Abstract
With the current usage of social media platforms, consumers are creating and sharing more information than ever before, some of which are misleading with no relevance to reality. Automated classification of a text article as misinformation or disinformation is a challenging task. Some of the information converted is misleading because it is not authorized. Even an expert in a particular domain has to explore multiple aspects before giving a verdict on the truthfulness of an article. In this work, we propose to use machine learning Tree classification model for detecting fake news.
Introduction
The adoption of Information over social media platforms such Facebook, twitter, telegram has recently increased. others have used news from magazines and posted it on there social platforms to gain more subscribers by converting the news from the magazines ,newspapers to be digital media formats. Facebook is the most used social media platform. This is because many people can discuss challenges affecting the world like; our economy , racism, nepotism, patriotism, democracy. However, such platform can also be used with a negative perspective like, monetary gain, creating biased opinions, manipulating mindsets, and spreading satire or absurdity. And this is what we refer to us as fake news.
Sharing articles online that do not confirm to facts that has led to many problems like , covering other topics such as sports , finance ,and also science instead is limited to politics only. And finance is the most affected area with fake news.
According to research many consumers of information have been responding to fake news. One recent case is the spread of corona virus, where fake reports spread over the Internet about the origin, nature, and behavior of the virus . The situation worsened as more people read about the fake contents online.
Fortunately, there are a number of computational techniques that can be used to mark certain articles as fake on the basis of their textual content . Majority of these techniques use fact checking websites such as “Politic Fact” and “Snopes.” There are a number of repositories maintained by researchers that contain lists of websites that are identified as ambiguous and fake . However, the problem with these resources is that human expertise is required to identify articles/websites as fake.
Contributions
Our study explores different textual properties that could be used to distinguish fake contents from real. By using those properties, we train a combination of different machine learning algorithms using various ensemble methods that are not thoroughly explored in the current literature. The ensemble learners have proven to be useful in a wide variety of applications, as the learning models have the tendency to reduce error rate by using techniques such as bagging and boosting.
Framework
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