Modeling emotional signatures to detect conspiratorial communities on social media

Disinformation and conspiracy theories represent a growing social threat, often amplified within echo chambers where emotions strongly influence content creation and diffusion. This paper introduces the concept of Emotional Signatures, aggregated affective/emotional profiles designed to capture the dominant emotions expressed on social media. The approach has been tested on Reddit communities, where we processed users’ posts through an emotion recognition model that scores 27 emotions. We compare the resulting emotional profiles of conspiracy and non-conspiracy subreddits groups using similarity measures, dimensionality reduction, and supervised classifiers. Experimental results show that emotions provide a powerful discriminative signal. Logistic Regression achieved an overall accuracy of 0.874, macro-precision of 0.904, and F1 of 0.870, outperforming a Multi-Layer Perceptron, which reached an accuracy of 0.785. Further tests confirmed that conspiracy groups show greater emotional homogeneity and stronger alignment with emotions such as optimism and curiosity, which means cohesion in terms of how the topic is perceived. In contrast, non-conspiracy communities show different emotional patterns, such as excitement, nervousness and confusion, linked to the different ideas expressed during discussions. We have also compared our models with the state of art ConspEmoLLM, an LLM trained for Conspiracy topic detection. The comparison shows promising results. The proposed framework not only detects potentially conspiratorial subreddits but also highlights the emotional drivers behind their discourse, providing interpretability and insights for understanding the affective mechanisms of online misinformation. These findings demonstrate that embedding emotions into computational models is a promising direction to identify, explain, and mitigate disinformation dynamics in online communities.