  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.
