Datasets:

JoTalbot
/

ukrainian-dialogs-constructiveness

---
license: cc-by-4.0
language:
- uk
- ru
task_categories:
- text-classification
tags:
- conversation
- discourse
- constructiveness
- ukrainian
- ordinal-classification
- annotation
size_categories:
- n<1K
configs:
- config_name: gold
  data_files: gold_clean.csv
- config_name: main
  data_files: main_clean.csv
---
# Ukrainian Online Discourse: A Two-Axis Constructiveness Dataset

Expert-annotated Ukrainian-language dialogues from Telegram and the Ukrayinska
Pravda Forum, scored on six ordinal dimensions of dialogic constructiveness
organized into two orthogonal axes — **Relational Conduct (RC)** and
**Substantive Contribution (SC)**.

> **Note.** This dataset accompanies an exploratory study. Findings should be
> treated as preliminary, given the single-language setting and the modest
> size of the expert-annotated split. See the *Limitations* section below.

## Dataset summary

| Split | Rows | Annotators per item | Sources |
|-------|------|---------------------|---------|
| `gold` | 300 | 3 (calibrated experts) | Telegram (151), Ukrayinska Pravda Forum (149) |
| `main` | 1,071 | 2 per item, with a third on a verification subset (189 items) | Telegram (688), Ukrayinska Pravda Forum (383) |

Each item is a single message together with up to nine preceding messages from
the same direct-reply chain, providing pragmatic context. Every text field
appears in both its original Cyrillic form and a Latin-script transliteration
(KMU 2010 for Ukrainian, with a BGN/PCGN-style fallback for Russian-only
letters).

## Two-axis framework

Constructiveness is modeled as a multidimensional property along two
independent axes, each decomposed into three subdimensions:

**Relational Conduct (RC)** — interpersonal quality of interaction
- `RC1` *Respect / Tone* — politeness, hostility, warmth of language
- `RC2` *Engagement* — social awareness, interest in continuing the exchange
- `RC3` *Conflict Management* — escalation vs. de-escalation, willingness to seek understanding

**Substantive Contribution (SC)** — epistemic value of the message
- `SC1` *Reasoning Quality* — logical structure, fairness of argumentation, treatment of evidence
- `SC2` *Informativeness* — new, relevant, or specific content
- `SC3` *Dialog Continuity* — how well the message follows from prior turns and advances the discussion

Each subdimension is scored on a centered 5-point ordinal scale:

| Score | Label | Description |
|-------|-------|-------------|
| `+2` | Exemplary | perspective-taking, structured reasoning, proactive de-escalation |
| `+1` | Constructive | polite engagement and clear reasoning |
|  `0` | Neutral | factual and professional but shallow |
| `−1` | Poor | dismissiveness, passive-aggressiveness, topical drift |
| `−2` | Anti-constructive | insults, mockery, escalation |

## Schema

Every row contains **62 columns**, organized as follows:

### Identifier and source
- `row_id` (int) — stable per-split identifier
- `source` (str) — `telegram` or `ukrpravda`

### Conversational context (10 turns total)
For each `i` in `1..9` (where `prev_1` is the immediately preceding turn):
- `prev_{i}_user` (str) — pseudonymized speaker label
- `prev_{i}_text` (str) — original Cyrillic text
- `prev_{i}_text_translit` (str) — Latin-script transliteration

When fewer than nine context turns are available, earlier `prev_*` cells are empty.

### Target message
- `user_label` (str) — pseudonymized speaker label (or `Unknown`)
- `message_text` (str) — original Cyrillic text
- `message_text_translit` (str) — Latin-script transliteration

### Expert annotations
For each annotator `k` in `1..3` and each subdimension
`d` in {`RC1`, `RC2`, `RC3`, `SC1`, `SC2`, `SC3`}:
- `expert_{k}_{d}` (int, range −2..+2)

In `gold`, all three annotator columns are populated (int64).
In `main`, `expert_3_*` is populated only for the verification subset
(57 / 450 rows); other rows have `NaN` (so all expert columns are float64).

