Orders Are Unwanted: Dynamic Deep Graph Convolutional Network for Personality Detection
This work addresses a challenge in social network analysis for personality detection, but it is incremental as it builds on existing graph-based methods.
The paper tackles the problem of personality detection from online posts by addressing the issue of unwarranted order in post concatenation, proposing a dynamic deep graph convolutional network (D-DGCN) that achieves superior performance on Kaggle and Pandora datasets compared to state-of-the-art baselines.
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.