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• W2900547346 abstract "As the social networking sites get more popular, spammers target these sites to spread spam posts. Twitter is one of the most popular online social networking sites where users communicate and interact on various topics. Most of the current spam filtering methods in Twitter focus on detecting the spammers and blocking them. However, spammers can create a new account and start posting new spam tweets again. So there is a need for robust spam detection techniques to detect the spam at tweet level. These types of techniques can prevent the spam in real time. To detect the spam at tweet level, often features are defined, and appropriate machine learning algorithms are applied in the literature. Recently, deep learning methods are showing fruitful results on several natural language processing tasks. We want to use the potential benefits of these two types of methods for our problem. Toward this, we propose an ensemble approach for spam detection at tweet level. We develop various deep learning models based on convolutional neural networks (CNNs). Five CNNs and one feature-based model are used in the ensemble. Each CNN uses different word embeddings (Glove, Word2vec) to train the model. The feature-based model uses content-based, user-based, and n-gram features. Our approach combines both deep learning and traditional feature-based models using a multilayer neural network which acts as a meta-classifier. We evaluate our method on two data sets, one data set is balanced, and another one is imbalanced. The experimental results show that our proposed method outperforms the existing methods." @default.
• W2900547346 created "2018-11-29" @default.
• W2900547346 creator A5018965092 @default.
• W2900547346 creator A5087152480 @default.
• W2900547346 date "2018-12-01" @default.
• W2900547346 modified "2023-10-06" @default.
• W2900547346 title "A Neural Network-Based Ensemble Approach for Spam Detection in Twitter" @default.
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• W2900547346 doi "https://doi.org/10.1109/tcss.2018.2878852" @default.
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