Information overloading became a well identified challenge ever since the advent of social media, and the information overloading led to the second birth of rumors. Rumors come in different kinds and significance. So, it became one of the priorities to try and resolve this expanding issue and inhibit its growth amongst social media users as it can jeopardize the people. The main task of rumor detection is to clarify and classify whether the rumor is true, false or yet to be known and also, detect the source of the rumor, which can benefit the society as a whole from the spread of incorrect information and prevent the spread of panic, fear or hate. The Rumor detection is going to use Machine Learning and Deep Learning as its base. The whole process should pass through multiple phases including data collection, preprocessing, feature extraction and classifying.

• Detecting the credibility of the news whether it is real or not while giving the user a percentage that is the result of the model.

• Giving references to the credible websites that affected the results.

• Helping people reaching the truth and not believing rumors.

• Reduce the believing of rumors from many people so reducing disasters caused by this news.

• Trace the rumor and pin it in a black list and pin the sources that post real news to whitelist.

• Calculate the spreading of fake news.

• Can classify the English and Arabic text to rumors or not.

• Displaying our system as a website for users.

• Offering a separate profile for each user.

• Extracting the tweet features itself when the user enter the text.

• Detecting if the tweets are rumors or not and Displaying the source.

• splitting the sources into black or white list.