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A Deep Neural Network Technique for Detecting Real-Time Drifted Twitter Spam
2022
Applied Sciences
The social network is considered a part of most user's lives as it contains more than a billion users, which makes it a source for spammers to spread their harmful activities. Most of the recent research focuses on detecting spammers using statistical features. However, such statistical features are changed over time, and spammers can defeat all detection systems by changing their behavior and using text paraphrasing. Therefore, we propose a novel technique for spam detection using deep neural
doi:10.3390/app12136407
fatcat:mmcb3a6655afpasqh2kvdiv3zi