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lutional neural networks. These methods only work
well when they have enough information about how
fake news is spreading. They are not good enough to
find fake news in its early stages. As a human, when
we are given a piece of information, we first use our
intuition to judge its factual correctness. At times, we
might also look for a reliable source to verify the in-
formation. This scenario motivates the importance of
publisher and user credibility in detecting fake news
in much earlier phases.
2 RELATED WORK
The detection of fake news on social media has drawn
a lot of attention in recent years. One of the main
goals of the studies that have already been conducted
is to create machine and deep learning-based clas-
sifiers that can automatically tell if a news article
spreading on social media is fake based on a number
of news features. Early research focused on finding
linguistic clues in news articles that could be used to
spot fake news. This section gives an overview of the
research that has been done on automatic extraction of
the features for spotting fake news and closely related
topics like spotting rumors or misinformation.
2.1 Analysis Related to News Content
Based Features
Many researchers use the simple method of just look-
ing at the news content to spot fake news. They
read the news article headlines, bodies of text, and
in some cases, related images and videos (Jin et al.,
2016). Some, such as (Gupta et al., 2014) counted
the number of swear words and words that contained
pronouns in order to create features to distinguish
fake news from real news. (Castillo et al., 2011)
adopted a list of content-based features, including
emoticons, pronouns, sentiment of words, and punc-
tuation marks, used to determine the veracity of news.
Based on writing styles, (Afroz et al., 2012) found
online fraud, deception, and hoaxes. They have used
things like assertive verbs, factive verbs, and implica-
tives to figure out how likely web claims are to be true.
These stylistic linguistic features can be easily manip-
ulated and do not convey semantic meaning. These
methods therefore have a lower likelihood of being
successful in practical applications. Content-based
detection methods (Sun et al., 2013) often have trou-
ble finding fake news because it comes in many differ-
ent forms, in many different ways, and on many dif-
ferent platforms. Additionally, news content features
may be event-specific. As a result, features based on
content that perform well on one dataset of fake news
may not perform well on another.
2.2 Analysis Related to Social Context
Based Features
Social interactions related to a news article are in-
cluded in social context features. They might reveal
information about whether a news article is accurate.
Some research has already been done on the ways that
social context is used to classify news. The most com-
mon types of social context features are based on the
user, on the text, and on the structure. User profiles
on social media, which show what kind of people use
social media, can be used to get information about
user-based features. (Castillo et al., 2011) used a list
of fundamental user-based features supported by var-
ious social media platforms, such as the number of
followers, friend count, and age of registration, to as-
sess the accuracy of information posted by its source
user. (Yang et al., 2012) added a few user features to
Sina Weibo, a Chinese social media platform, in ad-
dition to the typical user characteristics, such as gen-
der and registration area, to find rumors. Using only
user-based features to decide if a news article is fake
has a big drawback: people who make fake news of-
ten mix it with real news to make it more likely that
people will believe it. So, even if the news article
isn’t true, just looking at how people use a resource
doesn’t give us a full picture. Information on the
users who shared or retweeted a news article, how-
ever, may give us more insight into the authenticity
of a news article. However, this type of feature is
ignored by many existing studies. Text-based social
context features can be accessed through the com-
ments and discussions of social media users that show
up under news articles. A number of temporal-based
features extracted from the time series of user com-
ments and time-stamped on user comments are pro-
posed to detect false news. (Ma et al., 2015) used
a time series of content and features based on social
context, such as the percentage of microblogs with
URLs and the percentage of verified users, to tell the
difference between rumors and other types of content.
But these ”aggregated level” parts need a lot of sta-
tistical considerations in order to spot fake news as
soon as it comes out. Many deep learning techniques,
like RNN, are used by (Ma et al., 2016) to extract
temporal-linguistic patterns from user comment se-
quences in order to identify rumors. At the beginning
of the news propagation process, user responses may
be very limited, which can have a significant nega-
tive impact on the performance of RNN models and
lead to them becoming overfit. This is one of the
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