Fake News Detection using Machine Learning
ABSTARCT :
In recent years, 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 paper aims at investigating the principles, methodologies and algorithms for detecting fake news articles, creators and subjects from online social networks and evaluating the corresponding performance.
Information preciseness on Internet, especially on social media, is an increasingly important concern, but web-scale data hampers, ability to identify, evaluate and correct such data, or so called "fake news," present in these platforms.
In this paper, we propose a method for "fake news" detection and ways to apply it on Facebook, one of the most popular online social media platforms.
This method uses Naive Bayes classification model to predict whether a post on Facebook will be labeled as real or fake. The results may be improved by applying several techniques that are discussed in the paper.
Received results suggest, that fake news detection problem can be addressed with machine learning methods.
EXISTING SYSTEM :
With the advancement of technology, digital news is more widely exposed to users globally and contributes to the increment of spreading and disinformation online. Fake news can be found through popular platforms such as social media and the Internet.
There have been multiple solutions and efforts in the detection of fake news where it even works with tools. However, fake news intends to convince the reader to believe false information which deems these articles difficult to perceive. The rate of producing digital news is large and quick, running daily at every second, thus it is challenging for machine learning to effectively detect fake news
DISADVANTAGE :
One of the primary challenges is obtaining high-quality labeled data for training machine learning models. Labeling news articles as fake or genuine requires significant human effort and expertise.
Moreover, the definition of "fake news" can be subjective and may vary depending on cultural, political, or social contexts, making it challenging to create a universally applicable training dataset.
The datasets used for training machine learning models may themselves be biased, reflecting existing biases in the sources from which the data was collected or in the labeling process.
PROPOSED SYSTEM :
The proposed system of this project is to examine the problems and possible significances related with the spread of fake news. We will be working on different fake news data set in which we will apply different machine learning algorithms to train the data and test it to find which news is the real news or which one is the fake news.
As the fake news is a problem that is heavily affecting society and our perception of not only the media but also facts and opinions themselves. By using the artificial intelligence and the machine learning, the problem can be solved as we will be able to mine the patterns from the data to maximize well defined objectives. So, our focus is to find which machine learning algorithm is best suitable for what kind of text dataset. Also, which dataset is better for finding the accuracies as the accuracies directly depends on the type of data and the amount of data. The more the data, more are your chances of getting correct accuracy as you can test and train more data to find out your results
So, our focus is to find which machine learning algorithm is best suitable for what kind of text dataset. Also, which dataset is better for finding the accuracies as the accuracies directly depends on the type of data and the amount of data. The more the data, more are your chances of getting correct accuracy as you can test and train more data to find out your results
ADVANTAGE :
the goal is profiting through clickbaits. Clickbaits lure users and entice curiosity with flashy headlines or designs to click links to increase advertisements revenues.
This exposition analyzes the prevalence of fake news in light of the advances in communication made possible by the emergence of social networking sites.
The purpose of the work is to come up with a solution that can be utilized by users to detect and filter out sites containing false and misleading information.
We use simple and carefully selected features of the title and post to accurately identify fake posts. The experimental results show a 99.4% accuracy using logistic classifier
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