=Paper=
{{Paper
|id=Vol-2041/paper4
|storemode=property
|title=Towards an Understanding of Fake News
|pdfUrl=https://ceur-ws.org/Vol-2041/paper4.pdf
|volume=Vol-2041
|authors=Ozlem Ozgobek,Jon Atle Gulla
}}
==Towards an Understanding of Fake News==
https://ceur-ws.org/Vol-2041/paper4.pdf
Towards an Understanding of Fake News
Özlem Özgöbek, Jon Atle Gulla
Email: {ozlem.ozgobek, jon.atle.gulla}@ntnu.no
Department of Computer Science, NTNU, Trondheim, Norway
Abstract. Fake news articles are intentionally fabricated to be decep-
tive and can be proven that they are false. Fake news and spread of mis-
information are important concepts which may have serious real world
consequences. Even though this concept exists for so many years, with
the advancements in technology the speed of diusion of misinformation
and, how people consume and produce news has changed a lot. So the
eort towards detecting fake news quickly and correctly has became a
challenge. Today most of the fact checking is done by professional jour-
nalists but the research towards the automatic detection of fake news
increases rapidly. For automatic detection of fake news, linguistic and
machine learning techniques are the most frequently used techniques. In
this paper, we analyze these techniques in three main groups: Content
based methods, user based methods and network based methods. We also
give a short introduction to the concepts and present some preliminary
research results towards an understanding of fake news.
It was not so long ago that the term fake news started to appear frequently
in the media. Even though the terms like deception, hoax, clickbait and credibil-
ity detection of news articles are within the focus of researchers for some time,
with the frequent use of the term fake news, a new de tion and more speci c
work towards detecting it was required.
Even though the term fake news is quite new, there was always some news-
papers trying to take more attention from the readers through exaggerated head-
lines and articles containing misinformation [7] [1]. In the internet era, where
every individual have an opportunity to publish and be visible by many others,
it is not surprising that the generation of fake news has increased. One of the
main reasons of generating fake news is the economic gain which can be acquired
by getting more clicks or generating paid fake content [5] for parties who want
to get more clicks. Fake news can cause sudden changes in stock market and this
can easily be converted to an economic gain by the parties who published the
fake news articles [2]. Another common reason for generating fake news is trying
to create a deception and/or a political bias within users in order to get more
supporters. On the other hand where to draw the line between fake news and
expression of opinions is important.
Copyright held by the author(s). NOBIDS 2017
2
There are many reasons that the fake news gained so much attention in the
last couple of years. Especially the 2016 US elections played an important role
for the public attention on fake news. The fact that fake news actually causes
some real world problems [4] [6] is another reason that the public reaction has
increased.
Obviously social media is an important way of getting news for especially
younger generation [17]. There are two aspects of the news on social media:
Traditional news shared on social media and social media as a source of news
(through users who shared events nearby). The second aspect is sometimes used
by the traditional media houses to generate news articles. Both aspects, inten-
tionally or unintentionally, can lead fake news to spread even more.
According to some research people are not very good at distinguishing real
news from fake ones. In [18] it is suggested that humans can distinguish only 70%
of the fake news. In another study it is mentioned that 75% of people classi ed
the fake news as accurate news [26]. [23] suggests that people tend to classify
news articles that they do not agree as fake news. The confusion and bias of the
readers bring the demand for approved news from trusted sources.
Today fake news detection is mostly done by the manual work of professional
journalists. Fact checking web pages are available in many languages all around
the world [3]. On the other hand, the amount of research going on towards
the automatic detection of fake news is increasing rapidly. The collaboration
between the researchers and journalists has an increasing importance towards
the development of automatic fake news detection systems.
In this paper we give a brief state of the art to the automatic detection of
fake news. The paper is structured as follows: In Section 1 the de nition of fake
news is given with dierent aspects. In Section 2 the diusion mechanisms of fake
news is introduced. Commonly used techniques towards automatic detection of
fake news is discussed in Section 3. A brief summary of the important research
ndings is given in Section 4. Finally, the conclusions is given in Section 5.
1 De ning Fake News
Automatic detection of fake news has many challenges. De ning fake news and
setting the right scope for detecting it is one of the basic challenges. The scope of
fake news is usually dierent within dierent research about fake news detection.
