  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.
