Youtube video comments on healthy eating: Descriptive and predictive analysis

Shasha Teng*, Kok Wei Khong, Saeed Pahlevan Sharif, Amr Ahmed

*Corresponding author for this work

Research output: Contribution to journalArticle (journal)peer-review

6 Citations (Scopus)

Abstract

Background: Poor nutrition and food selection lead to health issues such as obesity, cardiovascular disease, diabetes, and cancer. This study of YouTube comments aims to uncover patterns of food choices and the factors driving them, in addition to exploring the sentiments of healthy eating in networked communities. Objective: The objectives of the study are to explore the determinants, motives, and barriers to healthy eating behaviors in online communities and provide insight into YouTube video commenters’ perceptions and sentiments of healthy eating through text mining techniques. Methods: This paper applied text mining techniques to identify and categorize meaningful healthy eating determinants. These determinants were then incorporated into hypothetically defined constructs that reflect their thematic and sentimental nature in order to test our proposed model using a variance-based structural equation modeling procedure. Results: With a dataset of 4654 comments extracted from YouTube videos in the context of Malaysia, we apply a text mining method to analyze the perceptions and behavior of healthy eating. There were 10 clusters identified with regard to food ingredients, food price, food choice, food portion, well-being, cooking, and culture in the concept of healthy eating. The structural equation modeling results show that clusters are positively associated with healthy eating with all P values less than .001, indicating a statistical significance of the study results. People hold complex and multifaceted beliefs about healthy eating in the context of YouTube videos. Fruits and vegetables are the epitome of healthy foods. Despite having a favorable perception of healthy eating, people may not purchase commonly recognized healthy food if it has a premium price. People associate healthy eating with weight concerns. Food taste, variety, and availability are identified as reasons why Malaysians cannot act on eating healthily. Conclusions: This study offers significant value to the existing literature of health-related studies by investigating the rich and diverse social media data gleaned from YouTube. This research integrated text mining analytics with predictive modeling techniques to identify thematic constructs and analyze the sentiments of healthy eating.

Original languageEnglish
Article numbere19618
JournalJMIR Public Health and Surveillance
Volume6
Issue number4
DOIs
Publication statusPublished - 1 Oct 2020

Keywords

  • Clustering
  • Healthy eating
  • Structural equation modeling
  • Text mining
  • YouTube comments

Research Centres

  • Data and Complex Systems Research Centre
  • Data Science STEM Research Centre

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