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              <text>Analysing the Ascendant Trend of Veganism: A Comprehensive Study on the Shift towards Sustainable Dietary Choices</text>
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              <text>Background: Veganism has become a prominent social and culinary movement due to concerns about animal welfare, environmental sustainability, and ones own health. Vegans strive to consume only plant-based meals in order to lessen the suffering of animals, stop the environmental damage caused by the animal agriculture sector, and enhance their own health. Objective: This chapter aspires to understand various dynamics of consumer consciousness towards veganism through social media analysis (Twitter) and research opinions. Materials and Methods: This chapter used a qualitative approach and a three-part methodology. Firstly, a literature review examines the impact of veganism on human health, ethical needs and sustainable food choices. Secondly, the authors extracted tweets and analysed them using data visualisation software- NVivo with the essential parameters being themes, sentiment, world map, and word cloud. Results: Sentiment analysis explained consumer perception towards veganism as a storming blackball result of 36.1 present positive insights. Word map analysis describes veganism as a global phenomenon. The third part analysed the Scopus research data and identified food, diet and meat as major themes in veganism. The Scopus database sentimental analysis also re-emphasised the growing positive insights towards it. Conclusion: This study highlighted the significance of veganism as a sustainable dietary choice for addressing urgent global issues while promoting a thoughtful and compassionate approach to eating. It is also emerging as a powerful tool for positive change in preserving and promoting biodiversity.  2024 selection and editorial matter, Mourade Azrour, Jamal Mabrouki, Azidine Guezzaz, Sultan Ahmad, Shakir Khan and Said Benkirane; individual chapters, the contributors.</text>
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              <text>Bhagat S.; Ravi S.S.; Rai R.</text>
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              <text>IoT, Machine Learning and Data Analytics for Smart Healthcare, pp. 71-84.</text>
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              <text>CRC Press</text>
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              <text>2024-01-01</text>
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              <text>&lt;a href="https://doi.org/10.1201/9781003430735-6" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1201/9781003430735-6&lt;/a&gt;
&lt;br /&gt;&lt;br /&gt;&lt;a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85186723448&amp;amp;doi=10.1201%2F9781003430735-6&amp;amp;partnerID=40&amp;amp;md5=0163bc90a31a90cf3202b7cf70707747" target="_blank" rel="noreferrer noopener"&gt;https://www.scopus.com/inward/record.uri?eid=2-s2.0-85186723448&amp;amp;doi=10.1201%2f9781003430735-6&amp;amp;partnerID=40&amp;amp;md5=0163bc90a31a90cf3202b7cf70707747&lt;/a&gt;</text>
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              <text>ISBN: 978-100386061-7; 978-103255107-4</text>
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              <text>Online</text>
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              <text>Bhagat S., School of Business and Management, Christ University, India; Ravi S.S., School of Business and Management, Christ University, India; Rai R., School of Business and Management, Christ University, India</text>
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