通过在异构图上联合建模用户、连接和多模态内容来理解政治极化
Understanding Political Polarization via Jointly Modeling Users, Connections and Multimodal Contents on Heterogeneous Graphs
摘要：
了解社交平台上的政治两极分化非常重要，因为当公众意见在同质社区中传播时，它们可能会变得越来越极端，从而可能在现实世界中造成损害。自动检测社交媒体用户的政治意识形态有助于更好地理解政治极化。然而，由于意识形态标签的稀缺性，多模态内容的复杂性，以及耗时的数据汇集过程的成本，这是具有挑战性的。在这项研究中，我们采用了一个异构的图神经网络，共同模型的用户特征，多模态后的内容以及用户项目的关系，在一个二分图学习一个全面和有效的用户嵌入，而不需要意识形态标签。我们将我们的框架应用于关于经济和公共卫生主题的在线讨论。然后，学习到的嵌入被用来检测政治意识形态并理解政治两极分化。我们的框架在两个社交媒体数据集上的接收器操作特性下的区域中的绝对增益至少为9%，优于单峰，早期/晚期融合基线和同质GNN框架。更重要的是，我们的工作不需要耗时的数据汇集过程，这可以更快地检测，从而使决策者能够及时进行分析和设计政策，以应对危机。我们还表明，我们的框架学习有意义的用户嵌入，可以帮助更好地理解政治极化。观察到用户描述、主题、图像和转发/引用活动水平的显著差异。我们的框架解码用户内容交互显示了广泛的适用性，在理解政治极化。此外，它可以扩展到用户项二分信息网络的其他应用，如内容和产品推荐。
英文摘要：
Understanding political polarization on social platforms is important as public opinions may become increasingly extreme when they are circulated in homogeneous communities, thus potentially causing damage in the real world. Automatically detecting the political ideology of social media users can help better understand political polarization. However, it is challenging due to the scarcity of ideology labels, complexity of multimodal contents, and cost of time-consuming data collection process. In this study, we adopt a heterogeneous graph neural network to jointly model user characteristics, multimodal post contents as well as user-item relations in a bipartite graph to learn a comprehensive and effective user embedding without requiring ideology labels. We apply our framework to online discussions about economy and public health topics. The learned embeddings are then used to detect political ideology and understand political polarization. Our framework outperforms the unimodal, early/late fusion baselines, and homogeneous GNN frameworks by a margin of at least 9% absolute gain in the area under the receiver operating characteristic on two social media datasets. More importantly, our work does not require a time-consuming data collection process, which allows faster detection and in turn allows the policy makers to conduct analysis and design policies in time to respond to crises. We also show that our framework learns meaningful user embeddings and can help better understand political polarization. Notable differences in user descriptions, topics, images, and levels of retweet/quote activities are observed. Our framework for decoding user-content interaction shows wide applicability in understanding political polarization. Furthermore, it can be extended to user-item bipartite information networks for other applications such as content and product recommendation.
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