Hybrid ensemble framework with self-attention mechanism for social spam detection on imbalanced data

Cybercriminals use social media platforms to disseminate spam, misleading facts, fake news, and malicious links. Blocking such deceptive social media spam is essential. However, extracting relevant features from social networks is challenging due to privacy and time constraints. Traditional frequency-based word representation techniques are time-consuming and inefficient in producing contextual word vectors. Word embeddings and deep learning models have recently shown good results in text classification. Also, most existing approaches assumed balanced class distribution, which is false for most real-world datasets. In this paper, an attempt is made to advance the performance of the social spam detection system by leveraging dataset balancing, advanced word embedding techniques, machine learning, and deep learning approaches with the self-attention mechanism. In the proposed framework, the datasets are balanced using NearMiss and SmoteTomek techniques to feed several machine-learning models. Later, the baseline ML models and proposed voting-based ensemble models are evaluated on imbalanced and balanced datasets. For the proposed deep learning-based hybrid approaches, embeddings are generated using GloVe and FastText word embeddings on the balanced combined dataset and passed into the deep neural network comprised of Conv1D and Bi-directional recurrent neural network layers with the self-attention mechanism for improved context understanding and effective results. This study examines hybrid approaches for detecting social spam using imbalanced social network data and picks the optimum combination. Besides, Machine learning ensembles, word embeddings, deep learning with hyper-parameter optimization, and a self-attention method are compared thoroughly. Experiments and comparisons with other techniques show that the proposed hybrid framework with deep learning-based approaches achieves better performance.