HarmPot: An Annotation Framework for Evaluating Offline Harm Potential
  of Social Media Text
Ritesh Kumar Ojaswee Bhalla Madhu Vanthi Shehlat Maknoon Wani Siddharth Singh
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.