Issue Framing in Online Discussion Fora

TL;DR

This paper introduces a new annotated corpus for issue framing in online discussions and investigates transfer learning techniques to detect issue frames across different online platforms.

Contribution

It presents a novel annotated corpus for issue framing in online fora and explores transfer learning methods to adapt models to this domain using unlabeled data.

Findings

Transfer learning improves issue frame detection in online fora.

Multi-task and adversarial training enhance cross-domain model transfer.

The corpus enables better understanding of issue framing in online discussions.

Abstract

In online discussion fora, speakers often make arguments for or against something, say birth control, by highlighting certain aspects of the topic. In social science, this is referred to as issue framing. In this paper, we introduce a new issue frame annotated corpus of online discussions. We explore to what extent models trained to detect issue frames in newswire and social media can be transferred to the domain of discussion fora, using a combination of multi-task and adversarial training, assuming only unlabeled training data in the target domain.