During the Covid-19 pandemic social media have become an important tool for spreading information from government agencies regarding restrictions. Government accounts and public health care organizations have used different social media platforms such as Twitter to communicate with the Swedish public. The Swedish public have interacted with the information, arguing for a stricter or a more relaxed approach to Covid-19 recommendations. This social network analysis aims at exploring statistical methods to investigate patterns made by twitter accounts commenting the Swedish Armed Forces field hospital activities and the national Covid-19 strategy during the Covid-19 pandemic. Data was collected using the twitter platform and the Ncapture add-on with Google Chrome. The interactions stored in the tweets and replies section (TRS) from 227 twitter accounts were collected and coded with the NVivo auto code function. Twitter usernames that occurred in less than 35 % of the 227 TRS were deleted. The 227 extracted TRS were treated as scale items and occurring twitter-names which interacted with the TRS as respondents n=761. Analysis of the factor structure with PCA and CFA indicated four factors: 1) Military policy, 2) Right wing politics, 3) Law enforcement, 4) Politics and strategy. Structural Equation Modelling revealed interrelationships between the factors. Thus, Military policy, Law enforcement and Politics and strategy had a direct effect on Right wing politics. Politics and strategy had a direct effect on Military policy and Law enforcement. This study revealed that PCA, CFA and SEM have the potential to discover the core of a thought collective. Despite the obvious dangers with misinformation and political extremism on social media, policymakers need to tackle misinformation and disinformation, protecting electoral processes and facilitating public discussion, built on the three fundamental democratic principles of equality, representation and participation.