A Computational Pipeline for Hierarchical Evocation Analysis of Renewable Energy in Online Climate Discourse
オンライン気候言説における再生可能エネルギーの階層的喚起分析のための計算パイプライン (AI 翻訳)
Michelangelo Misuraca, Luca D’Aniello, Maria Spano
🤖 gxceed AI 要約
日本語
本研究は、Redditの気候変動データ（2018-2026年、91,817コメント）を用いて、再生可能エネルギーに関する社会的表象を階層的喚起法（HEM）と計算手法を組み合わせて分析する。理論に基づく語彙アンカリングとデータ駆動の意味拡張により、表象の中心・周辺・対照的要素を特定し、言説の安定性と変動性を明らかにした。持続可能性コミュニケーション研究に貢献する。
English
This study analyzes renewable energy social representations in Reddit climate discourse (2018-2026, 91,817 comments) using a computational adaptation of the Hierarchical Evocation Method. Combining theory-informed lexical anchoring with data-driven semantic expansion, it identifies central, peripheral, and contrastive elements, revealing a stable core around climate transition and fossil dependency. Contributes to sustainability communication research.
Unofficial AI-generated summary based on the public title and abstract. Not an official translation.
📝 gxceed 編集解説 — Why this matters
日本のGX文脈において
日本では、再生可能エネルギーへの社会的受容が導入拡大の鍵であり、本手法はSNS上の世論形成を可視化する点で、政策立案や企業のコミュニケーション戦略に示唆を与える。SSBJ開示や統合報告書におけるステークホルダー対応にも応用可能。
In the global GX context
Globally, this work offers a reproducible computational pipeline for analyzing public discourse on renewable energy, relevant to understanding social acceptance and opposition. It complements climate disclosure frameworks by providing insights into stakeholder perceptions, useful for transition planning and sustainability communication.
👥 読者別の含意
🔬研究者:Provides a novel computational method for analyzing large-scale social media discourse on renewable energy, extending social representation theory.
🏢実務担当者:Offers insights into public perceptions of renewable energy that can inform stakeholder engagement and communication strategies.
🏛政策担当者:Highlights the importance of monitoring online discourse to anticipate social acceptance challenges in energy transition policies.
📄 Abstract（原文）
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.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.3390/su18147295first seen 2026-08-07 04:52:07
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