Spam detection in social media using convolutional and long short term memory neural network

As the use of the Internet is increasing, people are connected virtually using social media platforms such as text messages, Facebook, Twitter, etc. This has led to increase in the spread of unsolicited messages known as spam which is used for marketing, collecting personal information, or just to offend the people. Therefore, it is crucial to have a strong spam detection architecture that could prevent these types of messages. Spam detection in noisy platform such as Twitter is still a problem due to short text and high variability in the language used in social media. In this paper, we propose a novel deep learning architecture based on Convolutional Neural Network (CNN) and Long Short Term Neural Network (LSTM). The model is supported by introducing the semantic information in representation of the words with the help of knowledge-bases such as WordNet and ConceptNet. Use of these knowledge-bases improves the performance by providing better semantic vector representation of testing words which earlier were having random value due to not seen in the training. Proposed Experimental results on two benchmark datasets show the effectiveness of the proposed approach with respect to the accuracy and F1-score.