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 r...

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Bibliographic Details
Published in:2023 3rd International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE) pp. 174 - 178
Main Authors: Krishna, N. Bala, Reddy, Kandula Sainath, Krishnan, M. B. Mukesh, Sai, Katari, Chowdary, Karjala Harsha Vardhan, Akhil, Kande Venkat
Format: Conference Proceeding
Language:English
Published: IEEE 12.05.2023
Subjects:
Blogs
Computational modeling
Encoder-Decoder
Feature Extraction
Machine Learning
Message systems
Natural Language Processing
Social networking (online)
Twitter Spam Detection
Uniform resource locators
Word Embedding
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Summary: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.
DOI:10.1109/ICACITE57410.2023.10183140