HarmPot: An Annotation Framework for Evaluating Offline Harm Potential of Social Media Text

TL;DR

This paper introduces HarmPot, an annotation framework designed to evaluate the offline harm potential of social media texts by capturing socio-political context, intent, and triggers, moving beyond hate speech or misinformation.

Contribution

HarmPot provides a novel schema for annotating social media content with socio-political and intent features to assess offline harm potential, filling a gap in existing frameworks.

Findings

Developed a comprehensive annotation schema for harm potential

Compared HarmPot with existing annotation frameworks

Demonstrated the framework's applicability to social media data

Abstract

In this paper, we discuss the development of an annotation schema to build datasets for evaluating the offline harm potential of social media texts. We define "harm potential" as the potential for an online public post to cause real-world physical harm (i.e., violence). Understanding that real-world violence is often spurred by a web of triggers, often combining several online tactics and pre-existing intersectional fissures in the social milieu, to result in targeted physical violence, we do not focus on any single divisive aspect (i.e., caste, gender, religion, or other identities of the victim and perpetrators) nor do we focus on just hate speech or mis/dis-information. Rather, our understanding of the intersectional causes of such triggers focuses our attempt at measuring the harm potential of online content, irrespective of whether it is hateful or not. In this paper, we discuss the development of a framework/annotation schema that allows annotating the data with different aspects of the text including its socio-political grounding and intent of the speaker (as expressed through mood and modality) that together contribute to it being a trigger for offline harm. We also give a comparative analysis and mapping of our framework with some of the existing frameworks.