FACapsnet: A fusion capsule network with congruent attention for cyberbullying detection
FACapsnet: A fusion capsule network with congruent attention for cyberbullying detection
复制标题
DOI：
10.1016/j.neucom.2023.126253
复制
发表时间：
2023-04
期刊：
影响因子：
6
通讯作者：
Fan Wu;Bin Gao;Xiaoou Pan;Zelong Su;Yu Ji;Shutian Liu;Zhengjun Liu
中科院分区：
文献类型：
--
作者：
Fan Wu;Bin Gao;Xiaoou Pan;Zelong Su;Yu Ji;Shutian Liu;Zhengjun Liu
网络欺凌作为社交网络欺凌的延伸，严重影响了网络社会环境的安全，侵害了心理健康。采取适当的措施来检测在线欺凌评论至关重要。现有研究通常将整个内容分为网络欺凌和非网络欺凌。然而，他们没有充分利用多维特征的相互作用，并准确区分网络欺凌的类型。为了自动提取欺凌词的特征，并进一步更好地识别细粒度的网络欺凌类型，我们提出了一种融合胶囊网络与一致的注意力网络欺凌检测。在该算法中，一种新的相似性加权方案的基础上word2vec的设计软突出欺负功能的词嵌入。同时，为了充分利用提取的多个子空间特征的各自优势，本文构造了一种新的可扩展一致注意力，以平衡不同子空间表示之间复杂相关性的融合，并保持上下文特征的独立性。融合的功能迭代更新与动态路由聚合和生成细粒度的类别胶囊网络欺凌预测。在tweets网络欺凌基准上的一系列实验表明，我们的架构匹配或超过了比较基准模型的性能，大量实验的结果证明了不同策略的有效性。
As an extension of bullying on social networks, cyberbullying has seriously affected the security of the online social environment and infringed on mental health. Taking appropriate measures to detect online bullying reviews is crucial. Existing studies usually classify the whole content as cyberbullying and non-cyberbullying. However, they do not fully exploit the interaction of multi-dimensional features and precisely distinguish the types of cyberbullying. To automatically extract features of bullying words and further identify fine-grained types of cyberbullying better, we propose a fusion capsule network with congruent attention for cyberbullying detection. In the proposed algorithm, a novel similarity weighting scheme based on word2vec is designed to soft highlight bullying features in word embeddings. Meanwhile, to leverage the respective advantages of extracted multiple subspace features, we construct a novel extensible congruent attention to balance the fusion of complex correlations between different subspace representations and retain the independence of context features. The fused features are updated iteratively with dynamic routing to aggregate and generate fine-grained category capsules for cyberbullying prediction. A series of experiments on the tweets cyberbullying benchmark demonstrate that our architecture matches or exceeds the performance of the compared baseline models and the results of extensive experiments prove the effectiveness of different strategies.