Detection of fake news: A comparative analysis using machine learning.

In today's world, being the easy availability of the internet, almost everyone is connected. Every individual for their daily news relies on online resources, which greatly impacts their quality of life. The development of social media platforms has grown tremendously. However, many of these platforms are spreading fake news that pauses serious concern. It leads to a decrease in income for individuals or companies. Some people often create fake news for gaining fame, for election purposes, and for creating hatred towards other companies to reduce their revenue. All these can have negative consequences on society. Just by the use of a single click, many people forward appealing headlines, photos, etc. to gain the attention of an individual, without even checking whether it is fake or not. It is hard-core truth that many false websites give false news and checking their authenticity is really a challenge. This paper presents a comparative analysis of methods for the user to check the news whether that's real or fake by providing accuracy and recall using machine learning. The models used are naïve Bayes (NB), decision tree classifier (DT), random forest (RF), and logistic regression (LR) for analysis to detect fake news. The result shows that the DT classifier achieved 99.56% accuracy as compared to RF (99.35%), LR (98.91%), and NB (94.89%). The recall also shows better results for DT and RF than LR and NB. Lastly, it discusses some of the security threats and challenges faced while detecting fake news. [ABSTRACT FROM AUTHOR]