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Vaccine Hesitancy in Discussion Forums: Computer-Assisted Argument Mining with Topic Models.

Maria Skeppstedt¹, Andreas Kerren¹, Manfred Stede²

¹Computer Science Department, Linnaeus University, Växjö, Sweden.

Studies in Health Technology and Informatics

|April 22, 2018

Summary

Analyzing online vaccine debates reveals key arguments driving vaccine hesitancy. Automatic topic modeling successfully identified six distinct discussion themes, aiding in understanding public vaccine concerns.

Keywords:

Vaccine hesitancy argument mining topic modelling

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Area of Science:

Public Health
Computational Social Science
Communication Studies

Background:

Understanding vaccine hesitancy is crucial for public health.
Internet discussion forums are a rich source of public opinion on vaccination.
Identifying key arguments in vaccine debates can inform public health strategies.

Purpose of the Study:

To explore arguments used in online vaccine discussions.
To investigate the utility of automatic topic modeling in identifying vaccine debate themes.
To facilitate the analysis of vaccine hesitancy arguments.

Main Methods:

Collected 943 online discussion posts debating vaccination.
Applied automatic topic modeling to identify distinct discussion topics.
Manually coded posts to identify semantically coherent arguments within each topic.

Main Results:

Six distinct vaccine-related discussion topics were automatically detected.
Semantically coherent arguments were identified for each topic.
Topic modeling proved effective in identifying vaccine debate themes and relevant posts.

Conclusions:

Automatic topic modeling is a valuable tool for analyzing online vaccine discussions.
This method can help identify salient arguments related to vaccine hesitancy.
Findings can contribute to developing computer-assisted coding systems for public health discussions.