Plague Dot Text: Text mining and annotation of outbreak reports of the Third Plague Pandemic (18941952). The design of models that govern diseases in population is commonly built on information and data gathered from past outbreaks. However, epidemic outbreaks are never captured in statistical data alone but are communicated by narratives, supported by empirical observations. Outbreak reports discuss correlations between populations, locations and the disease to infer insights into causes, vectors and potential interventions. The problem with these narratives is usually the lack of consistent structure or strong conventions, which prohibit their formal analysis in larger corpora. Our interdisciplinary research investigates more than 100 reports from the third plague pandemic (18941952) evaluating ways of building a corpus to extract and structure this narrative information through text mining and manual annotation. In this paper we discuss the progress of our ongoing exploratory project, how we enhance optical character recognition (OCR) methods to improve text capture, our approach to structure the narratives and identify relevant entities in the reports. The structured corpus is made available via Solr enabling search and analysis across the whole collection for future research dedicated, for example, to the identification of concepts. We show preliminary visualisations of the characteristics of causation and differences with respect to gender as a result of syntacticcategorydependent corpus statistics. Our goal is to develop structured accounts of some of the most significant concepts that were used to understand the epidemiology of the third plague pandemic around the globe. The corpus enables researchers to analyse the reports collectively allowing for deep insights into the global epidemiological consideration of plague in the early twentieth century.

Computational modeling of stigmatized behaviour in provaccination and antivaccination discussions on social media Much research has been done within the social sciences on the interpretation and influence of stigma on human behaviour and health, which result in outofgroup exclusion, distancing, cognitive separation, status loss, discrimination, ingroup pressure, and often lead to disengagement, nonadherence to treatment plan, and prescriptions by the doctor. However, little work has been conducted on computational identification of stigma in general and in social media discourse in particular. In this paper, we develop the annotation scheme for stigma based on social science theories, and perform a corpus study on the data from Facebook groups on vaccination. The data from provaccination and antivaccination discussion groups are annotated by trained annotators and by MTurk annotators. We analyze the annotations using LIWC (Linguistic Inquiry and Word Count) software and TFIDF in order to identify differentiating features between stigmatizing vs. nonstigmatizing content. Our corpus study lays a valuable foundation in computational modeling of social stigma, as it can serve as validationinterpretation of the social science theories through the prism of laypeople understanding. The annotated corpora can be subsequently used for automatic stigma identification. Moreover, the annotation scheme can be applied to study stigma across different themes and diseases and can be utilized during public health informational campaigns and health interventions.