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

Ritesh Kumar, Ojaswee Bhalla, Madhu Vanthi, Shehlat Maknoon Wani, Siddharth Singh


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.
Anthology ID:
2024.lrec-main.706
Volume:
Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
Month:
May
Year:
2024
Address:
Torino, Italia
Editors:
Nicoletta Calzolari, Min-Yen Kan, Veronique Hoste, Alessandro Lenci, Sakriani Sakti, Nianwen Xue
Venues:
LREC | COLING
SIG:
Publisher:
ELRA and ICCL
Note:
Pages:
8016–8034
Language:
URL:
https://aclanthology.org/2024.lrec-main.706
DOI:
Bibkey:
Cite (ACL):
Ritesh Kumar, Ojaswee Bhalla, Madhu Vanthi, Shehlat Maknoon Wani, and Siddharth Singh. 2024. HarmPot: An Annotation Framework for Evaluating Offline Harm Potential of Social Media Text. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 8016–8034, Torino, Italia. ELRA and ICCL.
Cite (Informal):
HarmPot: An Annotation Framework for Evaluating Offline Harm Potential of Social Media Text (Kumar et al., LREC-COLING 2024)
Copy Citation:
PDF:
https://preview.aclanthology.org/nschneid-patch-5/2024.lrec-main.706.pdf