Classification of Rumour Detection System in Online Social Media Platforms

Social networks produce a lot of multimedia content due to their ubiquitous availability. Social media platforms' openness and lack of limits make content more likely to be disseminated, regardless of its credibility. This method of spreading misleading information is common in breaking news situations. Rumors, or misinformation, can be very dangerous because they are based on unconfirmed evidence. Even while social media is widely used, its uncontrolled nature nearly always leads to rumours. This makes social media analytics on social media sites essential for studying this topic. This work aims to provide a complete overview of state-of-the-art datasets and methods for rumour identification, including classified and non-classified approaches. We also present questions and difficulties as a future direction for scholars in this discipline.