DYNA JOURNAL ENGINEERING

Key words:

Green hydrogen, social media, text mining, artificial neural networks, social network analysis, social perception, technology and social change, Twitter, Hidrógeno verde, medios sociales, minería de textos, redes neuronales artificiales, análisis de redes sociales, percepción social, tecnología y cambio social

Article type:

COLABORACION/COLLABORATION DAMR

Section:

COLLABORATIONS

Given the current state of climate change and the fight against it, green hydrogen has the potential to be the energy vector of the future within a context of decarbonisation. In this way, fossil fuels might be replaced by green hydrogen in those sectors that are most difficult to decarbonise. Therefore, in recent years it is becoming more and more common to hear about green hydrogen in society and the great advantages of using it, although, it also has some disadvantages, mostly related to higher costs and higher energy consumption. With this in mind, the purpose of this paper is to use social media intelligence for the topical issue of green hydrogen; i.e. the objective is to gather and analyse the social perception of it. For this purpose, taking the social media platform Twitter as our source of information, we extracted 625,794 green hydrogen tweets for the years 2020, 2021 and 2022. On the one hand, a social network analysis was carried out, obtaining different metrics and identifying the main communities that have been talking about green hydrogen in the digital sphere over the last three years. On the other hand, using artificial neural networks, a sentiment classification model was applied to the tweets with the aim of detecting the emotion generated by society. Accordingly, results were obtained and interpreted through a novel methodological combination. The network analysis revealed that the conversation taking place is not polarized and is a reflection of the digital agora that brings together citizens, media actors and actors from the economic-political space. In addition, the sentiment analysis conducted shows that the overall digital discussion is positive.

Keywords: Green hydrogen, social media, text mining, artificial neural networks, social network analysis, social perception, technology and social change, Twitter