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About Statistical Twitter Spam Detection Demystified: Performance, Stability and Scalability
This document summarizes a research paper that evaluated machine learning algorithms for detecting spam on Twitter. It tested 9 algorithms on a dataset of 200,000 tweets to analyze their performance, stability, and scalability for spam detection. Deep learning algorithms achieved the best results, accurately detecting spam with high true positive rates. When run on increased computing resources, deep learning also showed the greatest ability to scale up processing and achieve real-time spam detection. The research helps understand which machine learning approaches are most effective and feasible for spam detection on social media platforms.
Detailed Information
Author: | ['Aakash Garg'] |
---|---|
Publication Year: | 2018 |
Pages: | 13 |
Language: | English |
Format: | |
Price: | FREE |
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