Green energy: identifying development trends in society using Twitter data mining to make strategic decisions



Palabras clave:

Green energy, Twitter, Social network analysis, Semantic analysis, Sentiment analysis, Big data, Business intelligence, Data analytics, Text analytics, Social analytics, Social networks, Social media, Environment, Renewable energy


This study analyzes Twitter’s contribution to green energy. More than 200,000 global tweets sent during 2020 containing the terms “green energy” OR “greenenergy” were analyzed. The tweets were captured by web scraping and processed using algorithms and techniques for the analysis of massive datasets from social networks. In particular, relationships between users (through mentions) were determined according to the Louvain multilevel algorithm to identify communities and analyze global (density and centralization) and node-level (centrality) metrics. Subsequently, the content of the conversation was subject to semantic analysis (co-occurrence of the most relevant words), hashtag analysis (frequency analysis), and sentiment analysis (using the Vader model). The results reveal nine main communities and their leaders, as well as three main topics of conversation and the emotional state of the digital discussion. The main communities revolve around politics, socioeconomic issues, and environmental activism, while the conversations, which have developed mostly in positive terms, focus on green energy sources and storage, being aligned with the main communities identified, i.e., on political, socioeconomic, and climate change issues. Although most of the conversations have been about socioeconomic issues, the presence of leading company accounts was minor. The main aim of this work is to take the first steps toward an innovative competitive intelligence methodology to study and determine trends within different scientific fields or technologies in society that will enable strategic decisions to be made.


Adani (2020). “Adani green energy wins the world’s largest solar award”. Newsroom of Adani Green Energy Limited (AGEL), Jun 9.

Adi, Ana (2018). “#Sustainability on Twitter: loose ties and green-washing CSR”. In: Grigore, Georgiana; Stancu, Alin; McQueen, David (eds.). Corporate responsibility and digital communities. An international perspective towards sustainability. Palgrave studies in governance, leadership and responsibility. Palgrave Macmillan, pp. 99-122. ISBN: 978 3 319 63480 7

Aguilar-Gallegos, Norman; Martínez-González, Enrique-Genaro; Aguilar-Ávila, Jorge (2017). Análisis de redes sociales: conceptos clave y cálculo de indicadores. Chapingo, México: Universidad Autónoma Chapingo (UACh). Centro de investigaciones económicas, sociales y tecnológicas de la agroindustria y la agricultura mundial (Ciestaam). ISBN: 978 607 12 0487 5

Aguilar-Gallegos, Norman; Martínez-González, Enrique-Genaro; Aguilar-Ávila, Jorge; Santoyo-Cortés, Horacio; Muñoz-Rodríguez, Manrrubio; García-Sánchez, Edgar-Iván (2016). “Social network analysis for catalysing agricultural innovation: from direct ties to integration and radiality”. Estudios gerenciales, v. 32, n. 140, pp. 197-207.

Ahuja, Vandana; Shakeel, Moonis (2017). “Twitter presence of jet airways-deriving customer insights using netnography and wordclouds”. Procedia computer science, v. 122, pp. 17-24.

Bastian, Mathieu; Heymann, Sebastien; Jacomy, Mathieu (2009). “Gephi: an open source software for exploring and manipulating networks”. In: International AAAI conference on weblogs and social media.

Blondel, Vincent D.; Guillaume, Jean-Loup; Lambiotte, Renaud; Lefebvre, Etienne (2008). “Fast unfolding of communities in large networks”. Journal of statistical mechanics: theory and experiment, v. 8, n. 10, pp. 1-12.

Borgatti, Stephen P.; Everett, Martin G.; Johnson, Jeffrey C. (2013). Analyzing social networks. London: SAGE Publications Limited. ISBN: 978 1 52 64 0410 7

Campolo, Alex; Sanfilippo, Madelyn; Whittaker, Meredith; Crawford, Kate (2017). AI now 2017 report.

Carvalho, Anabela (2009). “Communication for sustainable policy: connecting science, society and government”. Science for environment policy. Environmental communication, n. 17.

