README.md

---
pretty_name: Tweets Sample 2026 — Newspaper-Vocabulary Reference Corpus
# license: TODO — see "Licensing and terms of use" below before publishing
language:
- en
- de
multilinguality:
- multilingual
language_creators:
- found
annotations_creators:
- no-annotation
source_datasets:
- original
size_categories:
- 10M<n<100M
task_categories:
- text-classification
- text-retrieval
tags:
- twitter
- x
- social-media
- nitter
- german
- narrative-detection
- reference-corpus
- computational-social-science
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*.parquet
---

# Tweets Sample 2026 — Newspaper-Vocabulary Reference Corpus

A large, deliberately **untargeted** sample of public posts from X/Twitter, collected via
[Nitter](https://github.com/zedeus/nitter) by sweeping a 65,689-term newspaper vocabulary
rather than a topical keyword set.

It is built as a **background / reference corpus**: a baseline of "what was being said in
general" against which a topically targeted collection can be contrasted. It is the reference
arm of a narrative-detection study, not a curated dataset about any particular subject.

> [!IMPORTANT]
> Two columns do not contain what their names say. **`quotes` holds like counts and `likes`
> holds view counts.** See [Engagement columns are mislabeled](#engagement-columns-are-mislabeled)
> before using any engagement metric.

## At a glance

| | |
|---|---|
| Rows (tweets) | **75,845,834** |
| Unique tweet IDs | 75,845,834 (fully deduplicated — 1 row per tweet) |
| Unique authors | 9,215,132 (≈ 8.2 tweets per author) |
| Date range | 2006-03-24 → 2026-07-27 (**99.81 % falls in 2026-01-01 → 2026-07-27**) |
| Search terms swept | 65,689 |
| Distinct `search_term` values present | 59,383 |
| Size | 25.7 GB, 73 Parquet shards (snappy) |
| Language mix (heuristic) | ~52 % English, ~22 % German, remainder short/other |
| Splits | `train` only |

## Loading

The corpus is 25.7 GB; stream it unless you have the RAM to spare.

```python
from datasets import load_dataset

# streaming — no full download, constant memory
ds = load_dataset("SinclairSchneider/tweets_sample_2026", split="train", streaming=True)
print(next(iter(ds)))

# or materialize the whole thing (needs ~25 GB disk + a lot of RAM)
ds = load_dataset("SinclairSchneider/tweets_sample_2026", split="train")
```

For analytical work, querying the Parquet files directly is far more efficient than going
through `datasets`, because you can push column selection down to the file:

```python
import duckdb

duckdb.sql("""
  SELECT search_term, count(*) AS n
  FROM 'hf://datasets/SinclairSchneider/tweets_sample_2026/data/*.parquet'
  GROUP BY 1 ORDER BY n DESC LIMIT 20
""").show()
```

### The search vocabulary

`search_terms.txt` (shipped with this dataset) contains **65,689** lowercase, alphabetically
sorted, single-token terms spanning `aachen` → `übungsplatz`. It is a general newspaper
vocabulary — overwhelmingly ordinary words, not a political keyword list:

```
aachen, aal, aalglatt, aasgeier, abandon, abandoned, abarbeiten, abartig, abattoirs, abba, …
überziehend, überzieht, überzogen, üblich, übrig, übriggebliebener, übung, übungsplatz
```

Despite the file name (`political_reference_newspapers.txt`), the list is **not** topically
political; the "political" label refers to the study it was assembled for. It mixes German and
English vocabulary, which is why the corpus skews English (see
[Language composition](#language-composition)).

Hits are very unevenly distributed across terms:

| Percentile of terms | 5th | 25th | Median | 75th | 95th |
|---|---|---|---|---|---|
| Tweets returned | 13 | 101 | 382 | 1,357 | 5,570 |

The highest-yield terms are English and mostly generic: `leftie` (63,980), `statistic` (58,708),
`bootlicker` (48,482), `screenings` (47,040), `stadion` (46,015), `oversold` (44,747),
`preferable` (42,918).

## Data fields

| Field | Type | Notes |
|---|---|---|
| `search_term` | string | The query that surfaced this tweet. **Lossy — read the caveats below.** |
| `id` | string | Tweet ID (numeric, stored as string). Unique across the corpus. |
| `link` | string | Permalink, e.g. `https://twitter.com/{user}/status/{id}#m` |
| `text` | string | Tweet body as rendered by Nitter |
| `name` | string | Author display name |
| `username` | string | Author handle, `@`-prefixed |
| `profile_id` | string | Author profile identifier |
| `avatar` | string | Author avatar URL |
| `date` | string | `YYYY-MM-DD HH:MM:SS+00:00` (UTC). **String, not a timestamp** — lexically sortable. |
| `is-retweet` | bool | 3,605,695 rows (4.75 %) |
| `is-pinned` | bool | **Always `false`** — never populated |
| `external-link` | string | Non-empty for only 215,350 rows (0.28 %); empty string otherwise |
| `replying-to` | list\<string\> | Handles replied to. Non-empty for 43,997,721 rows (**58.01 %**) |
| `quoted-post` | struct | Nested quoted tweet (`date`, `link`, `text`, `user{…}`, `pictures`, `videos`, `gifs`). The struct is always present, but its fields are null unless a quote exists — 6,992,385 rows (9.22 %) have one. **Test `quoted-post.link IS NOT NULL`, not `quoted-post IS NOT NULL`.** |
| `comments` | int64 | Reply count |
| `retweets` | int64 | Retweet count |
| `quotes` | int64 | ⚠️ **Contains like counts** |
| `likes` | int64 | ⚠️ **Contains view counts** |
| `pictures` | list\<string\> | Image URLs. Non-empty for 13,002,600 rows (17.14 %) |
| `videos` | list\<null\> | **Always empty** — video extraction never produced output; typed `list<null>` |
| `gifs` | list\<string\> | Non-empty for 913,666 rows (1.20 %) |

