--- license: cc-by-nc-4.0 viewer: false task_categories: - text-classification - image-classification size_categories: - 1K ⚠️ **Content warning:** These splits may contain graphic, sexual, and self-harm imagery. Users can work with specific harm types (on moderated split, see below) to avoid certain content. ### `moderated` — Platform-moderated posts Posts that were flagged and actioned by the Bluesky moderation system. Covers all nine harm categories with roughly balanced label distribution. > See [**Working with specific harm categories**](#load-a-specific-harm-category-by_harm) > below for how to load individual label subsets without loading the full split. | Label | Group | Count | |---|---|---| | safe | safe | 163 | | porn | sexual | 111 | | sexual_figurative | sexual | 106 | | nudity | sexual | 104 | | self_harm | graphic | 102 | | threat | behavioural | 99 | | sexual | sexual | 95 | | graphic_media | graphic | 89 | | intolerant | behavioural | 74 | | rude | behavioural | 57 | > Each label has a dedicated pre-split file under `data/moderated/by_harm/`. > See the [Usage](#usage) section for loading examples. ### `random` — Random firehose sample 1 000 posts sampled uniformly from the public Bluesky firehose (Mar-Dec 2025). Used as a realistic class-imbalanced baseline (≈97 % safe). Platform labels are sparse. ### `safe` — Curated safe posts 1 000 posts curated from verified individuals and organizations accounts and where all human annotators unanimously agreed the content is safe. ### `near_moderated` — Semantically similar firehose posts 988 posts from the public firehose that are semantically similar to pilot-moderated posts (retrieved via embedding nearest-neighbour search). ≈35 % flagged as unsafe. --- ## Harm Taxonomy | Category | Group | Description | |---|---|---| | `self_harm` | graphic | Depictions of self-injury or suicide | | `graphic_media` | graphic | Gore, violence, or disturbing imagery | | `intolerant` | behavioural | Hate speech targeting protected characteristics | | `rude` | behavioural | Harassment, insults, or abusive language | | `threat` | behavioural | Explicit or implied threats of harm | | `porn` | sexual | Explicit sexual content | | `sexual` | sexual | Suggestive sexual content (not explicit) | | `nudity` | sexual | Non-sexual nudity | | `sexual_figurative` | sexual | Sexual content in art / figurative media | | `safe` | safe | No policy violation detected | --- ## Data Structure Each split contains: ``` data/ {split}/ records.jsonl ← one post per line annotations.jsonl ← one annotation per line (keyed by post id) by_harm/ ← (moderated only) merged records+annotations per label graphic/ self_harm.jsonl graphic_media.jsonl behavioural/ intolerant.jsonl rude.jsonl threat.jsonl sexual/ porn.jsonl sexual.jsonl nudity.jsonl sexual_figurative.jsonl media/ {split}/ images/ ← JPEG/PNG/WEBP image files videos/ ← MP4/MOV video files ``` ### `records.jsonl` schema ```json { "id": "post_XXXXXXXX", // sha256-derived 8-char post ID "split": "moderated", "text": "...", // post text "date": "2025-06...", "lang": "en", // ISO 639-1 language code (or 'und' if undetermined) "media": [ { "type": "image", "hf_path": "data/media/moderated/images/bafkrei....jpeg", "hf_url": "https://huggingface.co/datasets/usermodbench/ModerationBench/resolve/main/...", "filename": "bafkrei....jpeg" } ] } ``` ### `annotations.jsonl` schema ```json { "id": "post_XXXXXXXX", "split": "moderated", "is_harmful": true, "primary_harm": "porn", "harm_group": "sexual", "annotator_id": "majority_vote", "confidence": 1.0, // fraction of annotators who agreed with majority "agreed": true // true = unanimous agreement } ``` --- ## Annotation Methodology - **Annotators:** Three trained annotators per post - **Protocol:** Each annotator independently labelled posts as *Safe* or *Unsafe*; if unsafe, they selected one or more harm categories from the taxonomy above. - **Aggregation:** Majority vote across annotators. The `agreed` field is `true` when the majority was unanimous. The `confidence` field is the fraction of annotators who agreed with the final label. - **Tiebreaking :** When the two primary annotators disagreed, one of two tiebreaker annotators was called in. `agreed = false` for tiebroken rows. --- ## Privacy & Ethics - Post URIs are removed; posts are identified only by their pseudonymous `post_XXXXXXXX` ID. - This dataset is released under **CC BY-NC 4.0** — non-commercial use only. --- ## Usage ### Load a full split ```python from datasets import load_dataset # Records and annotations for the moderated split rec = load_dataset( "usermodbench/ModerationBench", data_files={"train": "data/moderated/records.jsonl"}, split="train", ) ann = load_dataset( "usermodbench/ModerationBench", data_files={"train": "data/moderated/annotations.jsonl"}, split="train", ) import pandas as pd df = pd.DataFrame(rec).merge(pd.DataFrame(ann), on="id") print(df[["id", "text", "primary_harm", "confidence"]].head()) ``` ### Load a specific harm category (`by_harm/`) The `moderated` split ships pre-split files under `data/moderated/by_harm//