Our inspiration comes from thousands of people who are given false beliefs or misled by fake news. I have heard stories of how people have seriously damaged their bodies from trying something that they later found out was fake. After hearing these horrific stories, I thought to myself “ how can I make sure this never happens again”

As we researched this topic we realized this issue is much more common than we thought. According to pew research, “only 35% of people have unknowingly shared fake news”. After reading about this, we wanted to make sure no one gets misled and eventually hurt from fake news again.

Thus we developed Rumor Reporter, an AI-based program explicitly designed to prevent people from being misled or hurt by fake news

What it does

Rumor Reporter is a cutting-edge web tool that helps people from being misled or hurt by fake news.

Rumor Reporter utilizes a passive-aggressive classifier and a tf-idf vectorizer, to identify significant signs of fake news and displays all this information in a user-friendly interface. Along with detecting fake news, Rumor Reporter provides users with sentiment analysis. Rumor Reporter returns if the text is positive, negative, or neutral. With this tool, users can know if the text is generally positive or negative.

How we built it

We divided our tasks into two main groups after about an hour of planning and brainstorming. Nilay focused mostly on the project's user interface and design, while Manit worked primarily on the machine learning model and backend.

Python is the best language to utilize because of its applications in machine learning, which is why we choose it as our language of choice. Rumor Reporter uses sci-kit learn because it is the lightweight backend web development library that has become synonymous with Python. This implies that when using Rumor Reporter, our users get a quick and simple experience. In order to make it simpler for our model to recognize fake news and prevent people from being misled, we also utilized a tf-idf vectorizer to perform sentiment analysis.

Additionally, to train our model and get it precise enough to meet our standards, we used thousands of different articles from different news sources around the world. This worked really well as we achieved a 99.5% accuracy on our model.

Challenges we ran into

We ran into some problems while trying to train our model to make it more accurate. Using many articles was really time-consuming but we needed to do it to increase our model’s accuracy. After countless hours of training our model, we finally reached the accuracy of 99.5%.

Accomplishments that we're proud of

We are really proud of ourselves for being able to design a project that would have the potential to significantly alter the lives of numerous individuals all over the world while still remaining distinctive. We are astounded at how much we were able to complete in such a short period of time and how well the final product came together.

What we learned

Throughout the course of this project, our team gained a great deal of knowledge regarding web development and machine learning. We gained knowledge of libraries like sci-kit learn and found some intriguing brand-new features in Python such as the passive aggressive classifier. We discovered how to employ computer vision to address problems in our neighborhood and how to incorporate our fixes into engaging applications.

What's next for Rumor Reporter

In the future for Rumor Reporter, we hope to develop a Mobile App so that you can check for fake news where ever you want. We also plan on adding more features to the program such as reporting the fake news article

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