A smart System for Fake News Detection Using Machine Learning
Abstract : Most of the smart phone users prefer to read the news via social media over internet. The news websites are publishing the news and provide the source of authentication.
The question is how to authenticate the news and articles which are circulated among social media like WhatsApp groups, Facebook Pages, Twitter and other micro blogs & social networking sites.
It is harmful for the society to believe on the rumors and pretend to be a 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.
This paper demonstrates a model and the methodology for fake news detection. With the help of Machine learning and natural language processing, it is tried to aggregate the news and later determine whether the news is real or fake using Support Vector Machine.
The results of the proposed model is compared with existing models. The proposed model is working well and defining the correctness of results upto 93.6% of accuracy
In the modern era where the internet is ubiquitous, everyone relies on various online resources for news. Together with the increase in the use of social media platforms like Whatsapp, Facebook, Twitter, etc. Rumors spread rapidly among millions of users within a short period.
With the Covid-19 spread worldwide, people are using this propaganda, and the citizens desperately need to know the news about the mysterious virus by spreading the fake rumor. The need for an hour is to stop the spread of articles in developing countries like India.
To stop this we have implemented with the help of Machine learning models and natural language processing technique, it is tried to aggregate the news and later it will predict whether the news is real or fake
due to the booming development of online social networks, fake news for various commercial and political purposes has been appearing in large numbers and widespread in the online world.
With deceptive words, online social network users can get infected by these online fake news easily, which has brought about tremendous effects on the offline society already
An important goal in improving the trustworthiness of information in online social networks is to identify the fake news timely.
This Project comes up with the three applications of Natural Language Processing techniques for detecting 'fake news, that is, misleading news stories that come from unknown sources. Only by building a model based on a count vectorizer or a TF-IDF matrix, can only get you so far.
But these models don’t consider the important qualities like word ordering and context. This process will result in feature extraction; we propose using Python sci-kit-learn library to perform extraction of text data because this library contains useful tools like Count Vectorizer, Tf-IDF Vectorizer, and Hashing Vectorizer. Then, we will perform the training model and then classify with the user input then it will obtain the highest accuracy and precision according to confusion matrix results.
News Feed ranks reduce the prevalence of false news content.
Determine what is valuable and what is not. Stories that are flagged as false by our community than might show up lower in the user feed
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