Graph-based Joint Pandemic Concern and Relation Extraction on Twitter

TL;DR

This paper introduces a novel deep learning model combining Graph Convolutional Networks and Bi-LSTM with Concern Graphs to detect public concerns and their relations on Twitter, especially effective with limited labeled data.

Contribution

The paper presents an end-to-end model integrating GCN, Bi-LSTM, and Concern Graphs for concern detection and relation extraction, addressing data scarcity and noise in social media analysis.

Findings

Outperforms state-of-the-art models on real-world datasets.

Effective in low-resource scenarios with limited labeled data.

High noise-tolerance due to regional feature extraction.

Abstract

Public concern detection provides potential guidance to the authorities for crisis management before or during a pandemic outbreak. Detecting people's concerns and attention from online social media platforms has been widely acknowledged as an effective approach to relieve public panic and prevent a social crisis. However, detecting concerns in time from massive information in social media turns out to be a big challenge, especially when sufficient manually labeled data is in the absence of public health emergencies, e.g., COVID-19. In this paper, we propose a novel end-to-end deep learning model to identify people's concerns and the corresponding relations based on Graph Convolutional Network and Bi-directional Long Short Term Memory integrated with Concern Graph. Except for the sequential features from BERT embeddings, the regional features of tweets can be extracted by the Concern Graph module, which not only benefits the concern detection but also enables our model to be high noise-tolerant. Thus, our model can address the issue of insufficient manually labeled data. We conduct extensive experiments to evaluate the proposed model by using both manually labeled tweets and automatically labeled tweets. The experimental results show that our model can outperform the state-of-art models on real-world datasets.