Misinformation has significant adverse effects on individuals, society, and democratic processes. It undermines trust in reliable information sources, leading to confusion and misinformation. It contributes to the polarization of society by reinforcing existing biases and creating echo chambers. Misinformation can also incite fear, panic, and violence, especially concerning sensitive issues or false information about health and safety. Additionally, it can manipulate public opinion, influence elections, and undermine the integrity of democratic systems. This project proposes a three- fold solution to tackle the negative impacts of misinformation in India. Hence, a robust system needs to be implemented that can be used to curb thespread of this misinformation. Rather than creating just one centralized system, we tried to contribute tothe linguistic analysis aspect of misinformation detection which can be further integrated into systems that incorporate systems such as user trust scores and public fact-checking. In this project, we first create a synthetic data set that contains news specific to India. We have also created a telegram chatbot where the user can input the news and the bot will tell the user the probability of the news being true and additional information such as ClickbaitProbability, Sentiment Analysis, and Bias Percentage. Our website implementation leverages our dataset, Twitter API, and cosine similarity to show the user a score that shows how reliable the information is. By using these methods, we can curb the spread of misinformation over public platforms such as Twitter and even on public groups on platforms such as Telegram.