Evaluating and improving social awareness of energy communities through semantic network analysis of online news

TL;DR

This paper uses semantic network analysis of online news to assess and enhance social awareness of energy communities, identifying information gaps and connections to support the energy transition.

Contribution

It introduces the Semantic Brand Score (SBS) as an innovative method combining social network analysis and text mining to evaluate media importance of energy topics.

Findings

Different importance trends for energy communities and related topics

Identification of connections between energy topics and societal issues

Detection of information gaps to promote low-carbon transition

Abstract

The implementation of energy communities represents a cross-disciplinary phenomenon that has the potential to support the energy transition while fostering citizens' participation throughout the energy system and their exploitation of renewables. An important role is played by online information sources in engaging people in this process and increasing their awareness of associated benefits. In this view, this work analyses online news data on energy communities to understand people's awareness and the media importance of this topic. We use the Semantic Brand Score (SBS) indicator as an innovative measure of semantic importance, combining social network analysis and text mining methods. Results show different importance trends for energy communities and other energy and society-related topics, also allowing the identification of their connections. Our approach gives evidence to information gaps and possible actions that could be taken to promote a low-carbon energy transition.