For example, clickbaits (the content which is speci cally designed to attarct more
attention from the readers) are seen as implicated in fast spread of fake news by
some researchers [11], while others does not count this aspect. Similarly satirical
news is classi ed as a type of fake news by some researchers [25], while it is
not by some others [29]. There is a similar debate about rumors, conspiracy
theories, hyperpartisan news and hoaxes too. A more general term used for fake
news and misinformation detection is called deception detection. In [24] authors
de ne deceptive news in three main categories: Intentionally fabricated, large
scale hoaxes and humorous news taken seriously. On the other hand, in [29]
a narrower de nition of fake news was accepted which only includes the news
3
articles that are intentionally and veri ably false [8]. This de nition of fake news
does not include satire, hoax, rumors, conspiracy theories and unintentionally
created misinformation.
Keeping the fake news de nition as narrow as possible for automatic fake
news detection research has more advantages in order to keep the focus down
to the core elements and eliminate ambiguity. But we should also keep in mind
that there are more complicated challenges and it is important to:
Draw the line between fake news and expression of opinions.
Distinguish humour, satire, fauxtire and irony: The satire and humouristic
news articles sometimes classi ed as fake news because they does not con-
tain real events and sometimes they can mislead readers. Also detecting the
humor within a true news story is challenging and can be a false cue in order
to detect the fake news automatically.
Consider fake items in a true story: Sometimes the news article can contain
fake elements. These elements does not make the whole story fake but can
cause deception. These fake elements are usually harder to detect than de-
tecting the whole fake news article and they make it hard to classify the
article as fake or not or to decide the level of fake elements in the article.
In this paper we assume the following de nition of fake news: Fake news
articles are intentionally fabricated to be deceptive and can be proven that they
are false.
2 The Diusion of Fake News
The diusion of fake news can be through dierent media but obviously social
media has the greatest share on spread of fake news. The very dynamic nature
of social media and the fact that every individual is also a publisher in it, makes
social media a suitable medium for diusion of any information. But in social
media fake, in ammatory, emotional and one-sided news spread quicker than
many serious, real news articles [21]. Understanding the spread mechanisms of
fake news helps us to prevent them spreading more and even to detect them.
The value of social media as a news source can not be ignored. Social media has
a value as a quick and rst hand source of news events [32] with an increased
event coverage [19]. On the other hand, due to the high noise levels and lack of
validation mechanisms, extracting true news from social media is highly chal-
lenging. Still, social media is commonly used as a source of news for various
news web pages and it has become a prioritized source of news for especially the
younger generation [17]. Besides, strong personal biases exist when it comes to
the perception of news. According to [23] people tend to classify the news arti-
cles that they do not agree with as fake news. Obviously these strong biases
will aect what they share on social media and the spread of fake news. All of
these aspects of social media put it in a very important position for fake news
diusion.
4
According to [13] the spread of fake images on Twitter as a representative
of a news, was mainly (%86) distributed through retweets and a few through
original tweets.
Social bots are another important thing to look at in order to understand
the spread mechanisms of fake news. In [28] authors suggest that bots play an
important role in spreading the misinformation on social networks, they are
mostly active in the early phases of the spreading and they target the in uential
users that has a bigger chance of spreading the false information.
3 Commonly Used Detection Techniques
With the increase in attention to fake news, there is also a fast increase in
the number of recent research in this topic. In this section we summarize the
commonly used automatic detection techniques for fake news.
3.1 Content based methods
Content based methods use content cues in order to detect fake news. Content
cue implies to any kind of content related cue to detect fake news. Most of the
content cues are textual cues, so we nd it useful to classify the content cues
as textual and non-textual cues. A more detailed classi cation of cues towards
clickbait detection is proposed in [11]. Content based methods include the anal-
ysis of any kind of content available in the news like text, image, video or sound
in combination with various machine learning techniques.
Textual content Many of the existing research look for textual cues in order
to detect fake news. This is also how professional journalists approach to manual
fake news detection. Understanding and analyzing the text for fake elements is
the most natural way that we can think of. In order to detect textual cues, lexi-
cal, semantic, syntactic and pragmatic leves of analysis are usually used. Writing
style analysis is one of the most commonly used techniques towards the detection
of misinformation in news [20], [21]. In addition to writing style some research
addresses the language use analysis. In [15] authors suggest a method that in-
cludes stylistic, complexity and psychological feature analysis by using NLP and
sentiment analysis. In [14] linguistic (n-gram) features, credibility features (cap-
italization, punctuation etc.) and semantic features (DBpedia and embeddings)
are used for analysis. Another research for detecting clickbaits proposes a lin-
guistic approach with dierent classi ers (e.g. SVM, Random forests etc.) with
an accuracy of 93% [10]. Even though clickbait detection is not directly related
with fake news, they can give us a clue about the fake news. Of course this
does not mean that every clickbait headline refers to a fake news article. Some
research does not include the full text analysis of news articles. In [31] authors
suggest a method only by analyzing the headlines. Fake news detection on social
media might be a little bit more challenging due to the di culty of reaching the
original source of news and the noisy text. In [16] authors propose a method of
5
detecting fake news by exploiting the con icting information on Twitter. They
suggest that opposing viewpoints can help detecting fake news. Fake News Chal-
lenge
1 leverages the use of machine learning and dierent arti cial intelligence
techniques for fake news detection. The rst part of the challenge was about
stance detection in news. In [22] authors applied several neural network archi-
tectures and two novel architectural variations for the stance detection of fake
news challenge.
