Characterising Communities of Twitter Users Who Posted Vaccines Related Tweets by Information Sources

TL;DR

This study analyzes Twitter communities discussing vaccines by examining information sources and content types, revealing distinct community structures and perceptions that can inform public health strategies.

Contribution

It introduces a community detection approach based on information credibility and content sources, specifically applied to vaccine-related Twitter data.

Findings

Identified 192 vaccine-related communities on Twitter.

Most tweets (57%) were evidence-based or advocacy.

Majority of experiential tweets were posted by users sharing evidence and advocacy.

Abstract

Objective: We formed community structure based on web page credibility and we measured the types of information for characterizing communities of tweeter users who posted about tweets related to vaccine. Methods: We performed the experiment on only Twitter data (tweets) regarding vaccine. The duration of data collection was between 17 January 2017 and 14 March 2018. We formulated cluster based on the information on its contents and sources it resides (i.e., website domains). We only focused the topics which were related to vaccine. To detect the structure and network of community, we applied Louvain community algorithm along with Random walks called Info map method over vaccines related tweeter user. We defined the communities based on various measures derived from the information shared by Twitter users. Representations and visualizations of the communities based on these derived measures help the public health organization to make understand the possible cause of the rejecting the safety and efficacy of vaccines. Results: To analyse people perception over social media, we downloaded 6,591,566 tweets. We distinguished 1,860,662 users who were posting related to vaccine. We applied Louvain community detection algorithm along with DMM values and we found 192 communities. It also produced higher alignment values as long as the number of topics were low. With the increase of topics number, the alignment becomes lower. The total tweets were divided into two parts based on their characterizes. The first category contained 163,148 (57.16%) tweets which were based as evidence and advocacy and the second category contained 6244 (2.19%) tweets which were based on the experiences and opinion. We observed 4548 users were posting about experiential tweets about vaccine and of them 3449 users (75.84%) were posted evidence and advocacy related.