A Neural Network-Based Ensemble Approach for Spam Detection in Twitter

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 contentbased, 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.