Public Sentiment Towards Sustainability: A Digital Platforms Analysis of Pakistani Fashion Brands

Abstract

This research aims to determine public sentiment towards sustainability initiatives by Pakistani fashion brands across the five major social media platforms, including Facebook, Instagram, LinkedIn, TikTok, and YouTube. The study explores platform-dependent engagement patterns, assesses the influence of sustainability-related hashtags, and makes recommendations for optimized sustainable messaging. The research uses 3,427 publicly archived comments posted on social media of Pakistani fashion brands from January 2020 to August 2025 and has been analysed quantitatively with the help of content analysis. For sentiment analysis, lexicon-based scoring approaches (Text Blob) and supervised machine learning models (logistic regression, support vector machines [SVMs], gradient boosting, and random forest) were employed. The findings reveal an overall non-normally distributed sentiment score (mean sentiment score = 0.082), with 36.1% of comments being positive, 23.5% negative, and 40.4% being neutral. Instagram was the medium with the most positive engagement, and TikTok had a negative sentiment bias. Hashtags such as #eco, #consciousfashion, and #sustainabilitygoal triggered the highest number of positive sentiments, whereas #sustainablefabric, #echofashion, and #greenfashion led to the most negative opinions. Brands are suggested to use new-generation and top-performing hashtags, match content with the platform's resonating audience, and give emphasis to authenticity and visual attraction to increase positive engagement. The paper makes a unique contribution to the literature by integrating computational sentiment analysis and cross-platform evaluation in an emerging market setting. It fills an important gap in current research on how to analyse the interaction of social media platform features, hashtag framing, and public sentiment in the Pakistani fashion industry.