Share this page:

Event Detection from Social Media for Epidemic Prediction

Tanmay Parekh, Anh Mac, Jiarui Yu, Yuxuan Dong, Syed Shahriar, Bonnie Liu, Eric J. Yang, Kuan-Hao Huang, Wei Wang, Nanyun Peng, and Kai-Wei Chang, in NAACL, 2024.

Download the full text

Abstract

Social media is an easy-to-access platform providing timely updates about societal trends and events. Discussions regarding epidemic-related events such as infections, symptoms, and social interactions can be crucial for informing policymaking during epidemic outbreaks. In our work, we pioneer exploiting Event Detection (ED) for better preparedness and early warnings of any upcoming epidemic by developing a framework to extract and analyze epidemic-related events from social media posts. To this end, we curate an epidemic event ontology comprising seven disease-agnostic event types and construct a Twitter dataset SPEED with human-annotated events focused on the COVID-19 pandemic. Experimentation reveals how ED models trained on COVID-based SPEED can effectively detect epidemic events for three unseen epidemics of Monkeypox, Zika, and Dengue; while models trained on existing ED datasets fail miserably. Furthermore, we show that reporting sharp increases in the extracted events by our framework can provide warnings 4-9 weeks earlier than the WHO epidemic declaration for Monkeypox. This utility of our framework lays the foundations for better preparedness against emerging epidemics.

Bib Entry

@inproceedings{parekh2024event,
  title = {Event Detection from Social Media for Epidemic Prediction},
  author = {Parekh, Tanmay and Mac, Anh and Yu, Jiarui and Dong, Yuxuan and Shahriar, Syed and Liu, Bonnie and Yang, Eric J and Huang, Kuan-Hao and Wang, Wei and Peng, Nanyun and Chang, Kai-Wei},
  booktitle = {NAACL},
  year = {2024}
}

Related Publications

  1. DiCoRe: Enhancing Zero-shot Event Detection via Divergent-Convergent LLM Reasoning, EMNLP, 2025
  2. SNaRe: Domain-aware Data Generation for Low-Resource Event Detection, EMNLP, 2025
  3. LongMemEval: Benchmarking Chat Assistants on Long-Term Interactive Memory, ICLR, 2025
  4. SPEED++: A Multilingual Event Extraction Framework for Epidemic Prediction and Preparedness, EMNLP, 2024
  5. TextEE: Benchmark, Reevaluation, Reflections, and Future Challenges in Event Extraction, ACL-Findings, 2024
  6. GENEVA: Pushing the Limit of Generalizability for Event Argument Extraction with 100+ Event Types, ACL, 2023
  7. TAGPRIME: A Unified Framework for Relational Structure Extraction, ACL, 2023
  8. Enhancing Unsupervised Semantic Parsing with Distributed Contextual Representations, ACL-Finding, 2023
  9. DEGREE: A Data-Efficient Generative Event Extraction Model, NAACL, 2022
  10. Intent Classification and Slot Filling for Privacy Policies, ACL, 2021