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This applied course introduces students to the field of corpus-assisted discourse analysis (CADS), a framework that uses data-driven tools to identify linguistic patterns that underlie rhetorical strategies, discursive representations, and ideologies in large collections of text such as political speeches and news media. Students will assemble their own corpus of texts based on research interest, use automated corpus linguistics tools to find patterns in the data, and analyze those patterns through the lens of (critical) discourse analysis and classical rhetoric. The goal is to examine how the corpus constructs political stances, ideologies, and social power in ways that persuade audiences, drive political narratives, and shape opinion.
The course is open to students in their second year or higher in the PPE program, and is well-suited for students aspiring to be speechwriters, campaign strategists, or media professionals. Poslední úprava: Straková Laura Juliet, M.A. (01.10.2025)
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To examine the construction and representation of political stances, ideologies, and social power through language, using the approach of corpus-assisted discourse analysis (CADS). Students will: - Learn the concepts of CADS and classical rhetorical appeals - Create a corpus of speeches, media coverage, or other texts for research purposes - Use corpus software tools to identify keywords and other patterns of language used to construct discursive representations - Apply CADS to draw inferences on agency, power, and ideology from the linguistic data - Identify classical rhetorical appeals and analyze their deployment in context - Consider practical applications of CADS (speechwriting, campaigns) Poslední úprava: Straková Laura Juliet, M.A. (01.10.2025)
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Credit for the course will be awarded on the basis of the following five components. All of the components are compulsory, and all tasks must be fulfilled before the examination period starts. This is a project-based course. There is no mid-term exam, and satisfactory completion of items 3, 4 and 5 serves in lieu of an end-of-semester exam. "Re-sits" are therefore not possible. Assessment is based on points: A = 91 – 100 points B = 81 – 90 points C = 71 – 80 points D = 61 – 70 points F = 0 – 60 points 1. Preparedness and participation (8 points). Students are to come prepared and to participate actively in the individual, pair, group or other work of the day to receive the full 8 points. As this is an applied course, attendance is strongly encouraged. 2. Three technical assignments (12 points). A student can earn a maximum of 4 points each. If the assignment is not uploaded by deadline, no points are awarded for it. #1 Discourse analysis of a short text. #2 Rhetorical appeals analysis of a short text. #3 KWIC, collocation, keyword analysis with AntConc (or LancsBox X – TBA). 3. CADS project proposal (15 points). Each student will write a brief project proposal, using a provided template. A student can earn a maximum of 15 points, using an assessment rubric provided beforehand. 4. CADS project draft (25 points). The draft is a work-in-progress and will consist of a description of the corpus data compiled for the project; a description of the corpus tools employed; preliminary data output and findings (e.g. tables, charts, wordclouds); description of considered theoretical frameworks and preliminary analysis. A template will be provided to help students produce the draft. A student can earn a maximum of 25 points, using an assessment rubric provided beforehand. 5. CADS final project (40 points). A polished version of approximately 3000 words (ca. 10 pages, 1.5 spaced). The same structure is applied as to the draft version, but with further development of findings and analysis. A template will be provided. A student can earn a maximum of 40 points, using a provided assessment rubric. Please note that the final project must be submitted by the end of the teaching semester (before Christmas break). Poslední úprava: Straková Laura Juliet, M.A. (07.10.2025)
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All material and a full list of sources will be available on Google Drive Polititalk: Rhetorical Analysis of Political Discourse, including links to online resources. (Access will be given after the first class.) Books. Material may be drawn from the following: Baker, P. (2023) Using Corpora in Discourse Analysis. Bloomsbury (2nd ed.) Baker, P., Vessey, R. and McEnery, T. (2021) The Language of Violent Jihad. Cambridge UP Charteris-Black, J. (2018) Analysing Political Speeches: Rhetoric, Discourse and Metaphor. MacMillan Charteris-Black, J. (2011) Politicians and Rhetoric: The Persuasive Power of Metaphor. Palgrave MacMillan (2nd ed.) Friginal, E. and Hardy, J. (eds.) (2021) The Routledge Handbook of Corpus Approaches to Discourse Analysis. Routledge. Mercieca, J. (2020) Demagogue for President: The Rhetorical Genius of Donald Trump. Texas A&M University O’Keeffe, A. and McCarthy, M. (eds.) (2022) The Routledge Handbook of Corpus Linguistics. Routledge (2nd ed.) Partington, A. and Taylor, C. (2018) The Language of Persuasion in Politics. Routledge (2nd ed.) Partington, A., Duguid, A. and Taylor, C. (2013) Patterns and Meanings in Discourse: Theory and Practice in Corpus-Assisted Discourse Studies (CADS). John Benjamins Zottola, A. (2021) Transgender Identities in the Press: A Corpus-Based Discourse Analysis. Bloomsbury. Journal articles will also be provided as material for discussion, project inspiration, and analytical models. A sampling: Amaireh, H. (2022) “Corpus-based analysis of the feminine style of Kamala Harris’ discourse: women (not men) are the backbone of America’s democracy and economy” Theory and Practice in Language Studies (12/9): 1762-1769 Baker, P. and Levon, E. (2015) “Picking the right cherries? A comparison of corpus-based and qualitative analyses of news articles about masculinity” Discourse & Communication 9(2): 221-236 Liu, M., Zhao, R. and Ngai, C. (2022) “Vaccines, media and politics: a corpus-assisted discourse study of press representations of the safety and efficacy of COVID-19 vaccines” PLoS ONE 17(12) Ngula, R. (2021) “’If you ride a lame horse into a race…’: a corpus-based analysis of metaphors in John Mahama’s political speeches” Language, Discourse & Society 9(2) Vessey, R. (2017) “Corpus approaches to language ideology” Applied Linguistics 38(3): 277-96 Willis, R. (2017) “Taming the climate? Corpus analysis of politicians’ speech on climate change” Environmental Politics 26(2): 212-31 The materials are for class use by registered students. Any circulation of the materials is prohibited. Poslední úprava: Straková Laura Juliet, M.A. (12.09.2025)
