Misleading and Ambiguous Factual Information Detection Using an Ensemble Classifier with Voting Average Approach

The alarming spread of fallacious reporting on the internet today is one of the biggest concerns that can be addressed. We have developed a reliable method for tackling this issue. In this paper, the authors introduce a robust approach built upon machine learning that uses the Random Forest algorithm to segregate real and fake news articles automatically, with maximal efficiency and accuracy. Our dataset comprised genuine and false messages that contained various language cues, contextual data as well as user engagement features. These variables are adopted by the Random Forest Classifier as its input attributes; therefore, it helps it in identifying patterns and relationships among information, so it can make more informed decisions. After going through intensive training using marked datasets, the Random Forest model has significantly improved its ability to differentiate between real and fraudulent messages thus becoming highly dependable. During the evaluation stage, another dataset was utilized to check how accurate it was in classifying unknown instances; this gave us overwhelming results. Our findings show how difficult it is to tell apart false communication and also point out ways we can develop dependable systems against the spreading of false news through online platforms.