Event Detection from Social Media for Epidemic Prediction (2024)

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.

## introduction early warnings and effective control measures are among the most important tools for policymakers to be prepared against the threat of any epidemic @xcite . world health organization (who) reports suggest that 65% of the first reports about infectious diseases and outbreaks originate from informal sources and the internet @xcite . social media is an important information source here, as it is more timely than other alternatives like news and public health @xcite , more publicly accessible than clinical notes @xcite , and possesses a huge vol- ## from event detection to epidemic prediction given a social media post, event detection (ed) @xcite extracts and classifies significant events of interest. by designing disease-agnostic epidemic-based events, we aim to train ed models to extract epidemic events from social media posts for any possible disease. by detecting abnormal influx in the trends of extracted epidemic events from social media, we can thus provide early epidemic warnings for any possible disease, as we show for monkeypox in figure 1 . existing epidemiological approaches @xcite are simple keyword or sentence classification-based and less accurate. other works like covidkb @xcite and excavatorcovid @xcite are disease-specific and utilize events for building knowledge bases. to the best of our knowledge, we are the first ones to leverage event detection to extract epidemic events from social media and provide early warnings for any possible disease. formal task definition following ace 2005 guidelines @xcite , we define an event to be something that happens or describes a change of state and is labeled by a specific event type. an event mention is the sentence wherein the event is described. each event mention comprises an event trigger, which is the word/phrase that most distinctly highlights the occurrence of the event. event detection is technically defined as the task of identifying event triggers from sentences and classifying them into one of the predefined event types (defined by an event ontology). the subtask of identifying event triggers is called trigger identification and classification into event types is trigger classification @xcite . figure 2 shows examples for three event mentions for the events symptom, infect, and death. ## ontology creation and data collection we choose social media as our document source as it provides faster and more timely worldly information than news and public health @xcite and is more publicly accessible than clinical notes @xcite . owing to its public access and huge content volume, we consider twitter 2 as the social media platform and consider the recent covid-19 pandemic as the primary disease. existing epidemiological ontologies are typically disease-specific, too fine-grained, or limited in coverage ( § 6 and ## epidemic prediction for our ed framework, we utilize our curated dataset speed to train various ed models ( § 4.1). to validate the utility of models for the application of epidemic prediction, we perform evaluations using two tasks: (1) epidemic event detection and (2) early warning prediction. epidemic event detection performs a formal ed evaluation of the models for detecting epidemic-based events. on the other hand, early warning prediction practically evaluates if the extracted events by the model can be aggregated to provide any early epidemic warnings. since speed focuses solely on covid-19, we conduct these epidemic prediction evaluations for three unseen epidemics of monkeypox (2022), zika (2017), and dengue (2018). these diseases are fairly distinct too, as monkeypox causes rashes and rarely fatal, zika causes birth defects, and dengue causes high fever and can be fatal. for our evalua- disease # sent # em train covid 1, 601 1, 746 dev covid 374 471 test monkeypox 286 398 zika + dengue 300 274 ## analysis and discussion in this section, we provide additional analyses to support the utility of our ed framework and also provide intuitions for why our framework works better than previous works. ## conclusion and future work in this work, we develop an event detection (ed) framework to extract events from social media to provide early epidemic warnings. to facilitate this, we create our twitter-based dataset speed comprising seven event types. through experimentation, we show how existing models fail; while models trained on speed can effectively extract events and provide early warnings for unseen emerging epidemics. more broadly, our work demonstrates how event extraction can exploit social media to aid policy-making for better epidemic preparedness.