@inproceedings{kumar-etal-2024-harmpot,
    title = "{H}arm{P}ot: An Annotation Framework for Evaluating Offline Harm Potential of Social Media Text",
    author = "Kumar, Ritesh  and
      Bhalla, Ojaswee  and
      Vanthi, Madhu  and
      Wani, Shehlat Maknoon  and
      Singh, Siddharth",
    editor = "Calzolari, Nicoletta  and
      Kan, Min-Yen  and
      Hoste, Veronique  and
      Lenci, Alessandro  and
      Sakti, Sakriani  and
      Xue, Nianwen",
    booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.lrec-main.706",
    pages = "8016--8034",
    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.",
}

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