@inproceedings{goyal-etal-2026-team,
    title = "Team Paradise at {\#}{SMM}4{H}-{H}ea{RD} 2026: Multi-Task Approaches for Social Media Health Mining",
    author = "Goyal, Dhruv  and
      Gupta, Ishita  and
      Bedi, Jatin",
    editor = "Lopez-Garcia, Guillermo  and
      Gonzalez-Hernandez, Graciela",
    booktitle = "Proceedings of the 11th Social Media Mining for Health Research and Applications ({SMM}4{H}-{H}ea{RD} 2026) Workshop and Shared Tasks",
    month = jul,
    year = "2026",
    address = "San Diego, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl-workshops/2026.smm4h-1.48/",
    pages = "305--308",
    ISBN = "979-8-89176-432-3",
    abstract = "We present Team Paradise{'}s systems for three tasks in the SMM4H-HeaRD 2026 shared task: multilingual adverse drug event detection (Task 1), influenza vaccine effectiveness estimation via two-subtask classification (Task 3), and opioid impact span extraction (Task 7). For Task 1, threshold-only ablation on XLMRoBERTa-large achieves a macro-F1 of 0.597, exceeding the field mean (0.547) by +0.050. For Task 3, a three-stage hybrid pipeline combining twitter-RoBERTa-base-2022 with rule-based post-processing achieves Micro-F1 0.8434 (Subtask 1: vaccination status) and 0.8936 (Subtask 2: test results). For Task 7, RoBERTa-large with CRF decoding and sliding-window inference obtains relaxed F1 0.60 despite severe train-test distributional shift Across tasks, we identify class imbalance, temporal ambiguity, and platform heterogeneity as central challenges."
}