{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T16:11:53Z","timestamp":1783613513558,"version":"3.55.0"},"reference-count":34,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,3,29]],"date-time":"2024-03-29T00:00:00Z","timestamp":1711670400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,3,29]],"date-time":"2024-03-29T00:00:00Z","timestamp":1711670400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Soc. Netw. Anal. Min."],"abstract":"<jats:title>Abstract<\/jats:title>\n          <jats:p>A prominent media topic in the UK in the early 2020s is the energy crisis affecting the UK and most of Europe. It brings into a single public debate issues of energy dependency and sustainability, fair distribution of economic burdens and cost of living, as well as climate change, risk, and sustainability. In this paper, we investigate the public discourse around the energy crisis and cost of living to identify how these pivotal and contradictory issues are reconciled in this debate and to identify which social actors are involved and the role they play. We analyse a document corpus retrieved from UK newspapers from January 2014 to March 2023. We apply a variety of natural language processing and data visualisation techniques to identify key topics, novel trends, critical social actors, and the role they play in the debate, along with the sentiment associated with those actors and topics. We combine automated techniques with manual discourse analysis to explore and validate the insights revealed in this study. The findings verify the utility of these techniques by providing a flexible and scalable pipeline for discourse analysis and providing critical insights for cost of living\u2014energy crisis nexus research.<\/jats:p>","DOI":"10.1007\/s13278-024-01233-w","type":"journal-article","created":{"date-parts":[[2024,3,29]],"date-time":"2024-03-29T07:01:42Z","timestamp":1711695702000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Crisis talk: analysis of the public debate around the energy crisis and cost of living"],"prefix":"10.1007","volume":"14","author":[{"given":"Rrubaa","family":"Panchendrarajan","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Geri","family":"Popova","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tony","family":"Russell-Rose","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,3,29]]},"reference":[{"issue":"1","key":"1233_CR1","doi-asserted-by":"publisher","first-page":"570","DOI":"10.3906\/elk-1703-79","volume":"26","author":"K Ak","year":"2018","unstructured":"Ak K, Toprak C, Esgel V, Yildiz OT (2018) Construction of a Turkish proposition bank. Turk J Electr Eng Comput Sci 26(1):570\u2013581","journal-title":"Turk J Electr Eng Comput Sci"},{"key":"1233_CR2","doi-asserted-by":"publisher","DOI":"10.1016\/j.dcm.2021.100564","author":"M Bednarek","year":"2022","unstructured":"Bednarek M, Ross AS, Boichak O, Doran Y, Carr G, Altmann EG, Alexander TJ (2022) Winning the discursive struggle? The impact of a significant environmental crisis event on dominant climate discourses on Twitter. Discourse Context Media. https:\/\/doi.org\/10.1016\/j.dcm.2021.100564","journal-title":"Discourse Context Media"},{"key":"1233_CR3","doi-asserted-by":"publisher","first-page":"583","DOI":"10.1016\/j.jclepro.2018.06.212","volume":"197","author":"LL Benites-Lazaro","year":"2018","unstructured":"Benites-Lazaro LL, Giatti L, Giarolla A (2018a) Sustainability and governance of sugarcane ethanol companies in Brazil: topic modeling analysis of CSR reporting. J Clean Prod 197:583\u2013591","journal-title":"J Clean Prod"},{"key":"1233_CR4","doi-asserted-by":"publisher","first-page":"318","DOI":"10.1016\/j.erss.2018.07.031","volume":"45","author":"LL Benites-Lazaro","year":"2018","unstructured":"Benites-Lazaro LL, Giatti L, Giarolla A (2018b) Topic modeling method for analyzing social actor discourses on climate change, energy and food security. Energy Res Soc Sci 45:318\u2013330","journal-title":"Energy Res Soc Sci"},{"key":"1233_CR5","doi-asserted-by":"crossref","unstructured":"Blei DM, Lafferty JD (2006) Dynamic topic models. In: Proceedings of the 23rd international conference on machine learning, pp 113\u2013120","DOI":"10.1145\/1143844.1143859"},{"key":"1233_CR6","first-page":"993","volume":"3","author":"DM Blei","year":"2003","unstructured":"Blei DM, Ng AY, Jordan MI (2003) Latent dirichlet allocation. J Mach Learn Res 3:993\u20131022","journal-title":"J Mach Learn Res"},{"key":"1233_CR7","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9780511978586","volume-title":"Who speaks for the climate? Making sense of media reporting on climate change","author":"M Boykoff","year":"2011","unstructured":"Boykoff M (2011) Who speaks for the climate? Making sense of media reporting on climate change. Cambridge University