@inproceedings{khetan-etal-2023-semeval,
    title = "{S}em{E}val-2023 Task 8: Causal Medical Claim Identification and Related {PIO} Frame Extraction from Social Media Posts",
    author = "Khetan, Vivek  and
      Wadhwa, Somin  and
      Wallace, Byron  and
      Amir, Silvio",
    editor = {Ojha, Atul Kr.  and
      Do{\u{g}}ru{\"o}z, A. Seza  and
      Da San Martino, Giovanni  and
      Tayyar Madabushi, Harish  and
      Kumar, Ritesh  and
      Sartori, Elisa},
    booktitle = "Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.semeval-1.311",
    doi = "10.18653/v1/2023.semeval-1.311",
    pages = "2266--2274",
    abstract = "Identification of medical claims from user-generated text data is an onerous but essential step for various tasks including content moderation, and hypothesis generation. SemEval-2023 Task 8 is an effort towards building those capabilities and motivating further research in this direction. This paper summarizes the details and results of shared task 8 at SemEval-2023 which involved identifying causal medical claims and extracting related Populations, Interventions, and Outcomes ({``}PIO{''}) frames from social media (Reddit) text. This shared task comprised two subtasks: (1) Causal claim identification; and (2) PIO frame extraction. In total, seven teams participated in the task. Of the seven, six provided system descriptions which we summarize here. For the first subtask, the best approach yielded a macro-averaged F-1 score of 78.40, and for the second subtask, the best approach achieved token-level F-1 scores of 40.55 for Populations, 49.71 for Interventions, and 30.08 for Outcome frames.",
}

Identification of medical claims from user-generated text data is an onerous but essential step for various tasks including content moderation, and hypothesis generation. SemEval-2023 Task 8 is an effort towards building those capabilities and motivating further research in this direction. This paper summarizes the details and results of shared task 8 at SemEval-2023 which involved identifying causal medical claims and extracting related Populations, Interventions, and Outcomes ({``}PIO{''}) frames from social media (Reddit) text. This shared task comprised two subtasks: (1) Causal claim identification; and (2) PIO frame extraction. In total, seven teams participated in the task. Of the seven, six provided system descriptions which we summarize here. For the first subtask, the best approach yielded a macro-averaged F-1 score of 78.40, and for the second subtask, the best approach achieved token-level F-1 scores of 40.55 for Populations, 49.71 for Interventions, and 30.08 for Outcome frames.
Khetan, Vivek Wadhwa, Somin Wallace, Byron Amir, Silvio
SemEval-2023 Task 8: Causal Medical Claim Identification and Related PIO Frame Extraction from Social Media Posts