Casero-Ripollés, Andreu (2018). “Research on political information and social media: Key points and challenges for the future”. El profesional de la información, v. 27, n. 5, pp. 964-974.

Chamorro, Verónica; Rivera, Richard; Varela-Aldás, José; Castillo-Salazar, David; Borja-Galeas, Carlos; Guevara, César; Arias-Flores, Hugo; Fierro-Saltos, Washington; Hidalgo-Guijarro, Jairo; Yandún-Velasteguí, Marco (2020). “Twitter mining for multiclass classification events of traffic and pollution”. In: IHSED 2019. International conference on human systems engineering and design: Future trends and applications, pp. 1030-1036.

Chen, Wenhong; Tu, Fangjing; Zheng, Pei (2017). “A transnational networked public sphere of air pollution: analysis of a Twitter network of PM2.5 from the risk society perspective”. Information, communication & society, v. 20, n. 7, pp. 1005-1023.

Cody, Emily M.; Reagan, Andrew J.; Mitchell, Lewis; Dodds, Peter-Sheridan; Danforth, Christopher M. (2015). “Climate change sentiment on Twitter: an unsolicited public opinion poll”. PloS one, v. 10, n. 8.

Cossu, Jean-Valère; Dugué, Nicolas; Labatut, Vincent (2015). “Detecting real-world influence through Twitter”. In: 2nd European network intelligence conference.

Dahal, Biraj; Kumar, Sathish A. P.; Li, Zhenlong (2019). “Topic modeling and sentiment analysis of global climate change tweets”. Social network analysis and mining, v. 9, art. 24.

De-Nooy, Wouter; Mrvar, Andrej; Batagelj, Vladimir (2018). Exploratory social network analysis with Pajek. Cambridge University Press. ISBN: 978 1 108 47414 6

Edizel, Bora; Bonchi, Francesco; Hajian, Sara; Panisson, André; Tassa, Tamir (2020). “FaiRecSys: mitigating algorithmic bias in recommender systems”. International journal of data science and analytics, v. 9, pp. 197-213.

EPA (2019). What is green power?. United States environmental protection Agency.

European Parliament (2018). “Directive (EU) 2018/2001 of the European parliament and of the council on the promotion of the use of energy from renewable sources”. Official journal of the European Union, n. L 328, 21/12/2018.

Fernández-Arias, Pablo (2017). Análisis de los factores que influyeron en la evolución y desarrollo del reactor nuclear PWR. Tesis doctoral. Universidad de Salamanca.

Fernández-Arias, Pablo; Cuevas, Ana; Vergara, Diego (2021). “Controversia nuclear en España: la central de Lemóniz”. Revista CTS, v. 16, n. 46, pp. 199-218.

Freeman, Linton C. (1978). “Centrality in social networks: conceptual clarification”. Social networks, v. 1, n. 3, pp. 215-239.

Gibbs, Jeff; Moore, Michael (2019). Planet of the humans.

González-Fernández-Villavicencio, Nieves (2014). “El #hashtag ya tiene historia”. Anuario ThinkEPI, v. 8, pp. 326-330.

Gupta, Kuhika; Ripberger, Joseph; Wehde, Wesley (2018). “Advocacy group messaging on social media: using the narrative policy framework to study Twitter messages about nuclear energy policy in the United States”. Policy studies journal, v. 46, n. 1, pp. 119-136.

Hanneman, Robert A. (2001). “Introducción a los métodos del análisis de redes sociales. Capítulo sexto: centralidad y poder”. In: Introducción a los métodos del análisis de redes sociales.

Holmberg, Kim; Hellsten, Iina (2015). “Gender differences in the climate change communication on Twitter”. Internet research, v. 25, n. 5, pp. 811-828.

Hutto, Clayton J.; Gilbert, Eric (2014). “Vader: A parsimonious rule-based model for sentiment analysis of social media text”. In: Proceedings of the 8th international AAAI conference on weblogs and social media, pp. 216-225.

Jain, Achin; Jain, Vanita (2019). “Sentiment classification of Twitter data belonging to renewable energy using machine learning”. Journal of information and optimization sciences, v. 40, n. 2, pp. 521-533.