Non-textual content Online news articles usually include more than just the
text. Images, URLs, sound or video les are often available with the news text.
Also the metadata of existing elements, web tra c information, image captions
can be classi ed as non-textual cues since they are not the direct part of the
news article [11]. The analysis of such cues can give us interesting results in the
detection of fake news. Image analysis is one of the most used non-textual content
analysis. In [12] authors proposed an automatic image veri cation method for
online news articles which has almost %73 of accuracy.
3.2 User based methods
User based fake news detection methods depend on the idea that user behaviour
can give clues about the misinformation. Any kind of user interaction analysis
take place in this category. User comments which can be taken as a textual cue
without the identi cation of belonging to a particular user. But analyzing the
comments in a relation with particular users can be classi ed as a user based
method. In [30] authors detect the hoax posts on Facebook by analyzing the users
who liked them. Even though there are some research towards the unreliability
of people identi ying fake news [23], in [27] crowdsourcing was used to identify
the fake news.
3.3 Network based methods
Network based fake news detection methods include web tra c analysis, web
metadata analysis [11] as well as user network analysis. In [13] authors models
how some tweets get viral on Twitter by analyzing the social network graph of
users. In [9] authors model a social network as a directed weighted graph and
then calculate the probability of nodes transmitting information to each other
in order to contrast the spread of misinformation. They also mentioned the
source identi cation problem on a social network which addresses the problem
of identi ying the infected nodes on a network by misinformation. Also tracking
the news items to its original source can give us strong clues about the reliability
of news.
1
http://fakenewschallenge.org/
6
3.4 Hybrid methods
Sometimes two or more of the methods above can be used together in order
to achieve more accurate results in fake news detection. One example is [26]
where authors considered the three generally agreed upon characteristics of fake
news: Text, response received and source. They propose an architecture called
CSI (Capture, Score, Integrate). In this architecture recurrent neural networks
is used to capture news article representation, user behaviour over time is used
to score users and two previous outputs are integrated and the result is used for
classi cation.
4 Research Findings
Although there is limited work on automatic fake news detection, there are
some interesting preliminary results that may help us shape fake news detection
systems in the future:
The textual characteristics of fake news articles share many similarities with
satire news. Fake and real news are substantially dierent. [15]
News articles with a hyperpartisan world view was successfully distinguished
from more balanced news. Fake news detection is di cult via style analysis
alone. [21]
A linguistic approach with dierent classi ers (e.g. SVM, Random forests
etc.) managed to detect clickbaits in online news with an accuracy of 93%.
[10]
The diusion pattern of information can be useful for detecting the hoax.
[30]
Opposing viewpoints can help us detecting fake news, e.g. by analyzing con-
icting information on Twitter. [16]
Users are quite biased when it comes to detecting fake news personally. The
perception of what is fake and what is not can be very dierent from one
person to another, and fake news related keywords are often used to express
disagreement. [23]
5 Conclusions
In this paper we give a short state of the art towards an understanding of fake
news. Fake news is an important concept which may have serious real world
consequences. Even though the scope of fake news diers (ex: including satire
or rumors as fake news), the challenges exist for the automatic detection of mis-
information for all. The diusion mechanisms of fake news is an important step
towards understanding and preventing the spread of misinformation. The im-
portance of social media in the spread of fake news can not be underestimated.
Deeper understanding of human psychology on fake news could be helpful to de-
velop tools for detection and prevention of misinformation. The existing methods
7
for automatic fake news detection are mostly based on lingusitic and machine
learning techniques. In addition to these methods image analysis and crowd-
sourcing methods were applied. With the increasing popularity of the term fake
news, the research towards automatic detection also increases rapidly. The man-
ual fact checking done by professional journalists give the researchers opportunity
to understand the nature of misinformation and work more e ciently towards
the automatic detection of fake news.
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