### Aggregated labels
For each subdimension `d`:
- `{d}_mean` (float) — mean across available annotators
- `{d}_median` (int / float) — median across available annotators

## Loading

```python
from datasets import load_dataset
gold = load_dataset("<org>/<repo>", "gold", split="train")
main = load_dataset("<org>/<repo>", "main", split="train")
```

If the `int` / `float` discrepancy between splits matters for your pipeline,
declare the expert columns as nullable integer features explicitly when
constructing `Features`.

## Construction

**Sources.** Two Ukrainian-language venues spanning different eras of online
discussion: the *ShrikeChat* Telegram channel (one of the most prominent
Ukrainian political discussion hubs) and the legacy Ukrayinska Pravda Forum
(20+ years of history). Only public messages were collected, via the official
Telegram API and public web scraping respectively.

**Context windows.** Unlike datasets that group comments into loose threads,
we preserve direct-response chains: each target message is paired with up to
nine preceding direct replies. This 10-message window is intended to capture
pragmatic signals (sarcasm, relational repair, retaliatory tone) that single-
comment analyses miss.

**Annotation pipeline.**
1. Each item was pre-labeled with Gemini 2.5 Flash.
2. Calibration rounds aligned annotators on the rubric.
3. Annotators independently reviewed and corrected labels in Argilla.
4. The `gold` split was triple-annotated by three calibrated experts;
   `main` was double-annotated, with a third pass on items requiring
   verification.

**Cleaning.** Forum-specific cruft was removed prior to release: edit footers
(`Останнє редагування: ...`), pasted UI elements
(`[ Відповісти в приват ] [Ігнорувати]`), `IP/Host:` disclosures, and
orphaned forum timestamps. The cleaning script is included alongside the
data.

**Transliteration.** Latin-script columns use the Ukrainian National
romanization (KMU 2010, Ukraine Cabinet of Ministers Resolution №55 of
27 January 2010), with positional rules (`Я` → `Ya` word-initially, `ia`
elsewhere) and the `зг → zgh` disambiguation. Russian-only Cyrillic letters
that occur in mixed-language items use a BGN/PCGN-style fallback.

## Inter-annotator agreement (gold, N=300)

| Dimension | Quadratic κ | Krippendorff α |
|---|---:|---:|
| RC1 — Respect / Tone | 0.650 | 0.647 |
| RC2 — Engagement | 0.471 | 0.445 |
| RC3 — Conflict Mgmt | 0.555 | 0.562 |
| SC1 — Reasoning | 0.611 | 0.604 |
| SC2 — Informativeness | 0.682 | 0.674 |
| SC3 — Dialog Continuity | 0.521 | 0.512 |
| **SC (collapsed sum)** | **0.761** | **0.762** |
| **RC (collapsed sum)** | **0.666** | **0.663** |

Collapsing the six rubric dimensions into the two meta-axes increases
agreement, which is consistent with — though not by itself conclusive of —
the existence of more stable two-dimensional latent structure. Aggregation
alone can mechanically increase agreement, and disentangling this from
genuine latent structure requires further study.

## Suggested uses

- **Ordinal classification** of dialogue turns along six interactional
  dimensions, or along the two collapsed RC/SC axes.
- **Conversational health analysis** with a centered scale that treats
  positive (`+1`, `+2`) and negative (`−1`, `−2`) constructiveness
  symmetrically around a neutral anchor.
- **Pragmatic context studies** using the 9-turn reply chain (e.g.
  retaliatory tone, sarcasm, repair attempts).

We recommend reporting **Quadratic Weighted Kappa (QWK)** when comparing
predictions against expert labels, since the scale is ordinal and naive
accuracy underweights large errors.

## Limitations

- **Single language and political context.** Items are Ukrainian-language
  political and civic discussion, much of it during a period of war. Norms
  of disagreement vary across languages, communities, and political
  conditions; we make no claim that the calibration of constructiveness
  observed here generalizes to other settings.
- **Small expert-annotated test set.** Triple expert annotation across six
  ordinal dimensions is labor-intensive, which limited the feasible scale
  within our resources. The `gold` set contains 300 items (90 reserved for
  test in the paper's evaluation protocol), which constrains both
  fine-grained learning and the statistical power of QWK comparisons.
- **Possible LLM anchoring bias.** Items were pre-labeled with Gemini 2.5
  Flash before human review. Calibration and independent expert annotation
  on the gold split mitigate this, but anchoring effects toward the model's
  initial judgments cannot be ruled out and may inflate observed agreement.
- **Reply-based structure assumption.** Constructiveness is evaluated within
  an explicit reply chain. In less structured discussions, broader
  interaction patterns may matter that pairwise adjacency does not capture.
- **Moderate IAA on relational dimensions.** Tone and intent interpretation
  remains subjective; some learned decision boundaries may be influenced by
  annotation uncertainty.

