Predicting personality traits based on online posts has emerged as an important task in many fields such as social network analysis. One of the challenges of this task is assembling information from various posts into an overall profile for each user. While many previous solutions simply concatenate the posts into a long document and then encode the document by sequential or hierarchical models, they introduce unwarranted orders for the posts, which may mislead the models. In this paper, we propose a dynamic deep graph convolutional network (D-DGCN) to overcome the above limitation. Specifically, we design a learn-to-connect approach that adopts a dynamic multi-hop structure instead of a deterministic structure, and combine it with a DGCN module to automatically learn the connections between posts. The modules of post encoder, learn-to-connect, and DGCN are jointly trained in an end-to-end manner. Experimental results on the Kaggle and Pandora datasets show the superior performance of D-DGCN to state-of-the-art baselines. Our code is available at https://github.com/djz233/D-DGCN.

翻译：基于在线帖子预测人格特质已成为社会网络分析等领域的重要任务。该任务的挑战之一在于需将来自不同帖子的信息整合为每个用户的整体画像。尽管先前诸多方法将帖子简单拼接为长文档，并通过序列或层级模型进行编码，但这些方法引入了不必要的帖子顺序，可能导致模型产生误导。本文提出动态深度图卷积网络（D-DGCN）以克服上述局限。具体而言，我们设计了一种采用动态多跳结构替代确定性结构的可学习连接方法，并将其与DGCN模块结合，以自动学习帖子间的关联。帖子编码器、可学习连接模块及DGCN三者以端到端方式联合训练。在Kaggle与Pandora数据集上的实验结果表明，D-DGCN相较于现有最优基线模型展现出更优越的性能。代码已开源：https://github.com/djz233/D-DGCN。