Adversarial Learning-based Stance Classifier for COVID-19-related Health Policies

TL;DR

This paper introduces an adversarial learning-based stance classifier that leverages external policy descriptions and regional background factors to improve COVID-19 policy attitude detection on social media, especially in cross-target and zero-shot scenarios.

Contribution

It proposes a novel adversarial learning framework incorporating external knowledge and regional context to enhance stance detection for emerging health policies.

Findings

Achieves state-of-the-art performance in cross-target stance detection.

Effectively generalizes to unseen health policies with limited labeled data.

Outperforms baseline models in zero-shot settings.

Abstract

The ongoing COVID-19 pandemic has caused immeasurable losses for people worldwide. To contain the spread of the virus and further alleviate the crisis, various health policies (e.g., stay-at-home orders) have been issued which spark heated discussions as users turn to share their attitudes on social media. In this paper, we consider a more realistic scenario on stance detection (i.e., cross-target and zero-shot settings) for the pandemic and propose an adversarial learning-based stance classifier to automatically identify the public's attitudes toward COVID-19-related health policies. Specifically, we adopt adversarial learning that allows the model to train on a large amount of labeled data and capture transferable knowledge from source topics, so as to enable generalize to the emerging health policies with sparse labeled data. To further enhance the model's deeper understanding, we incorporate policy descriptions as external knowledge into the model. Meanwhile, a GeoEncoder is designed which encourages the model to capture unobserved background factors specified by each region and then represent them as non-text information. We evaluate the performance of a broad range of baselines on the stance detection task for COVID-19-related health policies, and experimental results show that our proposed method achieves state-of-the-art performance in both cross-target and zero-shot settings.