Uncovering the relationship between food-related discussion on Twitter and neighborhood characteristics. (21st October 2019)

• Record Type: Journal Article
• Authors: Vydiswaran, V G Vinod; Romero, Daniel M; Zhao, Xinyan; Yu, Deahan; Gomez-Lopez, Iris; Lu, Jin Xiu; Iott, Bradley E; Baylin, Ana; Jansen, Erica C; Clarke, Philippa; Berrocal, Veronica J; Goodspeed, Robert; Veinot, Tiffany C
• Is Part Of: Journal of the American Medical Informatics Association. Volume 27:Number 2(2020)
• Page Start: 254
• Page End: 264
• Publication Date: 2019-10-21
• Subjects: natural language processing -- social media -- health equity -- population health -- healthy diet; Medical informatics -- Periodicals; Information Services -- Periodicals; Medical Informatics -- Periodicals; Médecine -- Informatique -- Périodiques; Informatica; Geneeskunde; Informatique médicale; Computer network resources; Electronic journals; 610.285
• DOI: 10.1093/jamia/ocz181
• Languages: English
• ISSNs: 1067-5027

Abstract

Abstract: Objective: Initiatives to reduce neighborhood-based health disparities require access to meaningful, timely, and local information regarding health behavior and its determinants. We examined the validity of Twitter as a source of information for neighborhood-level analysis of dietary choices and attitudes. Materials and Methods: We analyzed the "healthiness" quotient and sentiment in food-related tweets at the census tract level, and associated them with neighborhood characteristics and health outcomes. We analyzed keywords driving the differences in food healthiness between the most and least-affluent tracts, and qualitatively analyzed contents of a random sample of tweets. Results: Significant, albeit weak, correlations existed between healthiness and sentiment in food-related tweets and tract-level measures of affluence, disadvantage, race, age, U.S. density, and mortality from conditions associated with obesity. Analyses of keywords driving the differences in food healthiness revealed foods high in saturated fat (eg, pizza, bacon, fries) were mentioned more frequently in less-affluent tracts. Food-related discussion referred to activities (eating, drinking, cooking), locations where food was consumed, and positive (affection, cravings, enjoyment) and negative attitudes (dislike, personal struggles, complaints). Discussion: Tweet-based healthiness scores largely correlated with offline phenomena in the expected directions. Social media offer less resource-intensive data collection methods than traditional surveys do. Twitter may assist in informing local health programs that focus on drivers of food consumption and could inform interventions focused on attitudes and the food environment. Conclusions: Twitter provided weak but significant signals concerning food-related behavior and attitudes at the neighborhood level, suggesting its potential usefulness for informing local health disparity reduction efforts.