Exploring the nexus between food and veg*n lifestyle via text mining-based online community analytics. (March 2023)

• Record Type: Journal Article
• Authors: Yoo, Ra; Kim, Seo-Young; Kim, Do-Hee; Kim, Jiyoung; Jeon, Ye Ji; Park, Jung Han Yoon; Lee, Ki Won; Yang, Hee
• Is Part Of: Food quality and preference. Volume 104(2023)
• Publication Date: 2023-03
• Subjects: Vegan -- Vegetarian -- Online communities -- Consumer -- Perception -- Text mining; Food preferences -- Periodicals; Food -- Quality -- Periodicals; Food industry and trade -- Quality control -- Periodicals; Préférences alimentaires -- Périodiques; Aliments -- Qualité -- Périodiques; Aliments -- Industrie et commerce -- Qualité -- Contrôle -- Périodiques; Food industry and trade -- Quality control; Food preferences; Food -- Quality; Periodicals; 664
• DOI: 10.1016/j.foodqual.2022.104714
• Languages: English
• ISSNs: 0950-3293

Abstract

Highlights: Multi text analysis techniques were used to investigate online veg*n communities. 6 cluster topics and their proportions in online veg*n communities were revealed. Topics in food were identified further into 9 subcategories using LDA topic modeling. Sudden dietary changes may cause confusion in social and consumption routines. Systemic approaches considering various veg*n lifestyles are required. Abstract: Attention towards veg*nism is increasing as the impact of food choices on health and sustainability as well as ethical concerns regarding animal welfare emerge. Although online user analysis is an effective tool to obtain practical insights without geographical constraints, implementation on a large veg*n population has been carried out within a limited scope. This study investigated two veg*n subreddits, r/Vegan and r/Vegetarian, using multiple text mining techniques to classify users' interests and preferences. Based on K-means and term frequency-inverse document frequency, six clusters were identified: Food, Perception, Health, Altruism, Emotion, and Situation. The proportion of each cluster and keywords representing the clusters were obtained. Being a major sector, further assessment of the Food cluster was conducted using Latent Dirichlet Allocation topic modeling technique. Confusion was observed in relation to being pressured with sudden changes in dietary patterns, including meal composition, preparation, and shopping routines. The results also revealed barriers to transition for individuals who have recently started veg*n diets, and those wishing to switch to stricter dietary patterns. In addition to difficulties relating to economic and social aspects, our findings suggest that the establishment of detailed guidelines may help accommodate the various dietary compositions across the veg*n spectrum, and clear information relating to veg*n food products is needed from manufacturers.