## Ethics and privacy

The dataset was constructed from publicly available Telegram channels (via
the official Telegram API) and public discussion threads on the Ukrainska
Pravda Forum. No private groups, direct messages, or restricted-access
content were accessed. Identifiers were pseudonymized at collection time,
and messages whose content could plausibly enable re-identification were
removed before analysis. Annotators (student researchers fulfilling
structured research duties under educational grants) worked exclusively
with anonymized data and were briefed on confidentiality requirements.

The texts include political opinions, emotionally charged exchanges, and
hostile language, including slurs and ad hominem attacks; the labels (e.g.
`−2` on RC1) flag such content. Users of the dataset should handle it
accordingly.

## Citation

```bibtex
@misc{korotenko2026dataset,
  title        = {Ukrainian Online Discourse: A Two-Axis Constructiveness Dataset},
  author       = {Korotenko, Artem and Kyslyi, Roman},
  year         = {2026},
  publisher    = {Hugging Face},
  howpublished = {\url{https://huggingface.co/datasets/KSE-RESEARCH-Group/ukrainiain-dialogs-constructiveness}}
}
```

## License

Released under **CC BY 4.0**. Attribution to the dataset and accompanying
paper is required for redistribution and downstream use.

---

*Mirror created 2026-10-11 by the JoTalbot/ukraine pipeline. Original source: https://huggingface.co/datasets/KSE-RESEARCH-Group/ukrainian-dialogs-constructiveness — please credit and respect the original dataset's license.*

	---
	license: cc-by-4.0
	language:
	- uk
	- ru
	task_categories:
	- text-classification
	tags:
	- conversation
	- discourse
	- constructiveness
	- ukrainian
	- ordinal-classification
	- annotation
	size_categories:
	- n<1K
	configs:
	- config_name: gold
	data_files: gold_clean.csv
	- config_name: main
	data_files: main_clean.csv
	---

	# Ukrainian Online Discourse: A Two-Axis Constructiveness Dataset

	Expert-annotated Ukrainian-language dialogues from Telegram and the Ukrayinska
	Pravda Forum, scored on six ordinal dimensions of dialogic constructiveness
	organized into two orthogonal axes — Relational Conduct (RC) and
	Substantive Contribution (SC).

	> Note. This dataset accompanies an exploratory study. Findings should be
	> treated as preliminary, given the single-language setting and the modest
	> size of the expert-annotated split. See the Limitations section below.

	## Dataset summary

	\| Split \| Rows \| Annotators per item \| Sources \|
	\|-------\|------\|---------------------\|---------\|
	\| `gold` \| 300 \| 3 (calibrated experts) \| Telegram (151), Ukrayinska Pravda Forum (149) \|
	\| `main` \| 1,071 \| 2 per item, with a third on a verification subset (189 items) \| Telegram (688), Ukrayinska Pravda Forum (383) \|

	Each item is a single message together with up to nine preceding messages from
	the same direct-reply chain, providing pragmatic context. Every text field
	appears in both its original Cyrillic form and a Latin-script transliteration
	(KMU 2010 for Ukrainian, with a BGN/PCGN-style fallback for Russian-only
	letters).

	## Two-axis framework

	Constructiveness is modeled as a multidimensional property along two
	independent axes, each decomposed into three subdimensions:

	Relational Conduct (RC) — interpersonal quality of interaction
	- `RC1` Respect / Tone — politeness, hostility, warmth of language
	- `RC2` Engagement — social awareness, interest in continuing the exchange
	- `RC3` Conflict Management — escalation vs. de-escalation, willingness to seek understanding

	Substantive Contribution (SC) — epistemic value of the message
	- `SC1` Reasoning Quality — logical structure, fairness of argumentation, treatment of evidence
	- `SC2` Informativeness — new, relevant, or specific content
	- `SC3` Dialog Continuity — how well the message follows from prior turns and advances the discussion

