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Datasets:
usermodsky
/
ModerationBench

Tasks:
Text Classification
Image Classification
Size:
1K<n<10K
Tags:
content-moderation
safety
multimodal
bluesky
social-media
harm-detection
License:
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Moderation bench
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metadata 
license: cc-by-nc-4.0
viewer: false
task_categories:
  - text-classification
  - image-classification
size_categories:
  - 1K<n<10K
tags:
  - content-moderation
  - safety
  - multimodal
  - bluesky
  - social-media
  - harm-detection
  - benchmark
pretty_name: ModerationBench
configs:
  - config_name: records
    data_files:
      - split: moderated
        path: data/moderated/records.jsonl
      - split: random
        path: data/random/records.jsonl
      - split: safe
        path: data/safe/records.jsonl
      - split: near_moderated
        path: data/near_moderated/records.jsonl
  - config_name: annotations
    data_files:
      - split: moderated
        path: data/moderated/annotations.jsonl
      - split: random
        path: data/random/annotations.jsonl
      - split: safe
        path: data/safe/annotations.jsonl
      - split: near_moderated
        path: data/near_moderated/annotations.jsonl
  - config_name: moderated_by_harm
    data_files:
      - split: graphic__self_harm
        path: data/moderated/by_harm/graphic/self_harm.jsonl
      - split: graphic__graphic_media
        path: data/moderated/by_harm/graphic/graphic_media.jsonl
      - split: behavioural__intolerant
        path: data/moderated/by_harm/behavioural/intolerant.jsonl
      - split: behavioural__rude
        path: data/moderated/by_harm/behavioural/rude.jsonl
      - split: behavioural__threat
        path: data/moderated/by_harm/behavioural/threat.jsonl
      - split: sexual__porn
        path: data/moderated/by_harm/sexual/porn.jsonl
      - split: sexual__sexual
        path: data/moderated/by_harm/sexual/sexual.jsonl
      - split: sexual__nudity
        path: data/moderated/by_harm/sexual/nudity.jsonl
      - split: sexual__sexual_figurative
        path: data/moderated/by_harm/sexual/sexual_figurative.jsonl

ModerationBench

A multimodal benchmark for evaluating automated content-moderation systems on real-world social-media posts from Bluesky. Each post is accompanied by human-annotation label: Safe/Unsafe, covering nine fine-grained harm categories across three harm groups, plus a safe class.

Dataset Summary

Split Posts Media files
moderated 1,000 978
random 1,000 320
safe 1,000 0
near_moderated 988 654
Total 3,988 1,952

Splits

⚠️ 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 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 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 

{
  "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 

{
  "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 

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/<group>/<label>.jsonl. Each row is a merged record + annotation (no separate join needed).

Available paths:

  • data/moderated/by_harm/graphic/self_harm.jsonl
  • data/moderated/by_harm/graphic/graphic_media.jsonl
  • data/moderated/by_harm/behavioural/intolerant.jsonl
  • data/moderated/by_harm/behavioural/rude.jsonl
  • data/moderated/by_harm/behavioural/threat.jsonl
  • data/moderated/by_harm/sexual/porn.jsonl
  • data/moderated/by_harm/sexual/sexual.jsonl
  • data/moderated/by_harm/sexual/nudity.jsonl
  • data/moderated/by_harm/sexual/sexual_figurative.jsonl
from datasets import load_dataset

# Load a single harm category
self_harm = load_dataset(
    "usermodbench/ModerationBench",
    data_files={"train": "data/moderated/by_harm/graphic/self_harm.jsonl"},
    split="train",
)
print(f"self_harm posts: {len(self_harm)}")

# Load an entire harm group (all sexual content categories)
import glob
sexual_files = [
    "data/moderated/by_harm/sexual/porn.jsonl",
    "data/moderated/by_harm/sexual/sexual.jsonl",
    "data/moderated/by_harm/sexual/nudity.jsonl",
    "data/moderated/by_harm/sexual/sexual_figurative.jsonl",
]
sexual = load_dataset(
    "usermodbench/ModerationBench",
    data_files={"train": sexual_files},
    split="train",
)
print(f"All sexual-group posts: {len(sexual)}")
print(sexual[0])

Metadata

Machine-readable metadata for this dataset is available:

The Croissant file conforms to MLCommons Croissant 1.0 and includes rai: fields covering data limitations, sensitive information, intended use cases, and provenance.