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Exploring Food Waste Conversations on Social Media: A Sentiment, Emotion, and Topic Analysis of Twitter Data

Author

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  • Eva L. Jenkins

    (Department of Nutrition, Dietetics and Food, Monash University, Level 1, 264 Ferntree Gully Road, Notting Hill 3168, Australia)

  • Dickson Lukose

    (Tabcorp Holdings Ltd., Level 19, Tower 2, 727 Collins Street, Melbourne 3008, Australia)

  • Linda Brennan

    (School of Media and Communication, RMIT University, 124 La Trobe St, Melbourne 3004, Australia)

  • Annika Molenaar

    (Department of Nutrition, Dietetics and Food, Monash University, Level 1, 264 Ferntree Gully Road, Notting Hill 3168, Australia)

  • Tracy A. McCaffrey

    (Department of Nutrition, Dietetics and Food, Monash University, Level 1, 264 Ferntree Gully Road, Notting Hill 3168, Australia)

Abstract

Food waste is a complex issue requiring novel approaches to understand and identify areas that could be leveraged for food waste reduction. Data science techniques such as sentiment analysis, emotion analysis, and topic modelling could be used to explore big-picture themes of food waste discussions. This paper aimed to examine food waste discussions on Twitter and identify priority areas for future food waste communication campaigns and interventions. Australian tweets containing food-waste-related search terms were extracted from the Twitter Application Programming Interface from 2019–2021 and analysed using sentiment and emotion engines. Topic modelling was conducted using Latent Dirichlet Allocation. Engagement was calculated as the sum of likes, retweets, replies, and quotes. There were 39,449 tweets collected over three years. Tweets were mostly negative in sentiment and angry in emotion. The topic model identified 13 key topics such as eating to save food waste, morals, economics, and packaging. Engagement was higher for tweets with polarising sentiments and negative emotions. Overall, our interdisciplinary analysis highlighted the negative discourse surrounding food waste discussions and identified priority areas for food waste communication. Data science techniques should be used in the future to monitor public perceptions and understand priority areas for food waste reduction.

Suggested Citation

  • Eva L. Jenkins & Dickson Lukose & Linda Brennan & Annika Molenaar & Tracy A. McCaffrey, 2023. "Exploring Food Waste Conversations on Social Media: A Sentiment, Emotion, and Topic Analysis of Twitter Data," Sustainability, MDPI, vol. 15(18), pages 1-26, September.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:18:p:13788-:d:1240848
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    References listed on IDEAS

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    1. Fengni Gao & Emmanuel Nketiah & Victor Shi, 2024. "Understanding and Enhancing Food Conservation Behaviors and Operations," Sustainability, MDPI, vol. 16(7), pages 1-19, March.
    2. Maria-Georgeta Moldovan & Dan-Cristian Dabija & Liana Stanca & Cristina Bianca Pocol, 2024. "A Qualitative Study on the Consumer Behaviour Related to Food Waste: Romanian Perspectives through Word Cloud and Sentiment Analysis," Sustainability, MDPI, vol. 16(10), pages 1-27, May.

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