Fake Manipulation Detection in Social Network (Fact-Hive)

Fake manipulation detection is most important integration in current era of social media, when everybody can create and spread misinformation in a very short time. In relation to fake news detection, sentiment analysis for a given text means the identification and sentiment categorization of the emotional tone expressed in such content. This is based on the assumption that fake news tends to contain language that misleads deliberately or was sensationalistic in nature and can thus be detected through sentiment analysis. The enhanced User Experience and User Interface with trained modules of machine learning dataset is used for clear output Though capable of detecting certain patterns indicative of fake news, sentiment analysis methods are not perfect techniques for spotting fake news stories that are written in a more tempered language or designed to make a person feel good. Moreover, Natural Language Process-NLP is implemented for text classification based on graphical representation and Support Vector Machine. Functional We implement the Decision Tree Algorithm and Random Forest, Python Flask for integrating the website to the backend. Further, the accuracy of the Sentiment Analysis may get affected by various aspects like sarcasm, irony, cultural differences in used language. Also, the product will include three major detection frameworks to ensure nations' safety. Therefore, the menace of fake resource needs to be fought with solutions that include fact-checking and source verification.