Using Social Network Analysis methodology, specifically the Network of Similarity and Response Time Testing as a survey method, we measured and examined, based on conviction strength, the relationships between beliefs in various conspiracy theories. We employ Social Network Analysis (SNA) to uncover conspiracy thinking patterns. SNA facilitates the disclosure of interdependencies among variables and intricate direct and indirect relationships. The network of conspiracy convictions is mapped and scrutinized to discern the clustering of variables, which is achieved using greedy-modularity algorithms. Structural properties, such as nodal and subgroup density, are subsequently calculated to assess the quality of the clusters. A qualitative evaluation explores the semantic meanings underlying the observed patterns. Our analysis revealed strong correlations between the items, indicating that individuals who believe in one conspiracy theory are highly likely to believe in others. Furthermore, Response Time Testing allowed for measuring the level of people's conviction in these beliefs. We discuss the implications of these findings, suggesting that conspiracy theories may serve as a means for individuals to confirm their positions and feelings in society. This insight calls for a reassessment of strategies to address the spread and impact of conspiracy theories, focusing on understanding the psychological and social factors driving belief in multiple conspiracies and the strength of these convictions.

Mental distress has been consistently reported to be highly prevalent after collective traumas, alongside physical and personal damages. When left untreated, these will worsen survivors’ ability to function. Research also points to unmet needs, related to job security and a sense of belonging. Our study aims to identify a clustered-dimensional approach to people’s experiences after a massive urban violence apart from traditional categorical psychopathological assessments. This cross-sectional internet-based study included 1305 Lebanese adults, 4 months after the apocalyptic Beirut Port explosion. Emotions, attitudes and needs were assessed using iCode software, measuring explicit answers and implicit reaction time. First and foremost, explicit responses revealed alarming levels of distress (75-80%). Latent class analysis further differentiated three groups on seven different dimensions derived from principal component analysis. People who experienced the most intense emotional distress and intrusive thoughts had higher country dissatisfaction and job worries. Faith and community resilience buffered the negative emotionality of those affected in spite of avoidance and intrusion. The last group was less distressed by the trauma with a marked sense of community and an overall reduced country and job dissatisfactions. These findings suggest that integrating implicit responses helps cluster people’s experiences after a collective trauma above and beyond single demographic criteria as vulnerability to mass violence is quite variable within seemingly homogenous samples. They also provide insight onto hard-wired attitudes and needs post-trauma. It mostly taps into multi-factorial individual vulnerabilities and protective factors to better refine targeted interventions for at-risk subpopulation outreach and foster resilience in unstable environments.