Academic spam messages are unsolicited e-mails that professors, researchers, and other academics receive in their inboxes. Their purpose is to make money by charging fees to submit the receiver’s article in their journal, to promote commercial journals or payable conferences. An inexperienced receiver might think that messages of this kind are well-intended and flattering when in reality answering them could have devastating consequences in the receiver’s career. In this thesis, we developed an academic spam filter for Gmail that labels academic spam messages in the user’s inbox. We tested different classification models for classifying academic spam and used neural networks in combination with word to vector embedding in our final system since it has shown to be the most effective.Users can also update the classifier in our system based on the academic spam messages that they have received in the past. We compared our model with the existing methods of spam classification and confirmed that it is comparable or even better than them.
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