How Prevow analysed 391,459 Reddit records about marriage

This note describes how Prevow produced Indian marriage on Reddit: what 391,459 records show. Seven phases, each with its outputs on disk and a fixed random seed. Every failure the analysis found in itself is listed here rather than in a footnote. The aggregate counts are in the data release.

What was collected

• Source: a public archive of Reddit, not Reddit's own API and not scraped from the live site.
• Records: 391,459, being 32,971 posts and 358,488 comments.
• Publication window: 2024-09-02 to 2026-09-02, measured from the records themselves.
• Subreddits: 63 contributed of 72 listed.

The exclusions

Two exclusions written into the collection brief did not work as written. The satire flag and the promotional flag each carry one value across every record, so neither excluded anything, and both were re-specified during the integrity audit. Two further exclusions were found by reading:

• 401 of 358,488 comments (0.112%) name one commercial matrimonial brand, across 78 parent threads and 3 subreddits inside a 22-day window. All carry the highest source rating and none carries the promotional flag. They are advertisements written to read as peer advice.
• 2,089 of 358,488 comments (0.583%) are moderator or bot boilerplate carrying ordinary topic labels.

Together that is 2,490 comments that are not participant speech. The cleaned denominator used from phase five onward is 388,969 records, 99.364% of the corpus as delivered.

The searches that did not complete

The search log records 1,948 searches, of which 1,922 completed and 26 are marked incomplete. They cluster on money, law and three large general-India communities, so the corpus's financial and legal content comes almost entirely from marriage forums. 32 of the 72 listed subreddits contributed nothing, and a further 13 contributed posts and no comments.

The limitation nobody documented

Excerpts are capped near 914 characters, and 19,025 of 32,971 post excerpts (57.7%) end mid-sentence. This was found by measuring, not stated in the corpus manifest, and it binds every reading: an absence from an excerpt is not an absence from the post.

The labelling taxonomy

Every record carries a primary topic label and up to eight secondary ones, from a fixed list applied before this analysis began. One label behaves as a default rather than a discriminator, so both positions are reported everywhere. The escape hatch, other explicit topic, was used on no record at all. The smallest labels, in any position:

• Other explicit topic: 0 records.
• Family introduction: 25 records.
• Premarital counselling: 82 records.
• Engagement cancelled: 450 records.
• Premarital questions: 451 records.

The full census is in the data release.

The seven phases

1. Integrity audit. Line counts, parse failures, duplicate identifiers, referential integrity, field population, date coverage, and a scan for identifiers that survived redaction.
2. Deterministic counts. One streaming pass per file. Every percentage divides by a stated denominator, and cells below thirty are marked uninterpretable.
3. Sampling. Fourteen subject frames, each with a main sample, a hold-out sample and a counter sample, drawn with a fixed seed.
4. Close reading. Every drawn record read and coded, every pattern recorded with the identifiers supporting it, and every identifier checked against the drawn frame.
5. Disconfirmation. 101 patterns tested against the hold-out, the counter sample, subreddit and month concentration, and ten searches written to find the exception.
6. Comparison cuts. Six axes attempted. Two produced a usable comparison, one produced an artefact of labelling, and three could not be made.
7. Gaps, then the claim ledger. What the corpus cannot answer, then 111 claims, each with its identifiers, its verdict and what would be needed to publish it.

The sampling rule, and where it broke

Each frame draws two hundred records at random, with a fixed seed, capped so no single subreddit supplies more than a quarter of the sample. The cap could not always be met, and each breach is declared rather than smoothed. The counselling frame is the clearest case: its whole matched set is 82 records, so nothing was sampled at all. Sampling never used the score field or any measure of how a record was received.

The disconfirmation protocol

Every pattern was attacked before it was published. One script applies the verdict rule, in one order, first condition winning, so no verdict is a judgement call.

• Survives. Comparable rate in the hold-out, a balanced counter sample, and no single community supplying more than half the supporting records.
• Survives, narrowed. The pattern holds, thinner than first written. Published only in the narrowed wording, and withheld where no narrowed wording was written.
• One community. More than half the supporting records come from one subreddit. Published only with the community and its share named inside the sentence.
• Fails. Fewer than five supporting records, or fewer than three subreddits. Untraceable at this standard, not disproved.
• Not scorable. Fewer than twenty comparable records in the hold-out, so the test could not run.

