Building a Corpus

This is a practical guide to collecting, organizing, and managing a corpus of textual data for systematic analysis. It is not a method in itself — rather, it is a necessary first step for any method that requires you to work with a defined body of texts. If you are doing framing analysis, discourse analysis, or content analysis, you need a well-constructed corpus before you can begin your analytical work.

A poorly constructed corpus undermines everything built on top of it. If your selection criteria are unclear, your sampling biased, or your documentation incomplete, no amount of sophisticated analysis will produce credible results. This guide walks you through the process from planning to organization so that you finish with a corpus you can defend in your methodology chapter.

What Is a Corpus?

A corpus (plural: corpora) is a systematically assembled collection of texts selected according to explicit criteria for the purpose of analysis. The key word is systematic. A corpus is not simply “all the articles I found” or “everything on this topic.” It is a defined, bounded, documented collection whose composition you can explain and justify.

Corpora can include virtually any kind of textual material:

News articles from specific outlets over a defined period
Policy documents, government reports, or legislative texts
Speeches, press conferences, or official statements
Social media posts (tweets, Reddit threads, blog entries)
NGO reports, think-tank publications, or organizational communications
Legal texts, court rulings, or treaties
Interview transcripts or parliamentary proceedings

What makes these materials a corpus is not their format but the fact that you have collected them according to a clear rationale tied to your research question.

Corpus vs. sample: A corpus is the complete set of texts you have assembled for analysis. A sample is a subset drawn from a larger population. In some cases your corpus is a sample (e.g., 200 articles drawn from a universe of 3,000). In other cases your corpus aims to be comprehensive (e.g., all UN Security Council resolutions on a specific topic). Be clear about which approach you are using and why.

Planning Your Corpus

Before you collect a single text, you need to make four decisions. Write these down — they will form the backbone of your methodology section.

1. Define the scope

What types of texts will you include, and why? Your research question should drive this decision. Ask yourself:

What kind of textual evidence bears on my research question?
Which sources are most relevant to the actors, institutions, or debates I am studying?
What time period is appropriate? (A political crisis? A policy cycle? A decade of coverage?)
What geographic or linguistic boundaries apply?

2. Establish selection criteria

Selection criteria are the explicit rules that determine what goes into your corpus and what stays out. They must be specific enough that another researcher could replicate your collection. Good criteria typically specify:

Source(s): Which publications, platforms, archives, or databases?
Time frame: Exact start and end dates
Search terms: The keywords, Boolean operators, or filters you used
Inclusion rules: What counts? (e.g., “news articles only, not editorials or letters”)
Exclusion rules: What does not count? (e.g., “duplicate articles, articles under 200 words, wire service reprints”)

Write your criteria before you search. It is tempting to search first and define criteria afterward, but this introduces selection bias. Decide your rules, run your search, then apply the criteria consistently to filter results. Document any modifications you make along the way, and explain why.

3. Determine corpus size

There is no universal rule for how large a corpus should be. The right size depends on your method, your research question, and what is practically feasible within your thesis timeline. Some guidelines:

For close qualitative analysis (discourse analysis, detailed framing analysis): 30–80 texts is often sufficient. Quality of analysis matters more than volume.
For broader content analysis with coding schemes: 100–500 texts is common, depending on coding complexity.
For computational or mixed-methods approaches: Larger corpora are possible, but only if you have the tools and time to process them meaningfully.

The most common mistake is building a corpus that is too large to analyze carefully. A corpus of 2,000 articles sounds impressive, but if you cannot read and code them systematically within your thesis timeline, it is a liability, not an asset.

4. Plan your search strategy

Before you start collecting, write out your search strategy:

Which databases or sources will you search?
What search terms and Boolean operators will you use?
How will you handle synonyms, variant spellings, or translations?
Will you search full text, headlines only, or abstracts?
How will you de-duplicate results across databases?

Exercise

Before collecting any texts, draft a one-page corpus construction plan that includes: (1) the types of texts you will collect and why, (2) your selection criteria (sources, time frame, search terms, inclusion/exclusion rules), (3) your target corpus size and rationale, and (4) your search strategy. Bring this to a supervision meeting for feedback before you begin collecting.

Collection Methods

Databases and Archives

For most thesis projects in the social sciences and humanities, commercial databases and institutional archives are the primary collection tools. The three you are most likely to use through the Leiden University Library are:

LexisNexis Academic (Nexis Uni)

Best for: News media (newspapers, wire services, magazines, trade publications)
Coverage: Thousands of international sources; strong on English-language media, variable for non-English
Tips: Use the “Timeline” and “Source” filters to narrow results efficiently. Export metadata (headline, date, source, word count) along with full text. Be aware that LexisNexis sometimes includes duplicate articles from syndication — build de-duplication into your workflow. Limit searches by content type (e.g., “News” only) to exclude irrelevant material.

