Predictive Modelling of Stigmatized Behaviour in Vaccination Discussions on Facebook

Nadiya Straton, Raymond Ng, Hyeju Jang, Ravi Vatrapu, Raghava Rao Mukkamala

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

Abstract

Facebook often serves as a platform for sharing health-related 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,withtheaimoftacklingstigmatizedandstereotypical 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 Frequency-Inverse Document Frequency (TF-IDF) with Logistic Regression (LR), Support Vector Machine (SVM), Naive Bayes (NB), Multilayer Perceptron (MLP), Random Forest (RF), K-nearest neighbours (KNN), and Stochastic Gradient Descent (SGDC), Long short-term memory networks (LSTM), Bidirectional long short-term 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 t-test (p<0.05).
Original languageEnglish
Title of host publicationProceedings of 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
EditorsIllhoi Yoo, Jinbo Bi, Xiaohua Hu
Number of pages8
Place of PublicationPiscataway, NJ
PublisherIEEE
Publication dateNov 2019
Pages2561-2568
Article number8983175
ISBN (Electronic)9781728118673
DOIs
Publication statusPublished - Nov 2019
Event2019 IEEE International Conference on Bioinformatics and Biomedicine. BIBM 2019 - San Diego, United States
Duration: 19 Nov 201921 Nov 2019
https://ieeebibm.org/BIBM2019/

Conference

Conference2019 IEEE International Conference on Bioinformatics and Biomedicine. BIBM 2019
CountryUnited States
CitySan Diego
Period19/11/201921/11/2019
Internet address

Bibliographical note

CBS Library does not have access to the material

Keywords

  • Social media
  • Vaccination
  • CNN
  • LSTM
  • BiLSTM
  • FastText
  • N-grams
  • TF-IDF

Cite this

Straton, N., Ng, R., Jang, H., Vatrapu, R., & Mukkamala, R. R. (2019). Predictive Modelling of Stigmatized Behaviour in Vaccination Discussions on Facebook. In I. Yoo, J. Bi, & X. Hu (Eds.), Proceedings of 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) (pp. 2561-2568). [8983175] IEEE. https://doi.org/10.1109/BIBM47256.2019.8983175