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International Research Journal of Engineering and Technology (IRJET)

e-ISSN: 2395-0056

Volume: 11 Issue: 05 | May 2024

p-ISSN: 2395-0072

www.irjet.net

Fake News Detection Using Machine Learning and Web scraping Ayisha Nidha P P1, Hridya Nanda K2, Ayisha Marva A3 ,Najna Nazir M K4 123Student, Dept. of Information Technology, KMCT College of Engineering, Kerala, India

4Assisstant Professor, Dept. of Information Technology, KMCT College of Engineering, Kerala, India

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Abstract - Technology which is getting updated day-by-day

inaccurate and deceptive information has had a substantial negative social and economic impact on a variety of industries, including healthcare and banking.

has made the work load of human more easier and effort less. Also, social media networks which were created for the purpose of recreation and socialization over internet has become an inevitable part of our life. Now a days, rather than entertainment, more often social media is used for checking out news feeds. But the trustworthiness of source and content of news that is being viewed and shared over the internet is a greater matter of concern. Because, pseudo news and hoaxes are spreading in a menacing manner like a pandemic which is yet to be sorted out. Without checking the reliability of source and without knowing whether the news is reel or real people used to share news which seems to be satisfactory, without realizing the fact that these incognizant acts may abate the power of fake news and may end up in a severe issue especially during the time of disaster. We have a set of trusted sites such as Google. In these sites, if we search for a real content relevant information will be provided. If we search for a fake content, mismatching and irrelevant information will be retrieved as output. In this application, the news to be checked is searched online and results are gathered through web scrolling i.e., from different sites or from different links of Google. Then, the content obtained through search and the content of the concerned news is compared. comparing and finding out similarities by understanding contents is not an easy task. For that, we use techniques like natural language processing and associated set of algorithms. If similarities are found then that would be a real news and if not, it would be a fake news. Thus, one can easily figure out fake news and break the chain of fake news from being shared over the internet globally.

The need to recognize fake news has never been more urgent. The credibility of the source and quality of news that is being viewed and shared online, however, is a greater reason for concern. Because misleading information and hoaxes are alarmingly proliferating like an uncontrollably expanding illness. In the past, people would spread news that seemed credible without checking to see if the source was reliable or if it was phony. They didn't realize, though, that these thoughtless acts could accelerate the spread of false information and result in major issues, especially during emergencies. As the United States got ready for Hurricane Irma in 2017, a lot of misleading information circulated online. This included a Facebook post that, according to the alert, incorrectly predicted the storm would approach Houston and provided a map with a 14-day forecast that was nine days longer than official forecasts. "The post had been shared over 36,000 times on Facebook when the national weather service publicly refuted the forecast on Twitter with in the same day." These instances actually give a clear-cut idea about the danger hidden within fake news and the impact created by the same. So, it is high time to “think before click” i.e., to fact check the news and its reliability before sharing. For the above-mentioned purpose, design an application to check whether a news is fake or real so that users can share reliable information which ensures protection from critical false news disasters. Our system identifies a news posted on the application as real or not. A set of trusted sites such as Google. In these sites, if we search for a real content relevant information will be provided. If search for a fake content, mismatching and irrelevant information will be retrieved as output. In this application, the news to be checked is searched online and results are gathered through web scrolling i.e., from different sites or from different links of Google. Then, the content obtained through search and the content of the concerned news is compared. Comparing and finding out similarities by understanding contents is not an easy task. For that, we use techniques like natural language processing and associated set of algorithms. If similarities are found then that would be a real news and if not, it would be a fake news.

Key Words: Fake News, false information, analysis, Machine Learning, Web Scrapping.

1.INTRODUCTION. The term "fake news" describes news that is manufactured, misleading, or false and in which the veracity of the comments, sources, or facts cited are not known. Throughout human history, misinformation, rumors, and gossip have all been forms of fake news. This fake news is disseminated over social media in an effort to maximize its efficacy. Social media is used by billions of people, but it also contains robots, or simply bots. These bots aid in the quicker spread of false information and increase its visibility on social media. The quick dissemination of false information has grown to be a global problem. The dissemination of

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