Khan, M. Ali-ud-din; Uddin, Muhammad-Fahim; Gupta, Navarun (2014). “Seven V’s of big data understanding big data to extract value”. In: Proceedings of the 2014 zone 1 conference of the American Society for Engineering Education, pp.1-5.

Khatua, Aparup; Cambria, Erik; Ho, Shirley S.; Na, Jin-Cheon (2020). “Deciphering public opinion of nuclear energy on Twitter”. In: 2020 International joint conference on neural networks (IJCNN).

Kim, Jiyoun; Brossard, Dominique; Scheufele, Dietram A.; Xenos, Michael (2016). “Shared” information in the age of big data: exploring sentiment expression related to nuclear energy on Twitter”. Journalism and mass communication quarterly, v. 93, n. 2, pp. 430-445.

Kim, Serena Y.; Ganesan, Koushik; Dickens, Princess; Panda, Soumya (2020). “Public sentiment toward solar energy: opinion mining of Twitter using a transformer-based language model”. Sustainability, v. 13, n. 5, 2673.

Kramer, Adam D. I.; Guillory, Jamie E.; Hancock, Jeffrey T. (2014). “Experimental evidence of massive-scale emotional contagion through social networks”. In: Proceedings of the National Academy of Sciences of the United States of America, v. 111, n. 24, pp. 8788-8790.

Labonte, Dane; Rowlands, Ian H. (2021). “Tweets and transitions: exploring Twitter-based political discourse regarding energy and electricity in Ontario, Canada”. Energy research and social science, v. 72, p. 101870.

Laney, Doug (2001). 3D data management: controlling data volume, velocity, and variety. META Group Inc.

Larrondo-Ureta, Ainara; Morales-i-Gras, Jordi; Orbegozo-Terradillos, Julen (2019). “Feminist hashtag activism in Spain: Measuring the degree of politicisation of online discourse on #yosítecreo, #hermanayosítecreo, #cuéntalo y #noestássola”. Communication & society, v. 32, n. 4, pp. 207-221.

Li, Qiudan; Jin, Zhipeng; Wang, Can; Zeng, Daniel-Dajun (2016). “Mining opinion summarizations using convolutional neural networks in Chinese microblogging systems”. Knowledge-based systems, v. 107, pp. 289-300.

Li, Ruopu; Crowe, Jessica; Leifer, David; Zou, Lei; Schoof, Justin (2019). “Beyond big data: social media challenges and opportunities for understanding social perception of energy”. Energy research and social science, v. 56, p. 101217.

Ljubljana University (2021). Orange data mining.

Macmillan, Gordon (2015). “8 reasons why Twitter is the home of TV conversation”. Twitter blog, 29 September.

Margolin, Drew; Liao, Wang (2018). “The emotional antecedents of solidarity in social media crowds”. New media & society, v. 20, n. 10, pp. 3700-3719.

Microsoft (2021). About power query in Excel - Excel.

Mooney, Peter; Winstanley, Adam; Corcoran, Padraig (2009). “Evaluating Twitter for use in environmental awareness campaigns”. Proceedings of the China-Ireland information and communications technologies conference (CIICT 2009). Maynooth: Department of Computer science, NUI Maynooth ER, pp. 83-86.

Morales-i-Gras, Jordi (2017). Soberanías enredadas: una perspectiva reticular, constructural y agéntica hacia los relatos soberanistas vasco y catalán contemporáneos en Twitter. Tesis doctoral. Universidad del País Vasco / Euskal Herriko Unibertsitatea.

Morales-i-Gras, Jordi (2020). Datos masivos y minería de datos sociales: conceptos y herramientas básicas.

Mrvar, Andrej; Batagelj, Vladimir (2021). Programs for analysis and visualization of very large networks. Reference manual.

Newman, Mark E. J.; Girvan, Michelle (2004). “Finding and evaluating community structure in networks”. Physical review E, v. 69, n. 2, 026113.

Oguntimilehin, Abiodun; Ademola, Emmanuel-Ojo (2014). “A review of big data management, benefits and challenges”. Journal of emerging trends in computing and information sciences, v. 5, n. 6, pp. 433-438.