	Each subdimension is scored on a centered 5-point ordinal scale:

	\| Score \| Label \| Description \|
	\|-------\|-------\|-------------\|
	\| `+2` \| Exemplary \| perspective-taking, structured reasoning, proactive de-escalation \|
	\| `+1` \| Constructive \| polite engagement and clear reasoning \|
	\| `0` \| Neutral \| factual and professional but shallow \|
	\| `−1` \| Poor \| dismissiveness, passive-aggressiveness, topical drift \|
	\| `−2` \| Anti-constructive \| insults, mockery, escalation \|

	## Schema

	Every row contains 62 columns, organized as follows:

	### Identifier and source
	- `row_id` (int) — stable per-split identifier
	- `source` (str) — `telegram` or `ukrpravda`

	### Conversational context (10 turns total)
	For each `i` in `1..9` (where `prev_1` is the immediately preceding turn):
	- `prev_{i}_user` (str) — pseudonymized speaker label
	- `prev_{i}_text` (str) — original Cyrillic text
	- `prev_{i}_text_translit` (str) — Latin-script transliteration

	When fewer than nine context turns are available, earlier `prev_*` cells are empty.

	### Target message
	- `user_label` (str) — pseudonymized speaker label (or `Unknown`)
	- `message_text` (str) — original Cyrillic text
	- `message_text_translit` (str) — Latin-script transliteration

	### Expert annotations
	For each annotator `k` in `1..3` and each subdimension
	`d` in {`RC1`, `RC2`, `RC3`, `SC1`, `SC2`, `SC3`}:
	- `expert_{k}_{d}` (int, range −2..+2)

	In `gold`, all three annotator columns are populated (int64).
	In `main`, `expert_3_*` is populated only for the verification subset
	(57 / 450 rows); other rows have `NaN` (so all expert columns are float64).

	### Aggregated labels
	For each subdimension `d`:
	- `{d}_mean` (float) — mean across available annotators
	- `{d}_median` (int / float) — median across available annotators

	## Loading

	```python
	from datasets import load_dataset

	gold = load_dataset("<org>/<repo>", "gold", split="train")
	main = load_dataset("<org>/<repo>", "main", split="train")
	```

	If the `int` / `float` discrepancy between splits matters for your pipeline,
	declare the expert columns as nullable integer features explicitly when
	constructing `Features`.

	## Construction

	Sources. Two Ukrainian-language venues spanning different eras of online
	discussion: the ShrikeChat Telegram channel (one of the most prominent
	Ukrainian political discussion hubs) and the legacy Ukrayinska Pravda Forum
	(20+ years of history). Only public messages were collected, via the official
	Telegram API and public web scraping respectively.

	Context windows. Unlike datasets that group comments into loose threads,
	we preserve direct-response chains: each target message is paired with up to
	nine preceding direct replies. This 10-message window is intended to capture
	pragmatic signals (sarcasm, relational repair, retaliatory tone) that single-
	comment analyses miss.

	Annotation pipeline.
	1. Each item was pre-labeled with Gemini 2.5 Flash.
	2. Calibration rounds aligned annotators on the rubric.
	3. Annotators independently reviewed and corrected labels in Argilla.
	4. The `gold` split was triple-annotated by three calibrated experts;
	`main` was double-annotated, with a third pass on items requiring
	verification.

	Cleaning. Forum-specific cruft was removed prior to release: edit footers
	(`Останнє редагування: ...`), pasted UI elements
	(`[ Відповісти в приват ] [Ігнорувати]`), `IP/Host:` disclosures, and
	orphaned forum timestamps. The cleaning script is included alongside the
	data.

	Transliteration. Latin-script columns use the Ukrainian National
	romanization (KMU 2010, Ukraine Cabinet of Ministers Resolution №55 of
	27 January 2010), with positional rules (`Я` → `Ya` word-initially, `ia`
	elsewhere) and the `зг → zgh` disambiguation. Russian-only Cyrillic letters
	that occur in mixed-language items use a BGN/PCGN-style fallback.

