flam-

mini, A., and Menczer, F. (2017). The spread of low-

credibility content by social bots.

Shiralkar, P., Flammini, A., Menczer, F., and Ciampaglia,

G. L. (2017). Finding Streams in Knowledge Graphs

to Support Fact Checking.

Sholar, J. M., Chopra, S., and Jain, S. (2017). Towards

Automatic Identiﬁcation of Fake News : Headline-

Article Stance Detection with LSTM Attention Mo-

dels. 1:1–15.

Snopes (2016). Fact-check: Comet ping pong pizzeria

home to child abuse ring led by hillary clinton. https://

www.snopes.com/fact-check/pizzagate-conspiracy/.

Accessed: 2018-04-13.

Starbird, K., Maddock, J., Orand, M., Achterman, P., and

Mason, R. M. (2014). Rumors, False Flags, and Di-

gital Vigilantes: Misinformation on Twitter after the

2013 Boston Marathon Bombing. iConference 2014

Proceedings.

Tacchini, E., Ballarin, G., Della Vedova, M. L., Moret, S.,

and de Alfaro, L. (2017). Some Like it Hoax: Auto-

mated Fake News Detection in Social Networks. pa-

ges 1–12.

Tambuscio, M., Ruffo, G., Flammini, A., and Menczer, F.

(2015). Fact-checking Effect on Viral Hoaxes: A Mo-

del of Misinformation Spread in Social Networks. pa-

ges 977–982.

The Self Agency, L. (2016). B.s. detector - a browser exten-

sion that alerts users to unreliable news sources. http://

bsdetector.tech/. Accessed: 2018-06-18.

Thomson, R., Ito, N., Suda, H., Lin, F., Liu, Y., Hayasaka,

R., Isochi, R., and Wang, Z. (2012). Trusting Tweets :

The Fukushima Disaster and Information Source Cre-

dibility on Twitter. Iscram, (April):1–10.

Vargo, C. J., Guo, L., and Amazeen, M. A. (2017). The

agenda-setting power of fake news: A big data analy-

sis of the online media landscape from 2014 to 2016.

New Media & Society, page 146144481771208.

Vosoughi, S., Roy, D., and Aral, S. (2018). The spread of

true and false news online. Science, 359(6380):1146–

1151.

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339