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The right sequence is to define the question and the population your evidence should represent, then choose a sampling and collection method that fits. The method determines what you can responsibly claim later.
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What is the difference between corpus linguistics and web scraping?
Corpus linguistics studies language through a deliberately designed collection of texts or transcribed speech. The collection is called a corpus. Its value comes from the relationship between the research question, the target population, the sampling plan, the metadata, and the analyses that follow. A corpus may contain millions of words, but size alone does not make it representative or suitable for a particular claim.
Web scraping is an automated way to request, parse, and save material from websites. A scraper can follow links, extract page elements, record timestamps, and repeat the process on a schedule. It solves a retrieval and processing problem. It does not, by itself, decide which sites belong in the study, whether the pages represent a target population, whether duplicate or syndicated material should count once or many times, or whether the collected text may be redistributed.
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“A corpus is not simply a collection of texts.” — Douglas Biber, Susan Conrad, and Randi Reppen, chapter Issues in Corpus Design, Cambridge University Press.
That distinction matters because the two terms answer different questions. Corpus design asks, “What language population or domain should this evidence represent, and how will I sample it?” Scraping asks, “How can I retrieve selected online material reliably and at a useful scale?” A corpus project can use hand collection, licensed databases, APIs, web scraping, or a combination. A scrape can remain an exploratory dataset without ever becoming a corpus intended for generalization.
Choose according to the claim you need to make
| Decision axis | Corpus-led project | Scraping-led collection |
|---|---|---|
| Main purpose | Analyze a defined variety, genre, population, or language use with planned evidence. | Retrieve online material at a scale or cadence that manual collection would not support. |
| Main design question | What target population should the corpus represent, and how will it be sampled? | Which pages can and should be retrieved, and how will the collection be bounded? |
| Primary strength | Transparent domain definition, sampling, and interpretation can support appropriately scoped generalization. | Automation makes repeated or large-scale retrieval practical. |
| Primary limitation | A corpus can still be narrow or biased if its target domain and sampling are poorly specified. | A collection can be extensive yet skewed by search ranking, site structure, access restrictions, or extraction errors. |
| What to document | Purpose, target population, source types, sampling, metadata, processing, exclusions, and limits. | Targets, retrieval dates, crawler identity, request settings, extraction and cleaning rules, failures, and omissions. |
| Responsible-use checks | Rights, consent and privacy, participant expectations, institutional requirements, and distribution conditions. | Site terms, published access signals, applicable law, ethical review, and traffic impact. |
The columns are not mutually exclusive. For example, you might define a corpus of national newspaper editorials for a particular year, select a stratified sample of publications, and use a scraper to retrieve the selected articles. Conversely, you might scrape public pages to discover recurring vocabulary, then design a smaller corpus whose sources and sampling support a clearly stated research claim.
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Define the target population before counting words
Start by stating what your results are supposed to describe: legal judgments from a named jurisdiction, customer-support chats from a particular product, undergraduate essays written in a specified period, or online news editorials from selected outlets. A target such as “English on the web” is too broad unless you can define which varieties, genres, regions, authors, dates, and publication settings it includes.
Biber, Conrad, and Reppen explain that a corpus seeks to represent a language or a part of one, and that its design depends on what it is intended to represent. A news-heavy dataset may be useful for studying news discourse, but it cannot support an unrestricted generalization about English variation. State the intended population in the methods plan and repeat the boundary when reporting results.
Choose text types, sources, and sampling rules
Biber’s discussion of representativeness identifies several design decisions that precede sample size: which text types to include, how many texts to select, which texts within each type, how much to sample from each text, and how long each sample should be. Representativeness means that the sample covers the relevant range of variability in the population; it is not simply a consequence of collecting more tokens.
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A practical sampling specification can include:
- the source universe, such as all qualifying journals, sites, or institutions;
- inclusion and exclusion rules, including language, date, genre, author status, and minimum length;
- strata that matter to the question, such as region, publication type, speaker role, or year;
- the number of documents or words selected from each stratum;
- the rule for selecting documents, such as random, systematic, quota, or purposive sampling;
- the treatment of repeated, syndicated, updated, translated, or incomplete texts; and
- metadata needed to interpret comparisons, including date, source, genre, author or speaker information where ethically appropriate, and collection method.
Do not use a universal minimum corpus size
There is no single word-count threshold that makes every corpus reliable. A narrowly defined terminology study may need fewer documents than a study comparing regional variation across many genres, while a conversation corpus may require detailed participant and turn metadata rather than a very large token count.
One 2024 law-vocabulary case study reported a specialist corpus of 1.98 million words. That study also reported a 1,026-flemma single-word list, 26.37% coverage in the study corpus, 23.87% coverage in comparison texts, and a split-half rank correlation of rs = .98 (the abstract prints p < .00). Those figures describe that particular law corpus and validation exercise. They are not recommended thresholds for all corpus projects and are not performance benchmarks for scraping.
Plan the analysis around the design
Before collecting, decide what comparisons and measurements the corpus can support. Frequency comparisons need comparable sampling and normalization. Register or genre comparisons need enough material in each category and consistent coding. Longitudinal claims need dates and a sampling frame that remains interpretable over time. If a category is represented by one unusually prolific source, the result may reflect that source rather than the category.