	## Inter-annotator agreement (gold, N=300)

	\| Dimension \| Quadratic κ \| Krippendorff α \|
	\|---\|---:\|---:\|
	\| RC1 — Respect / Tone \| 0.650 \| 0.647 \|
	\| RC2 — Engagement \| 0.471 \| 0.445 \|
	\| RC3 — Conflict Mgmt \| 0.555 \| 0.562 \|
	\| SC1 — Reasoning \| 0.611 \| 0.604 \|
	\| SC2 — Informativeness \| 0.682 \| 0.674 \|
	\| SC3 — Dialog Continuity \| 0.521 \| 0.512 \|
	\| SC (collapsed sum) \| 0.761 \| 0.762 \|
	\| RC (collapsed sum) \| 0.666 \| 0.663 \|

	Collapsing the six rubric dimensions into the two meta-axes increases
	agreement, which is consistent with — though not by itself conclusive of —
	the existence of more stable two-dimensional latent structure. Aggregation
	alone can mechanically increase agreement, and disentangling this from
	genuine latent structure requires further study.

	## Suggested uses

	- Ordinal classification of dialogue turns along six interactional
	dimensions, or along the two collapsed RC/SC axes.
	- Conversational health analysis with a centered scale that treats
	positive (`+1`, `+2`) and negative (`−1`, `−2`) constructiveness
	symmetrically around a neutral anchor.
	- Pragmatic context studies using the 9-turn reply chain (e.g.
	retaliatory tone, sarcasm, repair attempts).

	We recommend reporting Quadratic Weighted Kappa (QWK) when comparing
	predictions against expert labels, since the scale is ordinal and naive
	accuracy underweights large errors.

	## Limitations

	- Single language and political context. Items are Ukrainian-language
	political and civic discussion, much of it during a period of war. Norms
	of disagreement vary across languages, communities, and political
	conditions; we make no claim that the calibration of constructiveness
	observed here generalizes to other settings.
	- Small expert-annotated test set. Triple expert annotation across six
	ordinal dimensions is labor-intensive, which limited the feasible scale
	within our resources. The `gold` set contains 300 items (90 reserved for
	test in the paper's evaluation protocol), which constrains both
	fine-grained learning and the statistical power of QWK comparisons.
	- Possible LLM anchoring bias. Items were pre-labeled with Gemini 2.5
	Flash before human review. Calibration and independent expert annotation
	on the gold split mitigate this, but anchoring effects toward the model's
	initial judgments cannot be ruled out and may inflate observed agreement.
	- Reply-based structure assumption. Constructiveness is evaluated within
	an explicit reply chain. In less structured discussions, broader
	interaction patterns may matter that pairwise adjacency does not capture.
	- Moderate IAA on relational dimensions. Tone and intent interpretation
	remains subjective; some learned decision boundaries may be influenced by
	annotation uncertainty.

	## Ethics and privacy

	The dataset was constructed from publicly available Telegram channels (via
	the official Telegram API) and public discussion threads on the Ukrainska
	Pravda Forum. No private groups, direct messages, or restricted-access
	content were accessed. Identifiers were pseudonymized at collection time,
	and messages whose content could plausibly enable re-identification were
	removed before analysis. Annotators (student researchers fulfilling
	structured research duties under educational grants) worked exclusively
	with anonymized data and were briefed on confidentiality requirements.

	The texts include political opinions, emotionally charged exchanges, and
	hostile language, including slurs and ad hominem attacks; the labels (e.g.
	`−2` on RC1) flag such content. Users of the dataset should handle it
	accordingly.

	## Citation

	```bibtex
	@misc{korotenko2026dataset,
	title = {Ukrainian Online Discourse: A Two-Axis Constructiveness Dataset},
	author = {Korotenko, Artem and Kyslyi, Roman},
	year = {2026},
	publisher = {Hugging Face},
	howpublished = {\url{https://huggingface.co/datasets/KSE-RESEARCH-Group/ukrainiain-dialogs-constructiveness}}
	}
	```

	## License

	Released under CC BY 4.0. Attribution to the dataset and accompanying
	paper is required for redistribution and downstream use.

	---

	Mirror created 2026-10-11 by the JoTalbot/ukraine pipeline. Original source: https://huggingface.co/datasets/KSE-RESEARCH-Group/ukrainian-dialogs-constructiveness — please credit and respect the original dataset's license.