Beyond influencers: Leveraging large language models for dynamic content generation and citizen interaction in environmental campaigns

Addressing global environmental challenges necessitates an imperative shift in behavioural change and citizen engagement. This study centres on the integration of social media with Large Language Model (LLM) functionalities, sufficiently efficient in generating content relevant to the targeted audience, as a potential complement to influencer mediated content. This research paradigm is anchored in zero pollution initiatives and formulates a strategy for the semi-automated of engagement development by amalgamating social media intelligence, behavioural assessment, and content creation facilitated by LLM technology. This model encourages how LLM based behavioural assessment can support zero-pollution efforts by utilizing social media engagement in a targeted manner. This framework aids in scalability by measuring the associated outcomes and ensuring alignment with semantic interoperability models. The research further stipulates possible potentials of LLMs for contributing to social media activities by generating draft content or through responding to comments made, this allows the generation of adaptable and engaging campaigns. This is encouraged that the role of LLMs as complement influencer roles in certain contexts, potentially allowing for greater environmental advocacy should be investigated, helping overcome engagement barriers and encouraging citizen activism. Finally, this study describes the influence of LLMs in the creation of environmentally sustainable operations in urban areas and gaining public acceptance in policy considering LLM potential capabilities to provide supportive tools for timely and efficient changes and also discuss the challenges and limitations which can open the horizon for future studies.