Press, Cambridge"},{"key":"1233_CR8","doi-asserted-by":"publisher","DOI":"10.1017\/9781108164047","volume-title":"Creative (climate) communications: productive pathways for science, policy and society","author":"M Boykoff","year":"2019","unstructured":"Boykoff M (2019) Creative (climate) communications: productive pathways for science, policy and society. Cambridge University Press, Cambridge"},{"key":"1233_CR9","doi-asserted-by":"publisher","first-page":"223","DOI":"10.1177\/0963662506066775","volume":"16","author":"A Carvalho","year":"2007","unstructured":"Carvalho A (2007) Ideological cultures and media discourses on scientific knowledge: re-reading news on climate change. Public Understand Sci 16:223\u2013243","journal-title":"Public Understand Sci"},{"key":"1233_CR10","doi-asserted-by":"crossref","unstructured":"Choi Y, Jung Y, Myaeng S-H (2010) Identifying controversial issues and their sub-topics in news articles. In: Intelligence and security informatics: Pacific Asia workshop, Paisi 2010, Hyderabad, India, June 21, 2010. Proceedings, pp 140\u2013153","DOI":"10.1007\/978-3-642-13601-6_16"},{"key":"1233_CR11","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s13278-019-0568-8","volume":"9","author":"B Dahal","year":"2019","unstructured":"Dahal B, Kumar SA, Li Z (2019) Topic modeling and sentiment analysis of global climate change tweets. Soc Netw Anal Min 9:1\u201320","journal-title":"Soc Netw Anal Min"},{"issue":"2","key":"1233_CR12","doi-asserted-by":"publisher","first-page":"32","DOI":"10.1016\/j.ipm.2014.10.006","volume":"51","author":"L Derczynski","year":"2015","unstructured":"Derczynski L, Maynard D, Rizzo G, Van Erp M, Gorrell G, Troncy R, Bontcheva K (2015) Analysis of named entity recognition and linking for tweets. Inf Process Manag 51(2):32\u201349","journal-title":"Inf Process Manag"},{"key":"1233_CR13","doi-asserted-by":"crossref","unstructured":"Gardner M, Grus J, Neumann M, Tafjord O, Dasigi P, Liu NF, Peters M,Schmitz M, Zettlemoyer LS (2017) AllenNLP: a deep semantic natural language processing platform. arXiv:1803.07640","DOI":"10.18653\/v1\/W18-2501"},{"key":"1233_CR14","doi-asserted-by":"publisher","DOI":"10.1093\/applin\/amad007","author":"M Gillings","year":"2023","unstructured":"Gillings M, Dayrell C (2023) Climate change in the UK press: Examining discourse fluctuation over time. Appl Linguistics. https:\/\/doi.org\/10.1093\/applin\/amad007","journal-title":"Appl Linguistics"},{"issue":"1","key":"1233_CR15","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1093\/pan\/mpp034","volume":"18","author":"J Grimmer","year":"2010","unstructured":"Grimmer J (2010) A Bayesian hierarchical topic model for political texts: Measuring expressed agendas in Senate press releases. Polit Anal 18(1):1\u201335","journal-title":"Polit Anal"},{"key":"1233_CR16","doi-asserted-by":"crossref","unstructured":"Hamborg F, Donnay K, Merlo P et al (2021) NewsMTSC: a dataset for (multi-) target-dependent sentiment classification in political news articles. In: Proceedings of the 16th conference of the European chapter of the association for computational linguistics: main volume, pp 1663\u20131675","DOI":"10.18653\/v1\/2021.eacl-main.142"},{"key":"1233_CR17","doi-asserted-by":"crossref","unstructured":"Handler A, Denny M, Wallach H, O\u2019Connor B. (2016). Bag of what? Simple noun phrase extraction for text analysis. In: Proceedings of the first workshop on NLP and computational social science, pp 114\u2013124","DOI":"10.18653\/v1\/W16-5615"},{"key":"1233_CR18","unstructured":"Hoffman AJ (2015) How culture shapes the climate change debate. Stanford University Press"},{"key":"1233_CR19","doi-asserted-by":"publisher","first-page":"15169","DOI":"10.1007\/s11042-018-6894-4","volume":"78","author":"H Jelodar","year":"2019","unstructured":"Jelodar H, Wang Y, Yuan C, Feng X, Jiang X, Li Y, Zhao L (2019) Latent dirichlet allocation (LDA) and topic modeling: models, applications, a survey. Multimedia Tools Appl 78:15169\u201315211","journal-title":"Multimedia Tools Appl"},{"issue":"1","key":"1233_CR20","doi-asserted-by":"publisher","first-page":"34","DOI":"10.1515\/jwl-2022-0004","volume":"8","author":"M Liu","year":"2022","unstructured":"Liu M, Huang J (2022) \u201cClimate change\u2019\u2019 vs. \u201cglobal warming\u2019\u2019: a corpus-assisted discourse analysis of two popular terms in the New York Times. J World Lang 8(1):34\u201355","journal-title":"J World Lang"},{"key":"1233_CR21","doi-asserted-by":"crossref","unstructured":"Maynard D, Bontcheva K (2015) Understanding climate change tweets: an open source toolkit for social media analysis. Enviroinfo and ict for sustainability 2015, pp 242\u2013250","DOI":"10.2991\/ict4s-env-15.2015.28"},{"key":"1233_CR22","unstructured":"Mishra P, Mittal R (2021) NeuralNERE: neural named entity relationship extraction for end-to-end climate change knowledge