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Since the start of COVID-19, several relevant corpora from various sourcesare presented in the literature that contain millions of data points. Whilethese corpora are valuable in supporting many analyses on this specificpandemic, researchers require additional benchmark corpora that contain otherepidemics to facilitate cross-epidemic pattern recognition and trend analysistasks. During our other efforts on COVID-19 related work, we discover verylittle disease related corpora in the literature that are sizable and richenough to support such cross-epidemic analysis tasks. In this paper, we presentEPIC30M, a large-scale epidemic corpus that contains 30 millions micro-blogposts, i.e., tweets crawled from Twitter, from year 2006 to 2020. EPIC30Mcontains a subset of 26.2 millions tweets related to three general diseases,namely Ebola, Cholera and Swine Flu, and another subset of 4.7 millions tweetsof six global epidemic outbreaks, including 2009 H1N1 Swine Flu, 2010 HaitiCholera, 2012 Middle-East Respiratory Syndrome (MERS), 2013 West African Ebola,2016 Yemen Cholera and 2018 Kivu Ebola. Furthermore, we explore and discuss theproperties of the corpus with statistics of key terms and hashtags and trendsanalysis for each subset. Finally, we demonstrate the value and impact thatEPIC30M could create through a discussion of multiple use cases ofcross-epidemic research topics that attract growing interest in recent years.These use cases span multiple research areas, such as epidemiological modeling,pattern recognition, natural language understanding and economical modeling.

EPIC30M: An Epidemics Corpus Of Over 30 Million Relevant Tweets