Early warnings and effective control measures are among the most important tools for policymakers to be prepared against the threat of any epidemic. Social media is an important information source here, as it is more timely than other alternatives like news and public health and is publicly accessible. Given the sheer volume of daily social media posts, there is a need for an automated system to monitor social media to provide early and effective epidemic prediction. To this end, I introduce two works to aid the creation of such an automated system using information extraction. In my first 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. We curate an epidemic event ontology comprising seven disease-agnostic event types and construct a Twitter dataset SPEED 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. 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.

Since epidemics can originate across the globe, social media posts discussing them can be in varied languages. However, training supervised models on every language is a tedious and resource-expensive task. The alternative is the usage of zero-shot cross-lingual models. In this work, we introduce a new approach for label projection that can be used to generate synthetic training data in any language using the translate-train paradigm. This novel approach, CLaP, translates text to the target language and performs contextual translation on the labels using the translated text as the context, ensuring better accuracy for the translated labels. We leverage instruction-tuned language models with multilingual capabilities as our contextual translator, imposing the constraint of the presence of translated labels in the translated text via instructions. We benchmark CLaP with other label projection techniques on zero-shot cross-lingual transfer across 39 languages on two representative structured prediction tasks — event argument extraction (EAE) and named entity recognition (NER), showing over 2.4 F1 improvement for EAE and 1.4 F1 improvement for NER.