INTRODUCTION
A large number of people are using the Internet as one of its most
important inventions. They use the Internet for various purposes. They
can access different social media platforms. Through these online
platforms, anyone can post or spread news. This platform does not verify
the users or their posts ( Ahmed Et al., 2021). This results in some
users spreading fake news. ’Fake news’ can be used as propaganda to
attack an individual, a society, an organization, or a political party
(Manzoor et al., 2019). All this fake news is impossible for a human to
detect. In order to identify fake news, algorithms must use machine
learning. This paper aims to construct a machine learning model that can
predict which Tweets are about real disasters and which ones are not.
The dataset contains 10,000 tweets that have been manually
classified.(al. & Al Ayub
Ahmed Et al., 2021)
In order to optimize revenue, the application helps organizations
predict which articles will be popular so that their targeted
advertising campaigns can be optimized. (An overview of Random Forest
Algorithm in Machine Learning, 2020) The Random Forest Algorithm is a
technique of making a classification by using a bundle of decision
trees. As well as avoiding overfitting, it is also considered to be an
effective technique. It reduces overfitting and helps improve accuracy
in decision trees. It can be applied to both classification and
regression problems. Continuous and categorical data can both be used.
Automatically fills in any missing values present in the data. As a
rule-based approach is used, there is no need to normalize data. While
random forest algorithms have many advantages, they also have some
disadvantages. These algorithms require time and resources with high
computational power as it builds numerous trees to combine their
outputs. As many decision trees are used to determine the class, it also
takes a lot of training time. Due to the number of decision trees, it is
also difficult to interpret and fails to determine the significance of
each variable. The definition of fake news generally refers to something
that is verifiably false and intentionally so. Bias news does not fall
under this definition. A biased report can be influenced by the
individual’s opinion, but fake news is fabricated intentionally. Their
high level of social engagement is one of the most important factors
behind the success of fake news stories. Facebook and Twitter give us
access to other like-minded individuals. Whenever we read a sentence or
a paragraph, our brains incorporate the information with the entire
document and understand what the words mean. We teach a system to read
and understand fake news with Machine Learning concepts (Shu and Liu
2019). The spread of false information and hoaxes online is on the rise
as a result of the advancement of technology. Popular online platforms
like social media and the Internet are popular sources of fake news.
Various methods and tools have been used to detect fake news, including
those that use artificial intelligence. Fake news, on the other hand,
aims to fool readers into believing false information, which makes these
articles difficult to comprehend. Machine learning cannot effectively
detect fake news because the rate of producing digital news is high and
runs at every second.
(Carlson 2017). Due to the
emergence of social networking sites, it has become possible to analyze
the prevaIn developing countries like India, it is important to stop
rumor-mongering and to focus on providing accurate, reputable news
articles. (Jain et al., 2019)lence of fake news using new communication
methods. This project aims to develop a method for detecting and
removing false or misleading information from websites that users can
access. By analyzing a few simple elements of the title and post, we can
tell whether a post is fake or not. Authenticating news and articles
appearing on social media sites such as WhatsApp groups, Facebook pages,
Twitter, and other blogs and social networks is a question. Society is
harmed when rumors are believed and are portrayed as news. The need of
an hour is to stop the rumors especially in the developing countries
like India, and focus on the correct, authenticated news
articles.(Jain et al. 2019)