When web scraping is the better collection mechanism
Use scraping for scale, repetition, or structured retrieval
Scraping is useful when pages are publicly retrievable, the target sites and page types are known, and manual saving would be slow or inconsistent. Typical reasons include collecting a bounded set of articles, monitoring changes at scheduled intervals, assembling examples from a defined set of domains, or retrieving page-level metadata alongside text.
Scraping is not a neutral way to sample the internet. Search results privilege ranking systems and discoverability; a site’s navigation may expose some content while hiding other content; paywalls, deleted pages, regional delivery, robots.txt responses, rate limits, and JavaScript rendering can all shape what is retrieved. Describe the result as a collection from the named sites and rules, not as “the web” or “English generally.”
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A bounded scraping workflow
- Write the collection specification. Define the domains, URL patterns, page types, language, date range, maximum depth, and stopping rule. State whether the unit is a page, article, post, paragraph, or another segment.
- Check permission and access signals. Read each site’s current terms and robots.txt file, identify any API or licensing route, and decide whether the project requires institutional or legal review. A page being fetchable is not proof that copying or redistribution is allowed.
- Select targets with an explicit frame. Build the URL list from a documented sitemap, archive, publication index, or other stated source. If discovery begins with a search engine, record that limitation instead of treating the results as a neutral sample.
- Identify the crawler. Send a clear user-agent where appropriate, keep request rates conservative, and retain logs of requests, responses, redirects, and failures. Do not attempt to evade access controls.
- Retrieve and preserve provenance. Save the URL, retrieval timestamp, response status, content type, source domain, and any stable identifier. Keep the original response or an integrity record when policy and rights permit.
- Extract text consistently. Apply the same selectors or parsing rules to all pages in a class. Separate article text from navigation, advertisements, comments, cookie notices, related links, and boilerplate, and record which elements were removed.
- Clean without erasing meaning. Normalize encoding and whitespace, but retain a raw-to-clean mapping or versioned transformation so that punctuation, headings, quotations, and markup decisions can be audited.
- Detect duplicates and near-duplicates. Syndicated stories, printer versions, mobile versions, pagination, and later edits can create multiple records. Define whether duplicates are collapsed, retained as editions, or analyzed separately.
- Inspect failures and missingness. Sample successful and failed pages, review extraction output manually, and report blocked, empty, malformed, dynamically rendered, or out-of-scope pages. Missing pages may be systematic rather than random.
- Freeze a release and describe its limits. Record the collection period, software and configuration, exclusions, cleaning version, and known gaps. If the collection will be repeated, specify what changes are expected between releases.
Control request rates without treating throttling as permission
Scrapy’s AutoThrottle extension, documented for version 2.19.0, adjusts download delays using standard concurrency and delay settings. It is one practical mechanism for moderating traffic. It does not replace checking site terms, robots.txt, applicable law, or the effect of requests on a site. Current software behavior may differ from that documentation, so consult the documentation for the version you actually deploy.
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Can scraping be used to build a corpus?
Yes, if scraping is subordinate to a corpus design. Define the population and sampling plan first, then configure the scraper to collect the selected material and metadata. The resulting corpus should make clear which pages were eligible, how URLs were selected, when they were retrieved, what was excluded, and how the final sample differs from the intended population.
A defensible combined project often has two phases:
Phase 1: exploratory collection
Use a broad but bounded scrape to discover terminology, genres, source variation, or likely sampling strata. Treat the output as exploratory. Inspect its biases and do not present frequencies from this phase as estimates of a wider population unless the design supports that interpretation.
Phase 2: designed corpus construction
Turn the exploratory findings into explicit inclusion rules and a sampling frame. Select sources and documents according to those rules, retrieve them consistently, and attach metadata. The scraper now implements a documented sample rather than defining the population by whatever pages happened to be easiest to fetch.
Where the methods complement each other
- Corpus design supplies the inferential boundary; scraping supplies repeatable retrieval.
- Scraping can expose source imbalance or genre gaps that require a revised sampling plan.
- A corpus release can preserve metadata and provenance even when raw text cannot be redistributed.
- Repeated scraping can create comparable time slices only when the target sites, page types, and rules remain stable enough to interpret change.
Representativeness, bias, and what your data can support
Representativeness is always relative to a target. A sample can be representative of one narrowly specified domain and unsuitable for another. A collection of national court opinions may support claims about those opinions under the stated selection rules, but not about everyday legal advice, spoken language, or all writing in the country.
Common sources of bias include:
- Coverage bias: eligible sources that have no public archive, block automated requests, require login, or use inaccessible formats never enter the dataset.
- Discovery bias: search ranking, link popularity, and site navigation favor some topics, publishers, and authors.
- Survivorship bias: deleted, corrected, archived, or short-lived pages are missing from a later collection.
- Extraction bias: templates, comments, captions, tables, or dynamic content are removed inconsistently.
- Duplication bias: syndicated text or multiple page versions overweights a source or event.
- Time bias: a collection made on one date may not reflect pages that were added, edited, or unavailable at another date.
