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---
language:
- en
license: mit
task_categories:
- time-series-forecasting
tags:
- temporal-point-process
- event-sequences
- wikipedia
- edit-history
- collaborative-editing
- marked-temporal-point-process
size_categories:
- n<1K
---
# Wikipedia Edit Events
Curated Wikipedia article edit history sequences from [Featured](https://en.wikipedia.org/wiki/Wikipedia:Featured_articles) and [Good](https://en.wikipedia.org/wiki/Wikipedia:Good_articles) articles, designed for temporal point process (TPP) and marked temporal point process (MTPP) modeling. Each sequence tracks an article's editorial activity over time, where the prediction target is the **edit action type**.
## Dataset Description
- **Source:** [Wikipedia MediaWiki API](https://en.wikipedia.org/w/api.php)
- **Article source:** Wikipedia Featured + Good articles
- **Grouping:** Per-article, chunked into consecutive sequences of 60–80 events
- **Sequences:** 276
- **Sequence length:** 63–80 events per sequence (mean: 79.9)
- **Event types:** 6 edit action types
- **Time unit:** hours
- **Duration range:** 2.9–4,319 hours (mean: 1,061 hours ≈ 44 days, capped at 6 months)
## Schema
Each record is a dictionary with 8 fields:
| Field | Type | Description |
|---|---|---|
| `seq_idx` | int | Sequence index |
| `seq_len` | int | Number of events in the sequence |
| `description` | str | Natural language description of the article's edit timeline |
| `metadata` | str (JSON) | `article_title`, `article_pageid`, `chunk_index`, `num_editors`, `date_range_start`, `date_range_end`, `time_unit` |
| `time_since_start` | list[float] | Time since the first event (in hours) |
| `time_since_last_event` | list[float] | Time since the previous event (in hours) |
| `type_event` | list[str] | Edit action type label (see below) |
| `type_text` | list[str] | Natural language description of each edit |
## Event Types (6 edit action types)
Edits are classified by priority order from revision metadata (byte delta, tags, edit summary):
| Type | Description | Classification Rule |
|---|---|---|
| `revert` | Undoing a previous edit | Tags contain mw-rollback, mw-undo, or mw-manual-revert |
| `reference` | Adding or modifying citations | Edit summary matches ref/cite/source/bibliography patterns |
| `content_addition` | Substantial new content | Byte delta > +200 |
| `content_removal` | Substantial content removed | Byte delta < -200 |
| `copy_edit` | Small wording/formatting changes | \|delta\| <= 200, not flagged as minor |
| `minor_edit` | Trivial corrections | Minor flag set |
## Curation Filters
Sequences are aggressively filtered to ensure diverse, non-trivial edit type distributions:
| Filter | Value | Description |
|---|---|---|
| `max-articles` | 1,000 | Featured + Good articles processed |
| `min-seq-size` | 60 | Min events per sequence chunk |
| `max-seq-size` | 80 | Max events per sequence chunk |
| `min-types` | 4 | At least 4 distinct edit types per sequence |
| `max-duration-hours` | 4,380 | Drop sequences spanning more than 6 months |
| `max-dominant-ratio` | 0.40 | Drop sequences where any single type exceeds 40% |
| `max-repeat-ratio` | 0.40 | Drop sequences where consecutive repeat ratio exceeds 40% |
Additional processing:
- Bot edits excluded (tag "bot" or username ending in "bot")
- Byte deltas computed from consecutive revision sizes
- Sequences chunked chronologically per article
## Event Text
Each event's `type_text` is a coherent natural sentence providing rich context without explicitly stating the edit type classification. The article title is omitted (available at sequence level in `description` and `metadata`).
> Registered editor My love is love made a moderate addition (+620 bytes, <1% of article) to section "Titling and artwork"
**Sentence structure:** `{Editor identity} {size action} [to section "..."]. [Summary: "..."]. [Signals]. [Tool]. [Minor flag].`
Components (included only when present):
1. **Editor identity** (lead) — `"Registered editor Centrx"` or `"An anonymous editor"`
2. **Size action** (verb) — `"made a substantial addition (+1,253 bytes, 61% of article)"`
3. **Section target**`to section "Background and formation"` (extracted from edit comment)
4. **Edit summary**`Summary: "start background"` (cleaned of wiki markup)
5. **Semantic signals** — auto-detected patterns (e.g., "The summary mentions fixing language or typography")
6. **Tool/interface**`"Made using the visual editor"` (from revision tags)
7. **Minor flag**`"Flagged as minor"`
The model must learn to predict the edit action type from these contextual signals.
## Example
```json
{
"seq_idx": 2,
"seq_len": 80,
"description": "Wikipedia edit timeline for the article '4 (Beyoncé album)' spanning Dec 2011 to Jan 2012. This sequence tracks editorial activity including content changes, reverts, and maintenance edits.",
"metadata": "{\"article_title\": \"4 (Beyoncé album)\", \"article_pageid\": 31176601, \"chunk_index\": 24, \"num_editors\": 22, \"date_range_start\": \"2011-12-13T16:21:57+00:00\", \"date_range_end\": \"2012-01-07T21:12:43+00:00\", \"time_unit\": \"hours\"}",
"time_since_start": [0.0, 0.22, 2.56, ...],
"time_since_last_event": [0.0, 0.22, 2.35, ...],
"type_event": ["content_addition", "minor_edit", "copy_edit", ...],
"type_text": [
"Registered editor My love is love made a moderate addition (+620 bytes, <1% of article) to section \"Titling and artwork\"",
"Registered editor My love is love made a null edit (no size change) to section \"Music and themes\". Flagged as minor",
"Registered editor Dumb + dumb = 4 made a small removal (-142 bytes)",
...
]
}
```
## Intended Use
- Training and evaluating temporal point process models
- Studying collaborative editing patterns on Wikipedia
- Benchmarking next-event prediction and event forecasting
- Modeling marked temporal point processes with rich text marks
## Citation
If you use this dataset, please cite:
```bibtex
@dataset{wikipedia_edit_events_2025,
title={Wikipedia Edit Events},
author={XiaoBB},
year={2025},
url={https://huggingface.co/datasets/DescribeEvents/wikipedia_edit_events},
note={Curated from Wikipedia MediaWiki API, Featured and Good articles}
}
```