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A Review of Fake News Detection Using Machine Learning Techniques

A Review of Fake News Detection Using Machine Learning Techniques

Introduction:

Fake news is one of the biggest discouragements in our digitally connected world. Fake news spreads at lightning-fast speed impacting millions of people in the form of clickbait, trigrams everyday [4]. Therefore, noticing fake news becomes a vital problem attracting huge research efforts. Detection of fake news from social media always creates a new challenge. It is written on social media to mislead readers. In the 2016 US presidential election, fake news propagated more on Facebook than authentic news. Fake news detection on social media has attracted politicians to researchers. The detection of fakenews on social media is very important because fake news can change the mindset of people or society or country. So, it is very important for those readers who read news on daily basis on social media to know whether the news is real or fake. So, they always try to read news from authenticating sites or authors.

Abstract:

The widespread use of social media has had a terrible impact on our society due to the spread of fake news. In particular, the author has seen the idea of the quality of fake news before its origin. Like the internet, publishers used false and misleading information to further their interest. People often get involved in social media as social media provides low cost, quick access, and fast spread of news. It has been seen for many years that fake news harms persons as well as society. So, the challenge of fake news detection arrived. Inappropriate news took place to attract people so the sender can start putting the rumor of news. This had led to a negative impact on people about social media for their news. This led to inconvenience for offline news as well because when people will too much depend on online platforms that will reduce the offline users. This survey deals with a review of existing machine learning algorithms Naïve Bayes, Convolutional Neural Network, LSTM, Neural Network, Support Vector Machine proposed for detecting and reducing fake news from different social media platforms like Facebook, whatsapp, twitter, etc. This review provides a comprehensive detail including data mining perspective, evaluation metrics, and representative datasheets.

Existing wok:

Report of Pew Research Center U.S.A. suggests that adults got around 70 percent of news from social media. With the news of Donald Trump as president, this information has led to an increase of 9 lakh and 60 thousand Facebook users. In this paper, linguistic features or visual features play their role. Moving onto network features it deals with diffusion networks as well as co-occurrence networks. So, the authors have achieved an accuracy of about 83 percent.

Disadvantage:

  • Difficult to understand

Proposed work:

This survey deals with a review of existing machine learning algorithms Naïve Bayes, Convolutional Neural Network, LSTM, Neural Network, Support Vector Machine proposed for detecting and reducing fake news from different social media platforms like Facebook, whatsapp, twitter, etc. This review provides a comprehensive detail including data mining perspective, evaluation metrics, and representative datasheets.

Advantage:

  • High performance

Algorithm: NLP, Linear Regression, KNN, SVM, LSTM, Artificial Neural Networking

Less time

System requirements:

  Software requirements:

  • Operating system   :   Windows.
  • Coding Language  :   Python.

Hardware components:

System                   :   Pentium IV 2.4 GHz or intel

Hard Disk              :   40 GB.

Floppy Drive         :   1.44 Mb.

Mouse                    :   Optical Mouse.

Ram                       :   512 Mb.

Conclusion: The review study discusses pioneering existing work in the field of false news detection. Machine learning-based classification algorithms play a very important role in the detection of fake news or rumors from social media, which is a very complicated and difficult process due to the diverse political, social and economic, and many other related factors. This review discusses various machine learning approaches such as NLP, Linear Regression, KNN, SVM, LSTM, Artificial Neural Networking, and many more.

March 14, 2022

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