| # Annotation guidelines — CodeMixCheck v2.2.1 |
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| This document records the labelling rules that can be **evidenced from the data itself**: the label vocabularies, the templated `evidence_explanation` strings, and the review notes in `change_v2_2_1`. |
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| ## 1. Fact verification (4-way) |
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| ### General rule |
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| **Rule 1: judge the claim as written** (quoted in the v2.2.1 review notes). The label applies to the literal statement in `text`. Do not guess what the poster probably meant, and do not apply a plausible real-world reading. For example, a text that states a true premise and correctly rejects an exaggeration is `True`, not `Partially True`. |
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| ### Labels |
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| | label | criterion | canonical explanation pattern (most frequent in data) | |
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| | `True` | The claim matches the official source, notice, or established general/reference knowledge. | "Confirmed — official source/notice matches this claim." | |
| | `False` | The official statement, records, or established knowledge directly contradicts the claim. | "Idhu false — official statement/records directly contradict this claim." | |
| | `Partially True` | The core fact holds, but a sweeping, absolute or extreme part is exaggerated or incomplete (for example, "100%", "ella districts", "permanently"). | "Partially true — core fact holds but the sweeping part is exaggerated or incomplete." | |
| | `Unverifiable` | There is no named or checkable source, only hearsay or anecdote, so the claim can be neither confirmed nor refuted. | "Idhu unverifiable — no named official source, hearsay only." | |
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| `label_3way` maps True → SUPPORTED, False → REFUTED and Unverifiable → NOT_ENOUGH_INFO. `Partially True` has **no** 3-way mapping and must not be forced into one. |
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| ### Framing is not the label |
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| Framing (`frame_family`: forwarded message, official attribution, personal hearsay, general report) is spread across all four labels in the synthetic data. A forwarded message can still be `True`. In the synthetic data, the label follows the **simulated evidence state** for the proposition, not the wording of the frame. Hearsay framing does *not* automatically mean `Unverifiable` in the synthetic batches. In the curated `hearsay` subset, every claim is `Unverifiable` because no checkable source exists for those claims. |
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| ### Synthetic labels |
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| Template rows get their label from the generator, together with a matching templated explanation. These labels are **not** individually human-reviewed (`label_origin = template_construction`). |
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| ### Curated test labels |
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| - Labelled by the dataset author (`label_source = user_labelled`). |
| - v2.2.1 review: the author reviewed model-suggested corrections, accepting 129 `Partially True → True` changes and confirming 1 label. The rationale is recorded verbatim in `change_v2_2_1`. Where a label changed, the explanation was cleared because it had been written for the old label. These 129 rows currently have no explanation. |
| - Sub-benchmarks: `human_verifiable_true_false` (50 science/health facts), `human_health_lifestyle` (144 claims), `human_hearsay` (150 claims). |
| - There was a single annotator (the dataset author). No agreement study has been done. |
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| ## 2. Sentiment (3-way) |
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| | label | criterion (from annotation notes) | |
| |---|---| |
| | `positive` | The comment expresses approval, affection, encouragement or happiness. | |
| | `negative` | It expresses disappointment, irritation, hurt, mockery or dismissal, including sarcastic consolation. | |
| | `neutral` | It is neutral, informational, ambiguous or too low in context to call. Short fragments are usually neutral. | |
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| Notes recorded in the data show these conventions: |
| - Mixed messages are labelled by their dominant valence ("bittersweet; nostalgia clause could read negative"). |
| - Teasing that targets the addressee is `negative` ("teasing tone; 'cute thaan aana timing worst'"). |
| - Changes from the model suggestion are recorded ("CHANGED from suggested 'positive': sarcastic consolation"). |
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| **Label status:** `silver` = model-annotated, pending human confirmation. **Never report silver labels as gold.** (The human-collected sentiment data, including all silver rows, is not part of this release.) |
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| ## 3. Abuse detection (binary) |
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| | label | criterion | |
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| | `abusive` | An insult, slur, profanity, threat, or attack directed at the addressee (including emoji-masked slurs and middle-finger emoji used with an insult). | |
| | `not_abusive` | An ordinary positive or negative comment with no insult, profanity, threat or attack. Mentioning an abuse term without using it against someone is a hard negative. | |
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| The recorded review rule for 19 human-collected comments was: "Ordinary positive/negative comment with no insult, profanity, threat or attack: not_abusive is correct." |
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| Some terms have a context-dependent reading, for example "'poda dei' alone reads playful here". Record the rationale in `annotation_note`. |
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| ## 4. What annotators must not do |
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| - Do not translate, normalise or spell-correct `text`. |
| - Do not infer a missing label from the text automatically. |
| - Do not collapse `Partially True` or `Unverifiable` into True/False. |
| - Do not upgrade `silver` to `gold` without a recorded human decision. Record that decision in `human_validated` and `label_source`. |
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