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README.md

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+---
+language:
+- en
+license: cc-by-4.0
+task_categories:
+- text-classification
+task_ids:
+- topic-classification
+- sentiment-classification
+tags:
+- linguistics
+- ukraine
+- toponyms
+- language-policy
+- kyivnotkiev
+size_categories:
+- 10K<n<100K
+---
+
+# KyivNotKiev Computational Linguistics Corpus
+
+A balanced, labeled corpus of texts containing Ukrainian and Russian toponym variants
+(e.g., "Kyiv" vs "Kiev"), annotated with context categories and sentiment.
+
+## Dataset Description
+
+- **Curated by:** Ivan Dobrovolskyi
+- **Language:** Primarily English
+- **License:** CC-BY 4.0
+- **Paper:** #KyivNotKiev: A Large-Scale Computational Study of Ukrainian Toponym Adoption (forthcoming)
+- **Website:** https://kyivnotkiev.org
+
+## Dataset Summary
+
+29,938 texts across 55 Ukrainian-Russian toponym pairs from 4 sources
+(Reddit, YouTube, GDELT news articles). Each text is labeled with:
+- **Context category**: politics, war_conflict, sports, culture_arts, food_cuisine, travel_tourism, academic_science, history, business_economy, general_news
+- **Sentiment**: positive, neutral, negative
+- **Variant**: which toponym form (russian/ukrainian) appears in the text
+
+## Intended Uses
+
+- Studying language policy adoption in media and social platforms
+- Training toponym context classifiers
+- Analyzing sentiment differences between spelling variants
+- Cross-source and temporal analysis of naming conventions
+
+## Dataset Structure
+
+### Data Fields
+- `pair_id`: Integer ID of the toponym pair
+- `text`: The full text content
+- `variant`: "russian" or "ukrainian" — which spelling form appears
+- `source`: Data source (reddit, youtube, gdelt)
+- `year`: Publication year
+- `context_label`: Annotated context category
+- `context_confidence`: Annotation confidence (0-1)
+- `sentiment_label`: Sentiment annotation
+- `sentiment_score`: Sentiment score (-1 to 1)
+- `word_count`: Number of words in text
+- `matched_term`: The specific toponym form found in text
+
+### Splits
+| Split | Count |
+|-------|-------|
+| train | 23,950 |
+| validation | 2,993 |
+| test | 2,993 |
+
+## Balance Report
+
+See `balance_report.json` for detailed per-pair, per-source, per-variant distributions
+and documented shortfalls.
+
+## Collection Methodology
+
+1. **Reddit**: Titles and bodies from Arctic Shift API + Reddit search (2010-2026)
+2. **YouTube**: Video titles and descriptions via yt-dlp (2010-2026)
+3. **GDELT**: News article bodies fetched from URLs using trafilatura (2010-2026)
+4. **Balancing**: Stratified sampling by pair × source × variant × year stratum
+5. **Annotation**: Llama 3.1 70B-Instruct with human validation on 200 random samples
+6. **Fetch transparency**: All GDELT URL fetch attempts logged in `fetch_log.parquet`
+
+## Citation
+
+```bibtex
+@article{dobrovolskyi2026kyivnotkiev,
+  title={#KyivNotKiev: A Large-Scale Computational Study of Ukrainian Toponym Adoption},
+  author={Dobrovolskyi, Ivan},
+  year={2026}
+}
+```

fetch_log.parquet

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+size 808079

test.parquet

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train.parquet

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validation.parquet

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