Critical Discourse Analysis: What Is It?

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A downloadable version of this explainer is available here: 

Overview: The Fundamentals

The language we use to communicate, along with other cultural artifacts such as images, films, music, and social media, has a life well beyond the simple denotation or literal meaning. Language is infused with normative cultural values, hegemonic power structures, bias, and subjectivity.

Critical Discourse Analysis (CDA) describes a series of approaches to how researchers (socio-environmental [S-E] and others) may critically analyze texts and cultural artifacts to reveal connotations and draw out the larger cultural narratives that these connotations support. What is the social context of the story being told and why is the teller relating it in this particular narrative structure, lexicon, and moment in time? Analysts study how language is used in discourse in order to:

• Consider the contexts in which texts are produced, distributed, and consumed.
• Designate the ways people use texts to construct a sense of self, society, and material reality.
• Explore a deeper context in which textual features influence wider social discourse, political stances, institutional values and choices, and to support or challenge hegemonies. (Burke 187)

The goal of CDA is to reveal submerged power structures and elaborate on the role of discourse in both reflecting and constructing social realities. It is based on the premise that language can reflect dominant cultural, political, gendered (etc.) views and practices whether conscious or unconscious. Some forms of CDA make use of big data, for example from social media posts, to analyze a range of cultural, demographic, geographic, and historical perspectives on human-environment interactions. Others use artificial intelligence and social network analysis to deduce how big data that consolidates language can be translated into network visuals and thematic clusters to illustrate cultural trends. But there are many other forms. This series of explainers elaborates on those most useful to S-E research.

CDA is a collection of approaches to analyzing texts that spans the disciplines and goes by many names: sentiment analysis, qualitative content analysis, opinion or emotion analysis, thematic analysis, narrative analysis, psychosocial analysis, visual discourse analysis, and ecolinguistics.   

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Theoretical Background: Barthes’ Denotation, Connotation, and Myth

Literary theorist Roland Barthes’ discourse theory of denotation, connotation, and myth elaborates on how social structures and norms impose submerged values on public discourse. For example:

Denotation:
1) A rose is a flower of the genus Rosa.
2) A rose is a saleable good with market values that support a multi-billion dollar industry.

Connotation:
1) A rose is a symbol of romance. It imbues values of passion, beauty, and faithfulness. 
2) A person who gives you roses loves you.

Cultural Narrative or Myth: You must buy roses for your beloved on Valentine’s Day!

The denoted “rose” extends to connote a culturally valued outcome: love and romance. The flower industry commodifies the connotation. That step allows the industry to fuel large-scale production and distribution to benefit the global flower industry. Then the discourse takes the next step to cultural myth: You must buy roses for your beloved on Valentine’s Day! 

Here, CDA unpacks how a simple material thing like a dozen roses becomes a cultural idea, recirculated annually through industry advertising. It reveals submerged economic priorities and shows how a flower’s social meaning translates to an environmentally impactful industry. Using CDA to review advertising produces fascinating results and provides tools to analyze problematic forms of environmental discourse like greenwashing. CDA methods serve a wide range of analytical needs.

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Sample Research Questions for CDA Analysis, with Illustrative Research Papers:

(Links to these papers are provided below.)

• How do denotation and connotation reveal submerged power structures and hegemonies? (Benjaminsen 2020; Stibbe 2015)
• How have various stakeholders spoken, written, and visualized their perspectives on particular S-E issues? (Burke et al. 2015) 
• How does the lack of discourse on a subject indicate erasures or subordinations of stakeholder perspectives or an incomplete understanding of the complexity of a S-E system? (Hoover et al. 2021)
• How can we translate texts into groupings, themes, and S-E network visuals that reveal relationships among system components? Can CDA reveal patterns of discourse that exhibit a range of perspectives in S-E systems? (Urbanitti et al. 2020)
• How can scientists working with stakeholders use language to empower their perspectives on the proper course of governance or management of S-E systems? (Lund et al. 2022)
• In what ways can quantitative analysis be a starting point for qualitative synthesis methods that support the integration of disciplinary silos, geographies, stakeholders, and governance regimes? (Keith et al. 2022)
• How can computer-based textual and visual analysis help us process and find significant trends in big data and social media activity? (Vigl et al. 2021)

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Further Reading

Further explainers are available on: 

• Visual Discourse Analysis
• Narrative Discourse Analysis
• Qualitative Content Analysis
• Artificial Intelligence, Social Network, and Social Media Analysis

These articles provide overviews and examples of how we may employ CDA to better understand how forms of discourse affect our perception and governance of S-E systems.     

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References

Blanc, G. The Invention of Green Colonialism. (H. Morrison, Trans.). Polity Press. (2022).

Burke, B.J., Welch-Divine, M., & Gustafson, S. (2015). Nature Talk in an Appalachian Newspaper: What Environmental Discourse Analysis Reveals about Efforts to Address Exurbanization and Climate Change. Human Organization, 74(2), 185–196. https://doi.org/10.17730/0018-7259-74.2.185

Benjaminsen, T.A. (2021). Depicting decline: images and myths in environmental discourse analysis. Landscape Research, 46(2), 211-225. https://doi.org/10.1080/01426397.2020.1737663

Gadsden, G.I.,  Golden, N., & Harris, N.C. (2023). Place-Based Bias in Environmental Scholarship Derived from Social–Ecological Landscapes of Fear. BioScience, 73(1), 23–35. https://doi.org/10.1093/biosci/biac095

Hoover, F., Meerow, S., Grabowski, Z.J., & McPhearson, T. (2021). Environmental justice implications of siting criteria in urban green infrastructure planning. Journal of Environmental Policy & Planning, 23(5), 665-682. https://doi.org/10.1080/1523908X.2021.1945916

Keith, R.J., Given, L.M., Martin, J.M., & Hochuli, D.F. (2022), Collaborating with qualitative researchers to co-design social-ecological studies. Austral Ecology, 47(4), 880-888. https://doi.org/10.1111/aec.13172

Lund, A.J., Harrington, E., Albrecht, T.R. et al. (2022). Tracing the inclusion of health as a component of the food-energy-water nexus in dam management in the Senegal River Basin. Environmental Science and Policy, 133, 74–86. https://doi.org/10.1016/j.envsci.2022.03.005

O'Neill, S.J., & Smith, N. (2014), Climate change and visual imagery. WIREs Climate Change, 5(1), 73-87. https://doi.org/10.1002/wcc.249

Stibbe, A. (2020). Ecolinguistics: Language, Ecology and the Stories We Live By (2nd edition). Routledge. https://doi.org/10.4324/9780367855512

Urbinatti, A.M., Benites-Lazaro, L.L., de Carvalho, C.M., & Giatti, L.L. (2020). The conceptual basis of water-energy-food nexus governance: systematic literature review using network and discourse analysis. Journal of Integrative Environmental Sciences, 17(2), 21-43. https://doi.org/10.1080/1943815X.2020.1749086

Vigl, L.E., Marsoner, T., Giombini, V. et al. (2021). Harnessing artificial intelligence technology and social media data to support Cultural Ecosystem Service assessments. People and Nature, 3(3), 673–685. https://doi.org/10.1002/pan3.10199