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, semantics, etc. 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 multi-feature fusion, which limiting the effectiveness of detection. To handle the current challenges, this paper 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 firstly 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.

中文翻译：

---

VL-MFER：具有多路模糊专家双向保留网络的视觉语言多模态预训练模型


在线垃圾邮件的智能检测技术一直是学术界关注的热点。尽管近年来取得了很大的技术进步，但学者们仍然面临两个方面的挑战。一方面，垃圾邮件活动具有行为、语义等多源属性；另一方面，垃圾邮件是一种跨平台活动，同时利用多个平台来扩大影响范围。上述情况实际上使得垃圾邮件检测趋于成为一项模糊检测任务。现有的工作通常考虑片面属性，缺乏跨平台的多特征融合，限制了检测的有效性。为了应对当前的挑战，本文通过行为建模和情感分析的协作，提出了一种基于跨领域深度学习的模糊垃圾邮件检测方法。首先，提出了基于跨领域深度学习的技术框架，实现混合上下文的多源特征融合。它首先从单个字段中提取多源特征，然后将它们集成到混合字段特征空间中。此外，还构建了三个基于现实世界社交网络数据集的跨领域数据集，并将其用于评估我们提出的方法。研究结果表明，与其他五种基线方法相比，我们的建议将检测准确度提高了约 7% 至 12%。