Factiva (Dow Jones)

Best for: International business and financial news, plus major general-interest media
Coverage: Strong international coverage; often better than LexisNexis for Asian and European sources
Tips: Factiva’s search operators differ from LexisNexis — consult the help documentation. Use “Source Lists” to define your publication set. Export to RTF or PDF for consistent formatting. Factiva is particularly useful for multilingual searches because it allows you to search in one language and filter by region.

ProQuest

Best for: Academic theses and dissertations, historical newspapers, specialized databases
Coverage: Includes historical archives (e.g., The New York Times back to 1851) and discipline-specific databases
Tips: Useful when your corpus needs historical depth. The “Document Type” filter is essential for excluding irrelevant results. ProQuest also hosts several specialized databases (e.g., PAIS International for policy literature, Ethnic NewsWatch for minority media).

Access: All three databases are available through the Leiden University Library. Log in with your ULCN credentials. If you encounter access issues, contact the library help desk — they can also arrange training sessions for database searching.

Other useful archives:

Government and institutional websites — for policy documents, legislation, speeches, press releases. Many governments maintain searchable archives (e.g., the EU’s EUR-Lex, the US Federal Register, or the Korean National Archives).
Organizational repositories — NGOs, think tanks, and international organizations often maintain document libraries (e.g., Human Rights Watch reports, World Bank publications, OECD policy briefs).
Digital newspaper archives — for historical research, sources like the British Newspaper Archive, Delpher (Dutch-language), or the Korean National Library’s digital archive may be relevant.

Web Sources and APIs

Some corpora require material from the open web: news websites, blogs, social media platforms, or organizational web pages. Collecting from these sources raises both practical and ethical considerations.

Web scraping involves programmatically extracting content from websites. Before scraping, check whether the source provides an API or structured data export — this is almost always preferable. If you do scrape, you must consider the site’s terms of service and the legal and ethical dimensions of automated data collection. For a thorough discussion of these issues, see Brown et al. (2025).

APIs (Application Programming Interfaces) provide structured access to platform data. Platforms that offer APIs include Reddit, YouTube, various government open data portals, and some news aggregators. APIs are generally more reliable and reproducible than scraping, but they come with rate limits and access restrictions that you need to plan around. For a discussion of using APIs for social media data collection, see Lomborg & Bechmann (2014).

Reproducibility matters. Whatever collection method you use, your goal is to make the process reproducible. Save your search queries, document every filter you apply, record the date you collected the data, and keep the raw files. If you use a script or tool, save it with your project files. See the Getting Started Guide — Step 2 for FAIR data management principles.

Multilingual Corpora

If your research question requires texts in more than one language — common in area studies and international relations — you need a clear strategy for managing multilingual material.

Key decisions:

Will you analyze texts in their original language or in translation? Analyzing originals preserves nuance but requires language competence; translation introduces interpretation at the data collection stage.
Who translates? If you translate, document your approach. If you use machine translation, acknowledge its limitations and describe quality checks.
Are search terms equivalent across languages? A direct translation of a keyword may not capture the same concept. Consult native-language scholarship to identify appropriate search terms.
How will you handle texts that mix languages? (e.g., Korean news articles that incorporate English loanwords or technical terms)

Practical tips:

Keep original-language texts as your primary data. Store translations separately and clearly label them.
In your metadata spreadsheet (see below), include a “Language” column.
If you use machine translation as a research aid, note which tool and version you used. This falls under your GenAI disclosure obligations — see the Ethics & AI page.

Organizing Your Corpus

A well-organized corpus saves enormous time during analysis. Set up your system before you start collecting.

File management

Use a consistent naming convention. A good pattern: YYYY-MM-DD_Source_ShortTitle (e.g., 2024-03-15_KoreaHerald_THAAD-Deployment). This sorts files chronologically by default.
Store files in a single dedicated folder with a clear directory structure. If your corpus is large, use subfolders by source or time period.
Keep originals untouched. Store raw downloaded files in one folder; work with copies in another. If you annotate or highlight texts, do it on the copies.
Back up everything. Use cloud storage (university OneDrive, Google Drive) and a local backup. A lost corpus means starting over.

Metadata spreadsheet

Create a spreadsheet to track every text in your corpus. At minimum, include:

Column	Example
ID	001
File name	2024-03-15_KoreaHerald_THAAD-Deployment.pdf
Title	“South Korea confirms THAAD deployment timeline”
Source	Korea Herald
Date	2024-03-15
Author	Kim, J.
Language	English
Word count	847
Collection date	2025-01-20
Database	LexisNexis
Search terms used	“THAAD” AND “South Korea” AND “deployment”
Notes	Wire service reprint — check for duplicates

This spreadsheet is not just for your own organization. It is a research instrument that documents your corpus construction process. You will draw on it directly when writing your methodology chapter.