graph construction. In: ICML 2021 workshop on tackling climate change with machine learning"},{"key":"1233_CR23","unstructured":"OpenAI (2023) Gpt-4 technical report"},{"key":"1233_CR24","unstructured":"Pak A, Paroubek P et al (2010) Twitter as a corpus for sentiment analysis and opinion mining. Lrec, vol 10, pp 1320\u20131326"},{"issue":"1","key":"1233_CR25","doi-asserted-by":"publisher","first-page":"71","DOI":"10.1162\/0891201053630264","volume":"31","author":"M Palmer","year":"2005","unstructured":"Palmer M, Gildea D, Kingsbury P (2005) The proposition bank: an annotated corpus of semantic roles. Comput Linguist 31(1):71\u2013106","journal-title":"Comput Linguist"},{"issue":"1","key":"1233_CR26","doi-asserted-by":"publisher","first-page":"209","DOI":"10.1111\/j.1540-5907.2009.00427.x","volume":"54","author":"KM Quinn","year":"2010","unstructured":"Quinn KM, Monroe BL, Colaresi M, Crespin MH, Radev DR (2010) How to analyze political attention with minimal assumptions and costs. Am J Polit Sci 54(1):209\u2013228","journal-title":"Am J Polit Sci"},{"key":"1233_CR27","doi-asserted-by":"crossref","unstructured":"Rao D, McNamee P, Dredze M (2013) Entity linking: finding extracted entities in a knowledge base. Multi-source, multilingual information extraction and summarization, pp 93\u2013115","DOI":"10.1007\/978-3-642-28569-1_5"},{"issue":"4","key":"1233_CR28","doi-asserted-by":"publisher","first-page":"491","DOI":"10.1080\/17524032.2014.983534","volume":"9","author":"S Rebich-Hespanha","year":"2015","unstructured":"Rebich-Hespanha S, Rice RE, Montello DR, Retzloff S, Tien S, Hespanha JP (2015) Image themes and frames in US print news stories about climate change. Environ Commun 9(4):491\u2013519","journal-title":"Environ Commun"},{"key":"1233_CR29","doi-asserted-by":"crossref","unstructured":"R\u00f6der M, Both A, Hinneburg A (2015) Exploring the space of topic coherence measures. In: Proceedings of the eighth ACM international conference on web search and data mining, pp 399\u2013408","DOI":"10.1145\/2684822.2685324"},{"key":"1233_CR30","doi-asserted-by":"crossref","unstructured":"Schmitt X, Kubler S, Robert J, Papadakis M, LeTraon Y (2019) A replicable comparison study of NER software: StanfordNLP, NLTK, openNLP, SpaCy, Gate. In: 2019 sixth international conference on social networks analysis, management and security (SNAMS), pp 338\u2013343","DOI":"10.1109\/SNAMS.2019.8931850"},{"issue":"10","key":"1233_CR31","doi-asserted-by":"publisher","first-page":"1825","DOI":"10.1109\/TKDE.2018.2812203","volume":"30","author":"J Shang","year":"2018","unstructured":"Shang J, Liu J, Jiang M, Ren X, Voss CR, Han J (2018) Automated phrase mining from massive text corpora. IEEE Trans Knowl Data Eng 30(10):1825\u20131837","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"1233_CR32","doi-asserted-by":"crossref","unstructured":"Stede M, Patz R (2021) The climate change debate and natural language processing. In: Proceedings of the 1st workshop on nlp for positive impact, pp 8\u201318","DOI":"10.18653\/v1\/2021.nlp4posimpact-1.2"},{"key":"1233_CR33","doi-asserted-by":"publisher","first-page":"4","DOI":"10.17576\/gema-2021-2104-11","volume":"21","author":"TE Taufek","year":"2021","unstructured":"Taufek TE, Nor NFM, Jaludin A, Tiun S, Choy LK (2021) Public perceptions on climate change: a sentiment analysis approach. GEMA Online J Lang Stud 21:4","journal-title":"GEMA Online J Lang Stud"},{"key":"1233_CR34","doi-asserted-by":"crossref","unstructured":"Volkanovska E, Tan S, Duan C, Bartsch S, Stille W (2023) The insightsNet climate change corpus (ICCC) compiling a multimodal corpus of discourses in a multi-disciplinary domain. Datenbank-Spektrum 23:177\u2013188","DOI":"10.1007\/s13222-023-00454-1"}],"container-title":["Social Network Analysis and Mining"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13278-024-01233-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13278-024-01233-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13278-024-01233-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,2,25]],"date-time":"2025-02-25T14:19:06Z","timestamp":1740493146000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13278-024-01233-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,3,29]]},"references-count":34,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["1233"],"URL":"https:\/\/doi.org\/10.1007\/s13278-024-01233-w","relation":{},"ISSN":["1869-5469"],"issn-type":[{"value":"1869-5469","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,3,29]]},"assertion":[{"value":"6 November 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 February 2024","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 February 2024","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 March 2024","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}],"article-number":"74"}}