- Language and register bias: a scraper may collect standard written prose while missing speech, messages, regional varieties, or informal interaction.
Large token counts can make statistical estimates more precise for the collected material while leaving these selection problems unchanged. Explain the population, the sampling frame, and the missing portion before reporting any generalization.
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Copyright, privacy, and research ethics
Public visibility is not blanket permission
The Lancaster corpus-linguistics textbook discussion notes that downloading and saving large quantities of online text is technically straightforward, but collection does not itself confer a right to reproduce or distribute those texts. Options may include seeking permission, using licensed material, limiting access to authorized researchers, or designing outputs that do not redistribute protected text. The correct treatment depends on the jurisdiction, the source terms, the use, and how the dataset will be shared; this is not a substitute for local legal advice.
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Brookes and McEnery identify consent, anonymity, public versus private communications, and fair representation as ethical issues in corpus research. A forum post may be publicly viewable while still carrying an expectation of contextual privacy. Sensitive topics can increase the risk of re-identification or harm even when names are not collected.
Build an ethics review into the design:
- collect only fields needed for the stated question;
- remove or transform direct identifiers and evaluate whether quotations remain searchable;
- protect raw files with access controls and retention limits;
- consider whether participants could reasonably expect research reuse;
- obtain institutional review where required; and
- justify any redistribution of raw text separately from the decision to analyze it.
Understand what robots.txt does and does not do
RFC 9309, the Internet Engineering Task Force’s Robots Exclusion Protocol standard published in September 2022, describes how site owners publish crawler preferences and how crawlers should interpret responses. Its section 1 states: “These rules are not a form of access authorization.” Robots.txt is therefore not a security boundary, a license, or a complete statement of legal permission. Check the current file and the site’s terms, honor applicable disallow rules, identify your crawler, and avoid inferring permission merely because a path can be fetched.
Documentation that makes a dataset credible and repeatable
A useful dataset record lets another researcher understand both what is present and what is absent. Keep a project record containing:
- the research question and target population;
- source lists, URL patterns, licenses, and access decisions;
- sampling rules, quotas, randomization or selection procedures, and stopping criteria;
- retrieval dates, time zone, crawler identity, concurrency, delays, retries, and software versions;
- raw and processed file identifiers, checksums or other integrity records where appropriate;
- parser, tokenizer, encoding, deduplication, and normalization rules;
- counts of attempted, successful, excluded, duplicate, empty, and failed items;
- metadata definitions and missing-value conventions;
- ethical safeguards, anonymization choices, review decisions, and retention periods; and
- the conditions under which the corpus may be accessed, cited, or redistributed.
Version the collection and cleaning logic. A changed parser can alter frequencies even when the source pages are unchanged; a changed site can alter the input even when your code is identical. Record both kinds of change.
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The scrape is large but the results do not generalize
Return to the target population and compare it with the actual source frame. Report the collection as a site-bounded sample, narrow the claim, or redesign the sample with missing source types and strata in mind. Do not solve a selection problem by downloading more pages from the same biased frame.
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Pages contain navigation instead of language data
Inspect extraction output from several templates and page types. Create separate parsing rules where layouts differ, preserve raw examples for auditing, and count pages that fail validation. A text-length threshold alone can retain menus or cookie notices while discarding short but valid documents.
Duplicate stories dominate frequencies
Use canonical identifiers, normalized text fingerprints, publication metadata, and a documented rule for syndicated or updated versions. If editions are substantively different, retain the relationship rather than silently collapsing them.
Requests are blocked or overload a site
Stop and reassess the collection path. Look for an authorized feed, API, archive, or permission arrangement; reduce concurrency and request frequency; honor robots.txt and terms; and document inaccessible material. Do not bypass authentication, rate limits, or technical controls.
Raw text cannot be shared
Separate reproducibility of the method from redistribution of copyrighted content. Share code, schemas, aggregate counts, derived features, metadata that can legally be disclosed, and instructions for authorized access where appropriate. Explain which files are withheld and why, and obtain permission if full text is essential.
A practical decision checklist
Use a corpus-led design first when most of these statements are true:
- You need to describe or compare a defined language variety, genre, population, or period.
- Your conclusions depend on sampling and representativeness rather than retrieval volume.
- You can name the source universe and the variability that matters.
- You need metadata and exclusions to interpret frequencies or patterns.
Use a scraping-led collection first when most of these statements are true:
- The material is online and a bounded set of sites or page types is known.
- Manual retrieval would be too slow, inconsistent, or difficult to repeat.
- You need scheduled snapshots, large-scale retrieval, or structured provenance.
- You can accept that access restrictions and site structure define part of the observed data.
Use both when you need designed evidence and automated acquisition: specify the corpus, implement the sample with a scraper, and preserve enough provenance to explain every inclusion, exclusion, and transformation.
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Further reading for corpus design
Readers moving from method selection to construction may find Sandrine Zufferey’s Introduction to Corpus Linguistics (Wiley / ISTE, 2020), especially the chapter How to Build a Corpus, useful for corpus reuse, sample size, representativeness, sampling, coding, and legal and ethical questions. Verify the edition and availability before purchase.
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