OpenRefine (2021). A free, open source, powerful tool for working with messy data.

Orbegozo-Terradillos, Julen; Larrondo-Ureta, Ainara; Morales-i-Gras, Jordi (2020). “Influencia del género en los debates electorales en España: análisis de la audiencia social en #ElDebateDecisivo y #L6Neldebate”. El profesional de la información, v. 29, n. 2.

Orbegozo-Terradillos, Julen; Morales-i-Gras, Jordi; Larrondo-Ureta, Ainara (2019). “Feminismos indignados ante la justicia: la conversación digital en el caso de La Manada”. IC revista científica de información y comunicación, n. 16, pp. 211-247.

Patgiri, Ripon; Ahmed, Arif (2016). “Big data : The V’s of the game changer paradigm”. In: 2016 IEEE 18th international conference on high performance computing and communications; IEEE 14th international conference on smart city; IEEE 2nd international conference on data science and systems (HPCC/SmartCity/DSS), pp. 17-24.

Pilař, Ladislav; Kvasničková-Stanislavská, Lucie; Pitrová, Jana; Krejčí, Igor; Tichá, Ivana; Chalupová, Martina (2019). “Twitter analysis of global communication in the field of sustainability”. Sustainability, v. 11, n. 24, 6958.

Prabhakar, Kaila-Rajesh (2019). “Climate change and Twitter. An empirical analysis of environmental awareness and engagement”. Disaster advances, v. 12, n. 9, pp. 10-15.

Reboredo, Juan C.; Ugolini, Andrea (2018). “The impact of Twitter sentiment on renewable energy stocks”. Energy economics, v. 76, pp. 153-169.

Reyes-Menéndez, Ana; Saura, José-Ramón; Álvarez-Alonso, César (2018). “Understanding #worldenvironmentday user opinions in Twitter: a topic-based sentiment analysis approach”. International journal of environmental research and public health, v. 15, n. 11.

Ruiz-Soler, Javier (2017). “Twitter research for social scientists: a brief introduction to the benefits, limitations and tools for analysing Twitter data”. Dígitos, v. 1, n. 3, pp. 17-32.

Shen, Chien-Wen; Luong, Thai-Ha; Pham, Tuan (2021). “Exploration of social media opinions on innovation for sustainable development goals by topic modeling and sentiment analysis”. In: Research and innovation forum 2020. RiiForum 2020, pp. 459-471.

Soussan, Tariq; Trovati, Marcello (2020). “Twitter analysis for business intelligence”. In: Barolli Leonard; Nishino, Hsing-Chung; Miwa, Hiroyoshi. Advances in intelligent networking and collaborative systems. INCoS 2019. Advances in intelligent systems and computing, v. 1035, pp. 473-480. ISBN: 978 3 030 29035 1

Unesco (2021). Invertir en ciencia, tecnología e innovación.

Velázquez-Álvarez, O. Alejandro; Aguilar-Gallegos, Norman (2005). Manual introductorio al análisis de redes sociales (medidas de centralidad).

Veltri, Giuseppe A. (2012). “Microblogging and nanotweets: nanotechnology on Twitter”. Public understanding of science, v. 22, n. 7, pp. 832-849.

Veltri, Giuseppe A.; Atanasova, Dimitrinka (2017). “Climate change on Twitter: Content, media ecology and information sharing behavior”. Public understanding of science, v. 26, n. 6, pp. 721-737.

WordArt (2021). Word cloud art creator.

YouTube (2020). Donald Trump vs Joe Biden: Full presidential debate | US Election 2020.

Zeifer, Bárbara (2020). “El hashtag contestatario: cuando los hashtags tienen efectos políticos”. Digitos. Revista de comunicación digital, v. 6, pp. 101-118.



Cómo citar

Zarrabeitia-Bilbao, E., Morales-i-Gras, J., Rio-Belver, R. M., & Garechana-Anacabe, G. (2022). Green energy: identifying development trends in society using Twitter data mining to make strategic decisions. Profesional De La información, 31(1).



Artículos de investigación / Research articles


La descarga de datos todavía no está disponible.


Cargando métricas ...