Identifying Morality Frames in Political Tweets using Relational Learning

TL;DR

This paper introduces a new framework and dataset for analyzing moral attitudes in political tweets, using relational learning to jointly predict moral foundations and targeted entities, revealing ideological differences.

Contribution

It presents a novel morality frames representation, a high-quality annotated dataset of political tweets, and a relational learning model for joint prediction of moral attitudes and entities.

Findings

Moral sentiment varies significantly across political ideologies.

The relational learning model outperforms baseline methods.

The dataset enables detailed analysis of moral attitudes in political discourse.

Abstract

Extracting moral sentiment from text is a vital component in understanding public opinion, social movements, and policy decisions. The Moral Foundation Theory identifies five moral foundations, each associated with a positive and negative polarity. However, moral sentiment is often motivated by its targets, which can correspond to individuals or collective entities. In this paper, we introduce morality frames, a representation framework for organizing moral attitudes directed at different entities, and come up with a novel and high-quality annotated dataset of tweets written by US politicians. Then, we propose a relational learning model to predict moral attitudes towards entities and moral foundations jointly. We do qualitative and quantitative evaluations, showing that moral sentiment towards entities differs highly across political ideologies.