Encoder-Decoder Model For Detecting Spammed Tweets on Twitter

Accompanied by the desire for communal engagement expanding in nowadays high society, a well-known social media website called Twitter plays a vital role in enabling every citizen to communicate socially, whether by tweeting a tweet on someone else’s behalf or researching many subjects in the running world. But these days, some spammers have infected this platform, connecting their URLs to informative tweets to drive traffic to their spam websites even in spite of no connection between the material in the URL and the tweet message. In this paper with the help of encoder-decoder methodology combined with a vectorizer converter on the stated threads and their connected URLs, this study offers a novel method to determine if a tweet written by the other person is spam or ham, which results in the finding of a similarized vectors between them.