A Cross-Field Deep Learning-Based Fuzzy Spamming Detection Approach via Collaboration of Behavior Modeling and Sentiment Analysis

Intelligent detection techniques for online spamming have been a hot concern in academia. Although much technical progress has been achieved in recent years, two aspects of challenges are still confronted by scholars. For one thing, spamming activities are accompanied by multisource attributes, such as behaviors and semantics. For another, spamming is a cross-platform activity, where multiple platforms are exploited simultaneously to expand the influential reach. The above circumstances actually make spamming detection tend to become a fuzzy detection task. Existing works typically consider one-sided attribute and lack cross-platform multifeature fusion, which limiting the effectiveness of detection. To handle the current challenges, this article proposes a cross-field deep learning-based fuzzy spamming detection approach via the collaboration of behavior modeling and sentiment analysis. First of all, a cross-field deep learning-based technical framework is put forward to implement multisource feature fusion from mixed context. It first extracts multisource features from single fields and then integrates them into a hybrid-field feature space. In addition, three cross-field datasets based on real-world social network datasets are constructed, and utilized in the evaluation of our proposed approach. The findings demonstrate that our proposal improves the detection accuracy by about 7% to 12%, in comparison to five other baseline approaches.