Twitter Spam Detection Using Natural Language Processing by Encoder Decoder Model

With the increasing demand of social life in today's world one famous platform known to be as Twitter plays important role for every citizen to connect socially, whether in the form of tweeting a tweet for other person or exploring various fields in the running world. But this platform, now a days gets infected by some spammers, with the intention of increasing traffic in their spam web sites they connect their URL with the informative tweet where there is no relation between the content present in URL and the tweet message, these are called spammed tweets. This paper provides a unique approach to detect whether the tweet mentioned by the user is spam or not spam using the encoder decoder technique used with vectorizer converter on the mentioned tweets and on its linked URL, which leads to the prediction of similarity score between them.