## Known issues and caveats

### Engagement columns are mislabeled

The scraper reads the four `tweet-stat` elements of a Nitter tweet card **by position** and
assigns them the names `comments`, `retweets`, `quotes`, `likes`. On the instances actually
used, positions 3 and 4 are *likes* and *views* — there is no quote count in the markup — so
the last two columns are shifted by one:

| Column name | Actually contains | Median | 99th pct | Max | % zero |
|---|---|---|---|---|---|
| `comments` | replies ✅ | 0 | 46 | 751,521 | 61.10 % |
| `retweets` | retweets ✅ | 0 | 205 | 1,339,671 | 75.09 % |
| `quotes` | ⚠️ **likes** | 2 | 1,176 | 4,228,611 | 31.14 % |
| `likes` | ⚠️ **views** | 89 | 51,433 | 1,539,096,983 | 0.71 % |

The ratios make the shift unambiguous: the `quotes` column runs at **4.6× the retweet count**,
which is the canonical likes-to-retweets ratio (real quote counts are a *fraction* of retweets).
The `likes` column runs at **99.7× retweets** and **28.4× the `quotes` column**, with a maximum
of 1.54 billion — impossible for likes, entirely normal for views. Only 0.71 % of rows are zero,
consistent with X displaying a view count on nearly every post.

Rename on load:

```python
df = df.rename(columns={"comments": "replies", "quotes": "likes", "likes": "views"})
```

**A true quote count does not exist in this dataset.**

### `search_term` is lossy in two ways

1. **It is one arbitrary matching term, not all of them.** A tweet containing several vocabulary
   words was returned by several queries, but deduplication kept only the first row encountered
   in filesystem-glob order. `search_term` is therefore *a* term that matched, chosen
   arbitrarily — never treat it as the complete set of matching terms, and do not use it as a
   label or as a frequency estimate for that term in the corpus.
2. **Long terms are truncated to their last 20 characters.** The value is recovered from a
   filename built with `term[-20:]`, so 455 of the 65,689 terms (0.69 %) appear clipped —
   `entscheidungskompetenz` → `tscheidungskompetenz`, `nachfolgeorganisation` →
   `achfolgeorganisation`. Truncation can in principle also merge two distinct long terms that
   share a suffix.

### Dates fall outside the requested window

Although the sweep requested `since=2026-01-01`, **144,215 rows (0.19 %) predate 2026**,
reaching back to 2006-03-24 — the search backend does not honour the date bound strictly.
Filter on `date` yourself if you need a clean window.

| Period | Tweets |
|---|---|
| Before 2025 | 77,052 |
| 2025 | 67,163 |
| **2026-01** | 6,521,317 |
| **2026-02** | 6,565,188 |
| **2026-03** | 8,166,263 |
| **2026-04** | 9,419,069 |
| **2026-05** | 16,926,040 |
| **2026-06** | 16,553,381 |
| **2026-07** (to the 27th) | 11,550,361 |

The 2026 month-over-month growth is a **collection artefact**, not a signal about platform
activity — scraping ran forward in time and later months had more instance capacity available.
Do not read the monthly curve as a volume trend.

### Language composition

No language filter was applied, and the vocabulary itself is mixed, so the corpus is
**majority English despite being assembled from a German newspaper vocabulary**. A stopword
heuristic over a 6,639,818-tweet sample:

| | Share |
|---|---|
| English markers only | 52.3 % |
| German markers only | 22.2 % |
| Both | 3.8 % |
| Neither (short texts, other languages) | 21.7 % |

This is a coarse heuristic, not a trained language ID. Run a proper detector
(fastText `lid.176`, GlotLID) if language is load-bearing for your work.

### Sampling bias

This is **not** a random sample of X, and no weighting will make it one:

* Coverage is bounded by the vocabulary. A tweet containing none of the 65,689 terms cannot
  appear — which biases against very short posts, emoji-only posts, other languages, and
  heavy slang.
* Nitter search returns *what the search backend chooses to return*, with its own opaque
  ranking and recall limits. Unlimited `--max_tweets` does not mean exhaustive retrieval.
* Instance availability varied over the collection period, so coverage is uneven across time
  and across terms — 6,306 of the 65,689 vocabulary terms (9.6 %) yielded nothing at all, and
  it is not determinable after the fact whether a given term genuinely had no matches or was
  simply never served successfully.
* Engagement-related selection is unknown: high-visibility tweets are plausibly over-represented.

### Other

* Content reflects the moment of scraping. Tweets later deleted, edited, or made private are
  still here; counts are frozen at scrape time and no longer match the platform.
* Retweets (4.75 %) duplicate the text of the original post — filter `is-retweet` for text work.
* `date` is a string. Cast it before doing time arithmetic.

## Intended use

Suited to: building background/reference distributions for narrative and framing detection,
term-frequency baselines, corpus-linguistic study of platform language, retrieval and
topic-model development, and pretraining or domain adaptation of social-media models.

Not suited to: measuring the prevalence of anything on X (the sampling frame forbids it),
tracking trends over time (the monthly curve is an artefact), studying individuals or
accounts, or any engagement analysis that has not first corrected the mislabeled columns.