docs/annotation_guidelines.md

Annotation guidelines — CodeMixCheck v2.2.1

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.

1. Fact verification (4-way)

General rule

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.

Labels

label	criterion	canonical explanation pattern (most frequent in data)
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."

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.

Framing is not the label

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.

Synthetic labels

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).

Curated test labels

• 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.

2. Sentiment (3-way)

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.

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").

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.)

3. Abuse detection (binary)

label	criterion
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.

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."

Some terms have a context-dependent reading, for example "'poda dei' alone reads playful here". Record the rationale in annotation_note.

4. What annotators must not do

• 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.