Start the spreadsheet on day one. Adding metadata retroactively is tedious and error-prone. Log each text as you collect it. If you are exporting from a database, most allow you to export metadata fields directly — use this to pre-populate your spreadsheet.

Reference management

Add all corpus texts to your reference manager (Zotero, Mendeley, or equivalent). This serves two purposes: it helps you cite corpus texts in your thesis, and it provides a second inventory of your collection. In Zotero, you can create a dedicated collection for your corpus and tag items by source, theme, or coding status.

Using Computational Tools

For small corpora (under 100 texts), manual organization is manageable. For larger collections, or for corpora requiring format conversion and cleaning, computational tools can help significantly.

Basic file operations

Many corpus-building tasks are repetitive: converting PDFs to plain text, renaming files in batch, extracting text from HTML pages, removing headers and footers, or splitting large export files into individual documents. These are well-suited to scripting.

Python is the most common language for text processing in the social sciences. Libraries like BeautifulSoup (HTML parsing), pdfplumber or PyMuPDF (PDF extraction), and pandas (metadata management) handle most corpus-building tasks.
R users can accomplish similar tasks with packages like pdftools, rvest, and readtext.
Command-line tools such as pdftotext, pandoc, and standard Unix utilities (rename, sed, awk) are effective for batch operations.

AI-assisted corpus management

AI coding assistants — such as Claude Code, OpenAI Codex, or GitHub Copilot — can be useful for corpus management tasks, particularly if you are not an experienced programmer. These tools can help you:

Write scripts to batch-convert PDFs to plain text
Clean and standardize text files (removing headers, footers, boilerplate)
Parse and restructure metadata exports from databases
Rename files in bulk according to your naming convention
Split large export files into individual documents
Run basic validation checks (e.g., flagging empty files, checking word counts)

You do not need to be a programmer to use these tools effectively. Describe the task in plain language — “I have 300 PDFs in a folder and I need to convert them to plain text files with the same names” — and the tool will generate a script you can run.

GenAI disclosure: Using AI tools for corpus management tasks (file conversion, cleaning, batch operations) falls under your program’s generative AI policy. You must disclose this use in your methodology chapter. See the Ethics & AI — Generative AI Policy for the full policy and disclosure requirements.

Important distinction: Using AI to manage your corpus (converting, cleaning, organizing files) is different from using AI to analyze your corpus (generating codes, interpreting meaning, producing findings). The former is a practical workflow tool; the latter raises fundamental questions about the integrity of your analysis. Discuss any planned AI use with your supervisor.

What not to automate

Computational tools are for logistics, not for analytical judgment. The following should always be done by you:

Deciding what to include and exclude — selection criteria require human judgment about relevance
Reading and interpreting texts — the core of qualitative analysis
Coding and categorization — even if you use software like NVivo or Atlas.ti, the intellectual work of coding is yours
Evaluating quality and relevance — automated tools cannot assess whether a text is substantively relevant to your argument

Structuring Your Thesis

Your corpus construction is not just a preparatory step — it is a methodological choice that needs to be documented and justified in your thesis. Reviewers and examiners will evaluate whether your corpus is appropriate for your research question and whether your construction process was rigorous and transparent.

In your methodology chapter, you should cover the following:

Selection criteria

Explain what types of texts you collected and why. Justify your choice of sources, time frame, and any inclusion/exclusion rules. Connect these decisions explicitly to your research question.

Example: “The corpus consists of English-language news articles from the Korea Herald and Korea Times published between March 2016 and December 2017, covering the period from the initial announcement of THAAD deployment to the completion of installation. These sources were selected because they are the two major English-language daily newspapers in South Korea and provide sustained coverage of the issue accessible to an international audience.”

Search strategy

Document exactly how you found your texts. Report the databases searched, the search terms used (including Boolean operators), and any filters applied. If you conducted multiple searches or revised your terms, explain why.

Example: “Articles were retrieved from LexisNexis Academic using the search string (“THAAD” OR “Terminal High Altitude Area Defense”) AND (“South Korea” OR “ROK”), limited to the date range 1 March 2016 to 31 December 2017, filtered by content type ‘News.’ The initial search returned 1,247 results.”

Sampling and filtering

If you did not analyze every text your search returned, explain how you reduced the set. Describe any sampling procedure (random, stratified, purposive) and your filtering process (e.g., removing duplicates, excluding irrelevant results after reading).