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In-class practice with the corpus software and its output forms the basis for most class sessions. Students will install the relevant free software to their laptops after the first class, and they should bring their laptops to subsequent class meetings. Weekly topics will be explored in a mix of individual work, group discussions, pair and small-group workshopping – always with an eye towards working with the data and analytical tools, and sharing interpretations of their findings. Homework tasks are devoted to preparatory reading, individual project design, corpus-building, and data analysis. The instructor will frequently act as a facilitator, with students expected to actively participate in the classes. AI Use Policy You may use AI to: Conduct research and summarize existing literature – but always check original sources (see Errors and Bias below) Check grammar and refine wording, style, and structure – but it should not rephrase your work or introduce vocabulary that you would not otherwise use You must not use AI to: Write entire assignments or drafts (rough or final) for you Present any AI work as your own without citation Copy and paste whole text Pretend that you wrote something you asked AI to write and then be unable to discuss the product you present as your own AI tools should support, not replace, your learning. Think critically about all of your assignments but particularly if you choose to use AI. Why is it important not to directly copy words from an AI engine into our texts? Plagiarism. AI uses previously published sources without citation. Therefore using their outputs without acknowledgement puts you at risk of plagiarism. AI tools are also typically trained on datasets that may be outdated and can include copyrighted material. Therefore, relying on an AI tool may result in copyright violations. Errors. AI engines are unreliable on facts—anything they assert must be checked against reliable sources. AI tools aim to simulate human-like content creation rather than ensuring accuracy or reliability. Therefore, it remains your responsibility, not the tool's, to ensure the quality, integrity, and accuracy of any work submitted for this course. Bias. AI engines reproduce biases and prejudices from their source material—it is incumbent on us to check and correct for bias. AI output may reflect bias because the data they are trained on may reflect bias or may not include sufficient data from certain groups. Crutching. Using AI to generate text may rob us of the chance to develop our own thinking on a subject. Think about it this way: The point in education is not to generate text artefacts. Rather, the point is to help us develop our own ability to think critically. Writing is a means to critical thinking, and we must do our own writing to cultivate our own true, not artificial, intelligence. Therefore, be sure that you consider ethical AI usage, data privacy and security, addressing potential biases in AI algorithms, and appropriately balancing technology with human interaction, as you will be responsible for any inaccurate, biased, or unethical content you submit, regardless of its origin (you or AI). Documentation of AI Use For any interaction a student has with AI, they must provide an AI Disclosure at the end of the work as a final paragraph, stating: Be prepared to defend your work orally if requested, including all arguments and sources, without the aid of AI. Lecturer Use of AI The Lecturer may use AI tools for the preparation of teaching materials, in line with the Statement of Charles University and the Recommendations for Educators. Any use of artificial intelligence tools will be carried out in a way that protects students' personal data. Student work will not be used to train AI models, and personal evaluation by the Lecturer will always complement AI-assisted assessment to ensure fairness and academic integrity. FSV UK Policy All uses of AI tools must be explicitly stated according to the guidelines set by FSV UK, and they must adhere to the broader ethical recommendations provided by Charles University. Students should carefully evaluate the information provided by AI tools and ensure that their final work reflects their own contribution and analysis. Violation of these rules may result in the essay not being accepted or in disciplinary proceedings under Charles University’s regulations. Poslední úprava: Straková Laura Juliet, M.A. (19.09.2025)
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Weeks 1-2 Foundational concepts in rhetoric, discourse, and corpora Weeks 3-4 Qualitative analysis: discursive representations and rhetorical appeals in political texts Weeks 5-7 Corpus bootcamp: KWIC, keywords, collocations and concordances Weeks 8-9 Integrating rhetoric, discourse analysis, and CADS: Projects-in-progress workshops Weeks 10-12 Project design & analysis workshops This outline is tentative and subject to change. A detailed syllabus will be available on GoogleDrive Polititalk: Rhetorical Analysis of Political Discourse after the first class; relevant study materials will also be posted here (only for registered users). Poslední úprava: Straková Laura Juliet, M.A. (12.09.2025)
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The course is open to students in their second year or higher in the PPE program. Poslední úprava: Straková Laura Juliet, M.A. (12.09.2025)
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