In the face of increasing economic turbulence, understanding public discourse on sustainability has never been more crucial. This study dissects and comprehends the nuances of how the public perceives and discusses sustainability on social media during varying economic conditions. Leveraging the capabilities of Data Mining and Natural Language Processing (NLP), this research delves into the rich tapestry of Instagram posts to unearth sentiment trends, thematic patterns, and engagement levels related to sustainability. By systematically harvesting Instagram data, we aim to map out the fluctuations in public sentiment towards sustainability in relation to economic shifts. The research employs advanced NLP techniques, such as sentiment analysis, topic modelling, and semantic analysis, to process and interpret the vast array of user-generated content. The expected outcomes of this study include a detailed sentiment timeline correlating economic events with shifts in public opinion on sustainability, an understanding of key themes and topics that dominate discussions during different economic phases, and insights into the effectiveness of various sustainability narratives in engaging the public. This research not only contributes to academic discourse but also offers valuable insights for policymakers, environmental organizations, and social media strategists aiming to foster a more sustainable future in challenging economic times.
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Çerasi et al. (2024) studied this question.
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