Directed Social Regard: Surfacing Targeted Advocacy, Opposition, Aid, Harms, and Victimization in Online Media

TL;DR

This paper introduces the Directed Social Regard (DSR) method, a transformer-based approach for multi-dimensional sentiment analysis that identifies sentiment targets and evaluates their social regard, addressing limitations of traditional sentiment tools.

Contribution

The paper develops a novel transformer-based model and dataset for detecting and scoring targeted sentiments along social axes, advancing nuanced sentiment analysis in online media.

Findings

DSR model effectively detects span-level sentiment targets.

DSR scores correlate with social science dataset labels.

Promising validation results on multiple online media datasets.

Abstract

The language in online platforms, influence operations, and political rhetoric frequently directs a mix of pro-social sentiment (e.g., advocacy, helpfulness, compassion) and anti-social sentiment (e.g., threats, opposition, blame) at different topics, all in the same message. While many natural language processing (NLP) tools classify or score a text's overall sentiment as positive, neutral, or negative, these tools cannot report that positive and negative sentiments coexist, and they cannot report the target of those sentiments. This paper presents the Directed Social Regard (DSR) approach to multi-dimensional, multi-valence sentiment analysis, comprised of a pair of transformer-based models that (1) detects span-level targets of sentiment in a message and then (2) scores all spans within the message context along three (-1, 1) axes of regard that are motivated by social science theories of moral disengagement and moral framing. We present a data collection and annotation strategy for DSR dataset construction, a transformer-based architecture for span-level scoring, and a validation study with promising results. We apply the validated DSR model on six third-party datasets of online media and report meaningful correlations between DSR outputs and the labels and topics in these pre-existing social science datasets.