Example: “After removing 312 duplicate articles and 89 articles that mentioned THAAD only in passing (fewer than two substantive paragraphs), the final corpus comprised 846 articles. From this set, a stratified random sample of 150 articles was drawn, with proportional representation by month, to ensure temporal coverage across the full deployment period.”

Corpus size and composition

Report the final size and composition of your corpus. Include a summary table or descriptive statistics: number of texts, breakdown by source, time distribution, word count range, languages represented.

Documentation and access

Describe how you organized and stored your data. Mention your naming convention, metadata spreadsheet, and backup procedures. If your corpus is drawn from publicly available sources, note whether and how other researchers could reconstruct it. This connects to the FAIR principles covered in the Getting Started Guide — Step 2.

Ask yourself

If another researcher read only your methodology chapter, could they reconstruct your corpus? If the answer is no, you need to add more detail about your selection criteria, search strategy, or filtering process.

Common Pitfalls

These are the problems that most frequently weaken corpus-based thesis work. Each one is avoidable with planning.

Undocumented selection criteria. You collected texts but cannot clearly explain why these texts and not others. This makes your corpus appear arbitrary and undermines the credibility of your analysis. Fix: write your criteria before you search, and record every decision.

Selection bias. Your corpus systematically over-represents certain perspectives, sources, or time periods because of how you searched. For example, searching only English-language sources for a study of Korean domestic debate, or collecting only articles that support your hypothesis. Fix: think critically about what your search strategy captures and what it misses. Acknowledge limitations honestly.

Not recording search terms and procedures. You ran searches three weeks ago and cannot remember exactly what terms or filters you used. Now you cannot describe your collection process accurately or re-run the search if needed. Fix: log every search query — date, database, search string, number of results — in a running document or your metadata spreadsheet.

Corpus too large to analyze carefully. You collected 1,500 articles because you could, but you do not have time to read and code them all properly. The result is either superficial analysis or a last-minute shift to a poorly justified subsample. Fix: estimate your analysis time before collecting. If close reading takes 15 minutes per text and you have 100 hours for analysis, your practical ceiling is around 400 texts — and that assumes no revisions.

Not backing up. Your corpus is stored in one location — your laptop, a single USB drive, or a folder you might accidentally delete. Fix: maintain at least two backups in different locations (e.g., university cloud storage + external drive). Do this from day one.

Inconsistent file management. Files are named haphazardly, stored in multiple locations, or lack metadata. When you need to find a specific text during writing, you waste time searching. Fix: establish your naming convention and metadata spreadsheet before you start collecting, and stick to it.

Treating corpus construction as a neutral activity. Every decision you make — which sources, which time frame, which search terms — shapes what your corpus can tell you. These are methodological choices, not administrative ones. Fix: discuss and justify these choices in your methodology chapter, and reflect on how different choices might have produced a different corpus.

Key Readings

These works provide methodological foundations for corpus construction and text-based research. Start with whichever is closest to your approach.

Krippendorff, K. (2018). Content Analysis: An Introduction to Its Methodology (4th ed.). Sage. — The standard reference for content analysis methodology, including detailed guidance on sampling, unitizing, and recording textual data. Chapter 6 on sampling is particularly relevant to corpus construction.
Stefanowitsch, A. (2020). Corpus Linguistics: A Guide to the Methodology. Language Science Press. DOI: 10.5281/zenodo.3735822. Free PDF. — A comprehensive, open-access guide to corpus linguistics methodology. Chapters 3–4 cover corpus design and data collection in detail.
McEnery, T., & Hardie, A. (2012). Corpus Linguistics: Method, Theory and Practice. Cambridge University Press. DOI: 10.1017/CBO9780511981395. — A thorough introduction to corpus-based research, including practical guidance on building and annotating corpora. Useful for students whose work bridges linguistics and social science.
Brown, M. A., Gruen, A., Maldoff, G., Messing, S., Sanderson, Z., & Zimmer, M. (2025). Web scraping for research. Big Data & Society, 12(4). DOI: 10.1177/20539517251381686. — A practical and ethical guide to web scraping for academic research, covering legal considerations, terms of service, and best practices.
Lomborg, S., & Bechmann, A. (2014). Using APIs for data collection on social media. The Information Society, 30(4), 256–265. DOI: 10.1080/01972243.2014.915276. — Discusses the methodological implications of using platform APIs for social media data collection, including issues of access, completeness, and representativeness.

Corpus construction is a preparatory step for several analytical methods. Once your corpus is built, you will apply one or more of the following:

Framing Analysis — Analyzing how issues are defined, problems diagnosed, and solutions proposed in textual material.
Discourse Analysis — Examining how language constructs meaning, identity, and power relations within and across texts.