# Event Detection from Social Media for Epidemic Prediction

arXiv: https://arxiv.org/abs/2404.01679 · HTML: https://mlsanity.com/paper/2404.01679/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 67 references that are in the map.
- First posted on arXiv 2 Apr 2024.

## Builds on (most cited first, 16 of 16)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Entity, Relation, and Event Extraction with Contextualized Span Representations](https://mlsanity.com/paper/1909.03546/) (2019)
- [Event Extraction by Answering (Almost) Natural Questions](https://mlsanity.com/paper/2004.13625/) (2020)
- [Document-Level Event Argument Extraction by Conditional Generation](https://mlsanity.com/paper/2104.05919/) (2021)
- [MAVEN: A Massive General Domain Event Detection Dataset](https://mlsanity.com/paper/2004.13590/) (2020)
- [DEGREE: A Data-Efficient Generation-Based Event Extraction Model](https://mlsanity.com/paper/2108.12724/) (2021)
- [Cross-media Structured Common Space for Multimedia Event Extraction](https://mlsanity.com/paper/2005.02472/) (2020)
- [Meta-Learning with Dynamic-Memory-Based Prototypical Network for Few-Shot Event Detection](https://mlsanity.com/paper/1910.11621/) (2019)
- [GENEVA: Benchmarking Generalizability for Event Argument Extraction with Hundreds of Event Types and Argument Roles](https://mlsanity.com/paper/2205.12505/) (2022)
- [Corpus-based Open-Domain Event Type Induction](https://mlsanity.com/paper/2109.03322/) (2021)
- [Human-in-the-loop Evaluation for Early Misinformation Detection: A Case Study of COVID-19 Treatments](https://mlsanity.com/paper/2212.09683/) (2022)
- [Open Relation and Event Type Discovery with Type Abstraction](https://mlsanity.com/paper/2212.00178/) (2022)
- [Extracting a Knowledge Base of COVID-19 Events from Social Media](https://mlsanity.com/paper/2006.02567/) (2020)
- [ExcavatorCovid: Extracting Events and Relations from Text Corpora for Temporal and Causal Analysis for COVID-19](https://mlsanity.com/paper/2105.01819/) (2021)
- [Connect-the-Dots: Bridging Semantics between Words and Definitions via Aligning Word Sense Inventories](https://mlsanity.com/paper/2110.14091/) (2021)

## Built on by (most cited first, 7 of 7)

- [Explicit, Implicit, and Scattered: Revisiting Event Extraction to Capture Complex Arguments](https://mlsanity.com/paper/2410.03594/) (2024)
- [SPEED++: A Multilingual Event Extraction Framework for Epidemic Prediction and Preparedness](https://mlsanity.com/paper/2410.18393/) (2024)
- [Event Extraction in Large Language Model](https://mlsanity.com/paper/2512.19537/) (2025)
- [Dynamic Strategy Planning for Efficient Question Answering with Large Language Models](https://mlsanity.com/paper/2410.23511/) (2024)
- [SNaRe: Domain-aware Data Generation for Low-Resource Event Detection](https://mlsanity.com/paper/2502.17394/) (2025)
- [EventHunter: Dynamic Clustering and Ranking of Security Events from Hacker Forum Discussions](https://mlsanity.com/paper/2507.09762/) (2025)
- [MetaKP: On-Demand Keyphrase Generation](https://mlsanity.com/paper/2407.00191/) (2024)

## Abstract (arXiv)

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.