A Brief Survey on Content-based Fake News Detection
Keywords:
Fake News Detection, Content Based, Machine Learning, Natural Language Processing (NLP), Propagation-based, TF-IDFAbstract
The increase in spread of fake news causes destruction of democracy and public confidence, which has boosted the demand for reliable fake news identification dramatically. Recent developments in this area have opened up innovative approaches for identifying fake news by looking at how it travels on social media. However, in order to spot false news early, one has no information about news distribution because at an early-stage, fake news can only be created and later dissemination happens. Also, the speed and amount at which news is created and propagated online is destructive in nature. As a result, there is an urgent need to create methods for detecting false news just based on news content and eliminate the danger before it reaches the mass of people. This study is divided into five sections. We have an introduction where we describe the topic and the motivations behind this survey. Fundamental Theories is the second section where we discuss the current theories that are used to detect fake news. In the third section, we briefly touched upon the advantages of utilizing content-based detection. In the fourth section, we conducted a survey on content-based fake news detection and listed the advantages and disadvantages of the available techniques. We closed the study with a discussion of some potential topics to investigate further.
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