Energy communities represent a novel social phenomenon that has the potential to support the energy transition by fostering the participation of citizens across the energy system. To involve people in this process and raise their awareness on the associated benefits, an important role is played by online information sources. In this view, this work analyses online news data about energy communities to understand people awareness and media importance of this topic. To this aim, the Semantic Brand Score (SBS) index, combining social network analysis and text mining, is used as an innovative measure of semantic importance. The outcomes of the analysis show that the SBS approach is flexible and useful to investigate media importance of energy communities; it also serves the identification of other energy and sustainability themes and their connections. The results support the identification of information gaps and of possible actions that should be taken to promote a low-carbon energy transition.
Evaluating social awareness of energy communities through semantic network analysis of online news / Piselli, C.; Fronzetti Colladon, A.; Segneri, L.; Pisello, A. L. - ELETTRONICO. - (2021), pp. 1-15. (Intervento presentato al convegno 16th Conference on Sustainable Development of Energy, Water and Environment Systems (SDEWES) tenutosi a Dubrovnik, Croatia nel October 10-15, 2021).
Evaluating social awareness of energy communities through semantic network analysis of online news
Piselli, C.;
2021
Abstract
Energy communities represent a novel social phenomenon that has the potential to support the energy transition by fostering the participation of citizens across the energy system. To involve people in this process and raise their awareness on the associated benefits, an important role is played by online information sources. In this view, this work analyses online news data about energy communities to understand people awareness and media importance of this topic. To this aim, the Semantic Brand Score (SBS) index, combining social network analysis and text mining, is used as an innovative measure of semantic importance. The outcomes of the analysis show that the SBS approach is flexible and useful to investigate media importance of energy communities; it also serves the identification of other energy and sustainability themes and their connections. The results support the identification of information gaps and of possible actions that should be taken to promote a low-carbon energy transition.
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