Themes and sentiments in conversations about food waste on Twitter: Proposal of a framework using neural topic modeling

• Published in: Food quality and preference Vol. 122; p. 105311
• Main Authors: Barbosa, Marcelo Werneck, Gomes, André
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.01.2025
• Subjects: data analysis; data collection; food industry; food quality; Food waste; Food waste behavior; garden residues; Neural topic modeling; plastics; Sentiment analysis; Social media analytics; social networks; waste management; Food waste; Social media analytics; Sentiment analysis; Neural topic modeling; Food waste behavior
• ISSN: 0950-3293
• DOI: 10.1016/j.foodqual.2024.105311

•Frequently discussed topics and the sentiments involved with food waste.•Most topic categories are associated with positive sentiments.•Negative sentiments are related to environmental and social impacts of food waste.•There is a gap in knowledge and awareness of food recycling practices.•A comprehensive framework of discussed topics and associated sentiments is proposed. There is growing research interest in the role of social media in the food industry, and its potential has yet to be fully exploited. This study extracted and analyzed data from Twitter, now X, (2018–2023) using relevant keywords related to food waste (FW), identified frequently discussed topics, and determined the sentiments involved in tweets. With the aid of neural topic modeling techniques, this study found that the main topics discussed in this social network include “Food and Garden Waste Composting and Recycling,” “Global Food Waste and Management,” “Reducing Food Waste & Saving Money,” “Single-Use Plastic and Waste Management,” and “Creative Uses of Leftovers in Meals”. Some of the subject categories often discussed are FW Environmental concerns, FW events, Leftovers management, and FW household management. Most subject categories are associated with positive sentiments, which are associated with topics like tips and recommendations for reducing FW. Negative sentiments are related to discussions around the environmental and social impacts of FW. Our study found a gap in knowledge and awareness of food recycling practices. Our study extends previous research because the analyzed messages are not restricted to specific geographical regions. Moreover, our study also used a more varied dataset in terms of user profiles and employed a search string that comprised a more varied set of terms, making our search more thorough. A comprehensive framework of discussed topics and associated sentiments is proposed. Our study has crucial implications for the design of FW reduction campaigns and interventions in social media.