Social networks are critical sources for event detection thanks to the characteristics of publicity and dissemination. Unfortunately, the randomness and semantic sparsity of the social network text bring significant challenges to the event detection task. In addition to text, time is another vital element in reflecting events since events are often followed for a while. Therefore, in this article, we propose a novel method named Multi-View Graph Attention Network (MVGAN) for event detection in social networks. It enriches event semantics through both neighbor aggregation and multi-view fusion in a heterogeneous social event graph. Specifically, we first construct a heterogeneous graph by adding the hashtag to associate the isolated short texts and describe events comprehensively. Then, we learn view-specific representations of events through graph convolutional networks from the perspectives of text semantics and time distribution, respectively. Finally, we design a hashtag-based multi-view graph attention mechanism to capture the intrinsic interaction across different views and integrate the feature representations to discover events. Extensive experiments on public benchmark datasets demonstrate that MVGAN performs favorably against many state-of-the-art social network event detection algorithms. It also proves that more meaningful signals can contribute to improving the event detection effect in social networks, such as published time and hashtags.

中文翻译：

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MVGAN：用于社交事件检测的多视图图注意网络

由于宣传和传播的特点，社交网络是事件检测的重要来源。不幸的是，社交网络文本的随机性和语义稀疏性给事件检测任务带来了重大挑战。除了文本之外，时间是反映事件的另一个重要因素，因为事件通常会被跟踪一段时间。因此，在本文中，我们提出了一种名为多视图图注意网络（MVGAN）的新方法，用于社交网络中的事件检测。它通过异构社交事件图中的邻居聚合和多视图融合来丰富事件语义。具体来说，我们首先通过添加主题标签来构建异构图，以关联孤立的短文本并全面描述事件。然后，我们分别从文本语义和时间分布的角度通过图卷积网络学习事件的特定视图表示。最后，我们设计了一种基于标签的多视图图注意机制，以捕获不同视图之间的内在交互，并整合特征表示以发现事件。在公共基准数据集上进行的大量实验表明，MVGAN 优于许多最先进的社交网络事件检测算法。它还证明了更有意义的信号可以有助于提高社交网络中的事件检测效果，例如发布时间和主题标签。我们设计了一种基于标签的多视图图注意机制，以捕获不同视图之间的内在交互，并整合特征表示以发现事件。在公共基准数据集上进行的大量实验表明，MVGAN 优于许多最先进的社交网络事件检测算法。它还证明了更有意义的信号可以有助于提高社交网络中的事件检测效果，例如发布时间和主题标签。我们设计了一种基于标签的多视图图注意机制，以捕获不同视图之间的内在交互，并整合特征表示以发现事件。在公共基准数据集上进行的大量实验表明，MVGAN 优于许多最先进的社交网络事件检测算法。它还证明了更有意义的信号可以有助于提高社交网络中的事件检测效果，例如发布时间和主题标签。