Twitter spam needs to be detected and arrested quickly. The paper examines methods for classification of spam in terms of determination of important features, comparative performance of classification models, and improvement in time performance for classification. It presents a conceptualization of several novel rich, deep, and naïve features. The extraction processes for rich and deep features increase the time complexity of spam classification. To address this, the proposed model selectively segregates and combines features to enable near real-time processing. This supersedes the time performance of standard machine learning and deep learning models, with no compromise on the quality of classification.

中文翻译：

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用于 Twitter 垃圾邮件快速分类的集成深度学习模型


需要快速检测和阻止 Twitter 垃圾邮件。本文从确定重要特征、分类模型的比较性能以及分类时间性能的改进方面研究了垃圾邮件的分类方法。它提出了几个新颖的丰富、深刻和天真的特征的概念化。丰富和深入特征的提取过程增加了垃圾邮件分类的时间复杂度。为了解决这个问题，所提出的模型选择性地分离和组合特征，以实现近乎实时的处理。这取代了标准机器学习和深度学习模型的时间性能，而不会影响分类质量。