The growing availability of large-scale online data has created new opportunities for analysing public discourse on climate change, although the reconstruction of structured social representations within digital environments remains methodologically challenging. This study proposes a computational framework for adapting the Hierarchical Evocation Method (HEM), grounded in Social Representation Theory, to large-scale social media discourse. Using a continuously updated Reddit dataset on climate change, the approach combines theory-informed lexical anchoring with data-driven semantic expansion to construct a renewable energy subcorpus comprising 91,817 comments published between 2018 and 2026. Representational structures are reconstructed through user-level lexical diffusion, positional salience, rhetorical foregrounding, and co-occurrence analysis, enabling the identification of central, peripheral, and contrastive components within online discourse. The results reveal a relatively stabilised representational core centred on climate transition, fossil dependency, renewable infrastructures, and socio-economic transformation, while peripheral zones display greater contextual variability and evaluative fragmentation. Longitudinal analyses further suggest a progressive consolidation of renewable energy discourse despite high user turnover and sustained growth in participation. The framework additionally highlights the relevance of affective and interactional dimensions, particularly through the widespread use of ironic and sceptical emoji configurations. Methodologically, the study provides a transparent and reproducible computational pipeline that extends classical evocation-based approaches to large-scale, dynamic corpora. More broadly, the findings contribute to sustainability communication research by showing how renewable energy is collectively framed and negotiated within English Reddit-based digital discussions.
A Computational Pipeline for Hierarchical Evocation Analysis of Renewable Energy in Online Climate Discourse
Michelangelo Misuraca
;
2026
Abstract
The growing availability of large-scale online data has created new opportunities for analysing public discourse on climate change, although the reconstruction of structured social representations within digital environments remains methodologically challenging. This study proposes a computational framework for adapting the Hierarchical Evocation Method (HEM), grounded in Social Representation Theory, to large-scale social media discourse. Using a continuously updated Reddit dataset on climate change, the approach combines theory-informed lexical anchoring with data-driven semantic expansion to construct a renewable energy subcorpus comprising 91,817 comments published between 2018 and 2026. Representational structures are reconstructed through user-level lexical diffusion, positional salience, rhetorical foregrounding, and co-occurrence analysis, enabling the identification of central, peripheral, and contrastive components within online discourse. The results reveal a relatively stabilised representational core centred on climate transition, fossil dependency, renewable infrastructures, and socio-economic transformation, while peripheral zones display greater contextual variability and evaluative fragmentation. Longitudinal analyses further suggest a progressive consolidation of renewable energy discourse despite high user turnover and sustained growth in participation. The framework additionally highlights the relevance of affective and interactional dimensions, particularly through the widespread use of ironic and sceptical emoji configurations. Methodologically, the study provides a transparent and reproducible computational pipeline that extends classical evocation-based approaches to large-scale, dynamic corpora. More broadly, the findings contribute to sustainability communication research by showing how renewable energy is collectively framed and negotiated within English Reddit-based digital discussions.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
