Public perception and usage of the term carbon: Linguistic analysis in an environmental social media corpus

Tomara Gotkova; Nikolay Chepurnykh

doi:10.2478/plc-2022-14

Abstract

The main objective of this study was to examine the specialized environmental vocabulary which is incorporated in ordinary discourse found on the Internet. Specifically, we collected English-language data from two social networks, Twitter and Reddit, with a set of environmental keywords which was compiled using the term candidates extraction technique described in Shvets and Wanner (2020). Furthermore, using data from the initial corpus, we built a smaller sample of texts to serve as a ground for a preliminary linguistic analysis of the environmental term carbon. In environmental discourse, carbon is an umbrella term which has acquired multiple senses when it comes to greenhouse gas emissions. Additionally, this term has settled in ordinary communication (Fletcher & Downing, 2011). By means of a fine-grained manual linguistic analysis applied to the data sample, we identified five semantic patterns in the way the general public conceptualizes carbon in the environmental context.

References

Druide. (2021). Antidote 10 [Software]. https://www.druide.com
Search in Google Scholar Back to article
Bird, S., Klein, E., & Loper, E. (2009). Natural language processing with Python (1st. ed.). O’Reilly Media, Inc.
Search in Google Scholar Back to article
Boe, B. (2016). PRAW: The Python Reddit API Wrapper. PRAW [Software]. https://praw.readthedocs.io/en/v6.5.1/
Search in Google Scholar Back to article
Drouin, P., L’Homme, M.-C., & Robichaud, B. (2018). Lexical profiling of environmental corpora. In: N. Calzolari, K. Choukri, C. Cieri, T. Declerck, K. Hashida, H. Isahara, B. Maegaard, J. Mariani, A. Moreno, J. Odijk, S. Piperidis, & T. Tokunaga (Eds.), Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018) (pp. 3419–3425). European Language Resources Association.
Search in Google Scholar Back to article
Fletcher, J., & Downing, P. (2011). Consumer understanding of green terms: Asupplementary report on consumer responses to specific green terms – A report to the Department for Environment, Food and Rural Affairs. Brook Lyndhurst & Icaro Consulting, for Defra, London.
Search in Google Scholar Back to article
Gader, N., Ollinger, S., & Polguère, A. (2014). One lexicon, two structures: So what gives? In: H. Orav, C. Fellbaum, & P. Vossen (Eds.), Proceedings of the Seventh Global Wordnet Conference (GWC2014) (pp. 163–171). University of Tartu Press.
Search in Google Scholar Back to article
Intergovernmental Panel on Climate Change. (2014). Climate change 2014: Synthesis report. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change.
Search in Google Scholar Back to article
International Union of Pure and Applied Chemistry. (2021). Compendium of Chemical Terminology. Gold Book. https://goldbook.iupac.org/
Search in Google Scholar Back to article
Leskovec, J., Rajaraman, A., & Ullman, J. D., (2020). Mining of massive datasets (pp. 78–137). Cambridge University Press.10.1017/9781108684163
Search in Google Scholar Back to article
McCallum, A. K. (2002). Mallet: MAchine Learning for LanguagE Toolkit. GitHub [Software]. https://mimno.github.io/Mallet/index
Search in Google Scholar Back to article
Mel’čuk, I., Clas, A., & Polguère, A. (1995). Introduction à la lexicologie explicative et combinatoire. Duculot/Aupelf-UREF.
Search in Google Scholar Back to article
Mel’čuk, I. (1997). Vers une linguistique Sens-Texte. http://olst.ling.umontreal.ca/pdf/MelcukColldeFr.pdf
Search in Google Scholar Back to article
Polguère, A. (2014). From writing dictionaries to weaving lexical networks. International Journal of Lexicography, Oxford University Press, 27(4), 396– 418. https://doi.org/10.1093/ijl/ecu017
Search in Google Scholar Back to article
Řehůřek, R., & Sojka, P. (2010). Software framework for topic modelling with large corpora. In: R. Witte, H. Cunningham, J. Patrick, E. Beisswanger, E. Buyko, U. Hahn, K. Verspoor, & A. R. Coden (Eds.), Proceedings of the LREC 2010 Workshop on New Challenge for NLP Frameworks (pp. 45–50). VELRA.
Search in Google Scholar Back to article
Roussel, S. H. (2019). The green web: Evaluating online news communities and their environmentalism [Thesis]. https://digitalcommons.humboldt.edu/etd/320.
Search in Google Scholar Back to article
Shvets, A., & Wanner, L. (2020). Concept extraction using pointer-generator networks and distant supervision for data augmentation. In: A. Teije, J. Völker, S. Handschuh, H. Stuckenschmidt, M. d’Acquin, A. Nikolov, N. Aussenac-Gilles, & N. Hernandez (Eds.), Knowledge engineering and knowledge management (pp. 120–135). Springer.10.1007/978-3-030-61244-3_8
Search in Google Scholar Back to article
Statista. (2021). Most popular social networks worldwide as of October 2021, ranked by number of active users. https://www.statista.com/statistics/272014/global-social-networks-ranked-by-number-of-users/
Search in Google Scholar Back to article
Twitter. (2021). Consuming streaming data. Twitter Developer Platform. https://developer.twitter.com/en/docs/tutorials/consuming-streaming-data
Search in Google Scholar Back to article
Twitter Developer Platform. (2021). Get started with the Twitter Developer Platform. Developer Twitter. from https://developer.twitter.com/en/docs/platform-overview
Search in Google Scholar Back to article

Public perception and usage of the term carbon: Linguistic analysis in an environmental social media corpus

Abstract

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