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.

Predictive modelling of stigmatized behaviour in vaccination discussions on Facebook Facebook often serves as a platform for sharing healthrelated information and is a venue to express attitudes, thoughts, and frustrations within groups centered around healthcare themes. This information can be utilized for public health monitoring, with the aim of tackling stigmatized and stereotypical attitudes in relation to immunization or other health related issues expressed in social media. However, the effectiveness of those attempts will rest on our understanding of the concept of stigma and its correct modeling. In this study, we aim to expand the small pool of existing computational studies on the topic of stigma identification in a health care context. More specifically, we compare the following models using a dataset of 2,761 comments from Facebook: Convolutional Neural Network (CNN): Term FrequencyInverse Document Frequency (TFIDF) with Logistic Regression (LR), Support Vector Machine (SVM), Naive Bayes (NB), Multilayer Perceptron (MLP), Random Forest (RF), Knearest neighbours (KNN), and Stochastic Gradient Descent (SGDC), Long shortterm memory networks (LSTM), Bidirectional long shortterm memory (BiLSTM), and fastText. Accuracy results as evaluated on an unbalanced data subset (with limited training samples) show that fastText gives the best performance, although BiLSTM and CNN achieve comparably good results on unbalanced data as well. CNN algorithm significantly outperforms other algorithms on balanced version of the dataset according to a paired sample ttest ( p  ).