Social network and linguistic analysis of the #nutrition discourse on the social network platform X, formerly known as Twitter

Abstract

Social network analysis (SNA) of social media content allows information transfer to be visualised, identifies influential actors, and reveals public opinion. However, to date no research has investigated content related to nutrition on X. This study examined the #nutrition conversations on X (formerly Twitter) utilising SNA and linguistic methods. NodeXL Pro was used for network, semantic and sentiment analyses on English language posts including ‘#nutrition’ collected between 1 and 21 March 2023. The #nutrition network included 17,129 vertices (users) with 26,809 edges (relationships). NodeXL Pro was used to assess the structure of the network and the actors involved by calculating the network metrics. The results show a low density, dispersed network (graph density = 0.001) with most users communicating heavily with a small number of other users. These subgroup community cluster structures restrict information flow outside of the subgroups (modularity = 0.79). These network structures rely on influential users to share information (betweenness centrality range, 0 to 23,375,544). Notably, influential users were typically from both personal and not-for-profit accounts. Semantic analysis identified 97,000 word-pair edges with the most frequently discussed topics related to health, healthy lifestyle and diet, with a positive sentiment found across the network. By using SNA, semantic, and sentiment analyses, this study found a dispersed X network with a high proportion of unconnected users who did not have relationship with other users in the network. The findings reveal a publicly driven debate focused on healthy diets and lifestyle, with information primarily propagated through reposting.