Natural language processing reveals network structure of pain communication in social media using discrete mathematical analysis

Pain-related discussions on social media provide valuable insights into how people naturally express and communicate their pain experiences. However, the network structure of these discussions remains poorly understood. This study analyzed 57,000 Reddit comments from the GoEmotions dataset (2005-2019) using natural language processing and network analysis techniques grounded in discrete mathematical principles. The constructed network, comprising 5,630 nodes and 86,972 edges, revealed complex patterns of pain-related language use. The network exhibited a sparse overall density (0.0055) but a high clustering coefficient (0.7700), indicating the presence of distinct thematic communities. At the center of the network was the term pain, which showed the highest degree centrality (0.821429), reflecting its semantic anchoring function in pain discourse. Other terms, such as headache, served as context-sensitive bridge nodes that connected different semantic subdomains. In contrast, terms like burning, despite moderate centrality values, were found to co-occur predominantly with metaphorical or decorative expressions rather than emotion- or symptom-related descriptors. Community detection revealed 12 distinct clusters, with the largest containing 1,021 nodes, capturing diverse aspects of pain communication. Stability analysis demonstrated that core pain-related terms maintained consistent centrality, while peripheral or metaphorical terms showed greater variability. These findings offer novel insights into the semantic structure of pain discourse and suggest that network analysis of social media discussions can inform improved clinical communication and symptom assessment.