Using large-language models to analyse social media engagement with the public sector communication on circular economy: Evidence from X

This study examines how public sector communication on circular economy (CE) initiatives influences social media engagement by analysing message elaboration, emotional tone (positive, negative, neutral), and message purpose (educational, advocacy, and policy-oriented). Grounded in the Elaboration Likelihood Model (ELM), we collected over 20,000 X (formerly Twitter) posts from 2015 to 2024 using Apify and classified them through zero-shot Large Language Model (GPT-4o-mini) analysis, validated against human coders. To establish causal effects rather than correlations, we applied a two-step approach: DirectLiNGAM for causal discovery and DoWhy for causal estimation with robustness tests. Results show that high-elaboration messages foster deeper engagement, negative tones evoke urgency; neutral tones support trust and advocacy-driven responses, while positive tones yield limited effects. Educational messages drive long-term behavioural change through systematic information processing; advocacy messages generate emotional resonance for rapid engagement, and policy-oriented messages enhance transparency but lack significant emotional impact. The study advances ELM by integrating emotional tone and message purpose into sustainability communication. Methodologically, it demonstrates how LLMs combined with causal inference methods can systematically analyse large-scale social media data. Practically, the findings provide actionable guidance for designing effective public campaigns to strengthen citizen engagement with CE goals. • Integrates emotional tone and message intent into the Elaboration Likelihood Model for CE engagement. • Proposes a framework combining GPT-4o-mini, DirectLiNGAM, and DoWhy for causal inference on X data. • Finds fact-based, high-elaboration messages drive deep cognitive and sustained behavioural engagement. • Negative tones trigger short bursts of engagement; neutral tones foster trust and long-term advocacy. • Educational posts shape lasting change; advocacy sparks quick reactions; policy messages yield highest impact.

Authors

Institutions

Publication Details

Journal
Technological Forecasting and Social Change
Published
2025-10-29
DOI
https://doi.org/10.1016/j.techfore.2025.124400
Citations
1
Primary Topic
Digital Marketing and Social Media
Type
article
Field-Weighted Citation Impact
4.20
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Using large-language models to analyse social media engagement with the public sector communication on circular economy: Evidence from X

Yulei Li, Shan Shan, Dongmei Cao, Michael P. Hewitt
1 citations
Technological Forecasting and Social Change
Digital Marketing and Social Media
4.20
article

Using large-language models to analyse social media engagement with the public sector communication on circular economy: Evidence from X

Yulei Li, Shan Shan, Dongmei Cao, Michael P. Hewitt
article en
1 citations

Abstract

This study examines how public sector communication on circular economy (CE) initiatives influences social media engagement by analysing message elaboration, emotional tone (positive, negative, neutral), and message purpose (educational, advocacy, and policy-oriented). Grounded in the Elaboration Likelihood Model (ELM), we collected over 20,000 X (formerly Twitter) posts from 2015 to 2024 using Apify and classified them through zero-shot Large Language Model (GPT-4o-mini) analysis, validated against human coders. To establish causal effects rather than correlations, we applied a two-step approach: DirectLiNGAM for causal discovery and DoWhy for causal estimation with robustness tests. Results show that high-elaboration messages foster deeper engagement, negative tones evoke urgency; neutral tones support trust and advocacy-driven responses, while positive tones yield limited effects. Educational messages drive long-term behavioural change through systematic information processing; advocacy messages generate emotional resonance for rapid engagement, and policy-oriented messages enhance transparency but lack significant emotional impact. The study advances ELM by integrating emotional tone and message purpose into sustainability communication. Methodologically, it demonstrates how LLMs combined with causal inference methods can systematically analyse large-scale social media data. Practically, the findings provide actionable guidance for designing effective public campaigns to strengthen citizen engagement with CE goals. • Integrates emotional tone and message intent into the Elaboration Likelihood Model for CE engagement. • Proposes a framework combining GPT-4o-mini, DirectLiNGAM, and DoWhy for causal inference on X data. • Finds fact-based, high-elaboration messages drive deep cognitive and sustained behavioural engagement. • Negative tones trigger short bursts of engagement; neutral tones foster trust and long-term advocacy. • Educational posts shape lasting change; advocacy sparks quick reactions; policy messages yield highest impact.

Technological Forecasting and Social ChangeVol. 222
Northumbria University (GB), Nottingham Trent University (GB)
Openalex Percentile: Top 5%
Digital Marketing and Social Media
4.20
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.