Enhanced Fake News Detection using Prediction Algorithms

The advent of web-based media has provided a platform for individuals to express their opinions freely, facilitating easier communication compared to the past. This convenience comes with the risk of deliberate dissemination of misinformation. The ready access to a plethora of online information sources exacerbates the problem, as people may be exposed to and potentially believes false information. It is crucial for us to be aware of and flag such content on social media platforms. Given the rapid generation of information through online media, distinguishing between authentic news and fabrications is challenging without knowing the news source. The spread of fake news can have serious consequences, emphasizing the need for measures to reduce and differentiate between genuine and false messages. Term Frequency measures how often a word appears in a document, indicating its relevance. Inverse Document Frequency assesses the significance of a term in the entire corpus, helping filter out irrelevant words. An examination of spam detection is conducted by employing diverse classifiers, including BERT and RoBERTa to evaluate accuracy and furnish comparative findings.