Improving Stance Detection by Leveraging Measurement Knowledge from Social Sciences: A Case Study of Dutch Political Tweets and Traditional Gender Role Division

TL;DR

This paper enhances stance detection on Dutch political tweets about gender roles by integrating social science measurement tools, demonstrating improved accuracy in classifying political attitudes.

Contribution

It introduces the novel use of a validated social science survey instrument to improve stance detection in social media texts.

Findings

Using a social science measurement instrument improves stance detection accuracy.

Application to Dutch political tweets shows practical effectiveness.

Highlights the benefit of interdisciplinary approaches in NLP tasks.

Abstract

Stance detection concerns automatically determining the viewpoint (i.e., in favour of, against, or neutral) of a text's author towards a target. Stance detection has been applied to many research topics, among which the detection of stances behind political tweets is an important one. In this paper, we apply stance detection to a dataset of tweets from official party accounts in the Netherlands between 2017 and 2021, with a focus on stances towards traditional gender role division, a dividing issue between (some) Dutch political parties. To implement and improve stance detection of traditional gender role division, we propose to leverage an established survey instrument from social sciences, which has been validated for the purpose of measuring attitudes towards traditional gender role division. Based on our experiments, we show that using such a validated survey instrument helps to improve stance detection performance.