The searches written to find the exception

Ten searches were written before their results were seen. Four verdicts were revised as a result, every one a demotion or a rewrite.

• A search for a family objection being talked round returned 100 records across 16 subreddits, which narrowed the veto claim.
• A search for another attribute disclosed as precisely as income returned 486 records giving height or weight as a labelled field, which falsified the income claim and forced a rewrite.
• A search for a price attached to counselling returned 26 records, none carrying the counselling label.
• Two searches found nothing: the intimacy conversation held before the wedding, and counselling changing a decision. Both are reported as nulls.

The search that was wrong, and is published anyway

The first search for the career claim returned 110 records, because it caught the subject being discussed rather than the thing happening. The replacement returned 4 records, of which 1 survived reading. The first income query returned 2,574 against the 486 the corrected query found. If you see 110 or 2,574 quoted as a result of this research, it is a misreading of a discarded query.

The publishability standard

A claim is publishable as stated if it is a corpus count carrying its denominator inside the sentence, or a pattern that survives, worded as an observation about records. 56 of 111 logged claims meet that. The report publishes 63, because a claim that survived only in narrower form is printed in its narrowed wording, and a claim that holds in one community is printed with that community and its share named. Why the other 48 are not on it:

• Survived only in a narrower form, and no narrowed wording was written for it: 19.
• Written in the ledger as a label or a cross-reference, not a sentence that stands on its own: 13.
• Failed the traceability minimum: fewer than five supporting records, or fewer than three subreddits: 10.
• Could not be scored, because the hold-out sample it needed did not exist: 4.
• The ledger entry contradicts itself, so it is held back until corrected: 1.
• Labelled as one community's, but that community supplies only a third of the records, so the label does not hold; held back until corrected: 1.

The failure that matters most

One failed pattern cuts against the premise of the whole analysis: that an arrangement agreed before a wedding is reversed after it. It failed on breadth rather than plausibility, and it is carried as an open question, not as a settled negative.

Every known skew

• r/Arrangedmarriage supplies 41.964% of the 388,969 cleaned records, and two subreddits supply 47.51% of the posts.
• The highest source tier holds 72.35% of posts and cannot be used as a quality filter: the planted advertisements and the moderator notices carry it too.
• 408 records are in non-Latin scripts with no translation, and far more are substantially not in English than the manifest discloses, because transliterated Hindi is written in Latin characters.
• Reddit is English-skewed, urban-skewed, young-skewed and male-skewed relative to India. Gender is stated on four posts in ten.
• Age, diaspora status, marriage type and matchmaking route are too sparsely or unreliably populated to cut on. One record carries an age of 90 while describing someone of 23.

Privacy

Usernames were never collected and no author identifier exists in the corpus. Prevow checked whether the source's redaction pass worked, not just whether it ran, and found that 18 of 391,459 records still carry an identifier. All of them are permanently barred from publication, and no excerpt from any of them is published anywhere. Raw records are not published and will not be. No quotation is published either: 60 were selected and 0 cleared.

Limitations, in full

Topic frequency in this corpus measures what generates enough distress, confusion or grievance to write about publicly. It does not measure what is common in Indian marriages, what is normal, or what is happening.

• No claim here is about people. Every figure is a count of records in this corpus, and none is a share of any population.
• No trend claim is available. The window is two years, and its final two days hold 47 posts.
• Absence of evidence is not evidence of absence. Two searches returned nothing, and both are reported as nulls.
• An absence from the English half of a record is not an absence from the record. Accounts are often written in English with the pressure quoted in Hindi.
• An absence from a sample is not an absence from the corpus. Two statements of absence written from samples were falsified by searching the whole corpus, and both were withdrawn.

Checking this

Every figure in the report is compiled from the claim ledger and the count tables rather than typed. The aggregate counts, a data package describing every field, and a checksum for every file are in the data release, under CC BY 4.0. If a figure does not match the file it came from, that is a defect, and Prevow would like to hear about it: contact.