14.2 Discourse Analysis (DA)
2.1. Definition and Core Concepts
Discourse analysis (DA) is a broad approach to studying language as it's used in real-world social and cultural contexts. It focuses on how language helps us:
Construct meaning.
Establish identities.
Negotiate power dynamics.
DA looks beyond explicit communication to implicit meanings and unspoken assumptions. It includes nonverbal elements like body language, tone, and timing. Ultimately, DA aims to reveal how language both creates and reflects social reality.
2.2. Historical Development and Key Theoretical Perspectives
The term "discourse analysis" became widely used after Zellig Harris's work starting in 1952. The late 1960s and 1970s saw independent developments in DA across various fields, alongside areas like semiotics and sociolinguistics. This era brought significant contributions like Austin's speech act theory and Hymes' SPEAKING framework.
Several key theories underpin DA:
Structuralism: Meaning comes from relationships within a language system (Saussure).
Social Constructionism: Reality is shaped by discourse; language actively forms our perceptions.
Ethnomethodology: People collaboratively build shared reality through everyday conversation (Garfinkel).
Pragmatics: Context heavily influences meaning; focuses on implied meanings and intentions.
Post-structuralism & Deconstruction: Meanings in texts are unstable and multiple (Derrida).
Foucauldian Discourse Analysis: Explores how power and knowledge are intertwined in language, shaping what can be thought and said (Foucault).
Influential figures in DA include:
Zellig Harris: Popularized the term.
Michel Foucault: Analyzed power and discourse, introduced "discursive formations."
Norman Fairclough: Key in Critical Discourse Analysis (CDA), linking language to power and ideology.
Deborah Tannen: Studied gender differences in conversation.
James Paul Gee: Introduced "Discourses" (with a capital D) as integrated patterns of language, thinking, and acting tied to social identities.
2.3. Methods and Applications
Conducting DA involves:
Defining a research question.
Collecting data (e.g., interviews, social media, news).
Preparing data (e.g., detailed transcripts).
Analyzing data (e.g., word choice, grammar, power relationships, context).
Interpreting findings and connecting them to broader social theories.
DA can analyze diverse materials like books, news, marketing, and social media. Multimodal discourse analysis extends this to images, sound, and gestures. Researchers examine linguistic features like word choice, grammar, rhetorical devices, and non-verbal communication, connecting micro-level linguistic choices to macro-level social realities. For example, using "battle metaphors" for technology reflects and perpetuates negative perceptions.
DA is a diagnostic tool, revealing how societal values and power structures are subtly communicated through everyday language.
DA is applied in many professional fields:
Media & Communication Studies: Understanding how media shapes public opinion and constructs social issues.
Educational Research: Analyzing classroom interactions and academic writing to identify learning opportunities or barriers.
Organizational Communication: Revealing how workplace communication creates culture, power, and decision-making.
UX Research: Analyzing user language to understand frustrations and product perceptions.
Political Speeches & Marketing Messages: Understanding persuasion and influence.
Cross-cultural considerations are vital, as language meaning varies across cultures.
AI tools can assist DA with transcription, theme identification, and summarizing large datasets. However, human expertise remains crucial for nuanced interpretation.
The evolution of DA into a "flexible umbrella" reflects its maturation. It emerged independently across various disciplines due to a shared need to analyze language in its social context. This adaptability allows DA to be combined with other qualitative methods for deeper insights.