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Header

VTuber 1B: Live Chat and Moderation Events

VTuber 1B is an academic purpose NLP dataset, collecting over a billion live chats, superchats, and moderation events (bans/deletions) from virtual YouTubers' live streams.

Download the dataset from Kaggle Datasets and join #vtuber-1b channel on holodata Discord for discussions.

We also offer β€οΈβ€πŸ©Ή Sensai, a live chat dataset specifically made for building ML models for spam detection / toxic chat classification.

Provenance

  • Source: YouTube live chat events collected by our Honeybee cluster. Holodex is a stream index provider for Honeybee which covers Hololive, Nijisanji, 774inc, etc.
  • Temporal Coverage:
    • Chats: from 2021-01-15
    • Super chats: from 2021-03-16
    • Super stickers: from 2022-01-20 (N/A yet)
    • Membership joining events: from 2021-10-18 (N/A yet)
    • Membership milestones: from 2021-10-20 (N/A yet)
    • Membership gifts: N/A
    • Placeholders: from 2022-01-21 (N/A yet)
  • Update Frequency:
    • At least once every 6 months

Research Ideas

  • Toxic Chat Classification
  • Spam Detection
  • Demographic Visualization
  • Superchat Analysis
  • Training neural language models

See Kaggle public notebooks (VTuber 1B / VTuber 1B Elements) for ideas, as well as /notebooks folder in the repo.

We employed Honeybee cluster to collect real-time live chat events across major Vtubers' live streams. All sensitive data such as author name or author profile image are omitted from the dataset, and author channel id is anonymized by SHA-1 hashing algorithm with a grain of salt.

Editions

VTuber 1B Elements

Kaggle Datasets (2 MB)

VTuber 1B Elements is most suitable for statistical visualizations and exploratory data analysis.

filename summary
channels.csv Channel index
chat_stats.csv Chat statistics
superchat_stats.csv Super Chat statistics

VTuber 1B

Kaggle Datasets (47 GB)

VTuber 1B is most suitable for frequency analysis. This edition includes only the essential columns in order to reduce dataset size and make it faster from Kaggle Kernels to load data in.

filename summary
chats_%Y-%m.parquet Live chat events (> 1,000,000,000)
superchats_%Y-%m.parquet Super chat events (> 4,000,000)
deletion_events.parquet Deletion events
ban_events.parquet Ban events

VTuber 1B Complete

VTuber 1B Complete is only available to those approved by the admins. If you are interested in conducting research using this edition, please reach us at [email protected] (for organizations only).

filename summary
chats_%Y-%m.parquet Live chat messages (> 1,000,000,000)
superchats_%Y-%m.parquet Super chat messages (> 4,000,000)
deletion_events.parquet Deletion events
ban_events.parquet Ban events

Dataset Breakdown

Ban and deletion are equivalent to markChatItemsByAuthorAsDeletedAction and markChatItemAsDeletedAction respectively.

Channels (channels.csv)

column type description
channelId string channel id
name string channel name
englishName nullable string channel name (English)
affiliation string channel affiliation
group nullable string group
subscriptionCount number subscription count
videoCount number uploads count
photo string channel icon

Inactive channels have INACTIVE in group column.

Pandas usage

import pandas as pd

dtype_dict = {
    'channelId': 'category',
    'name': 'category',
    'englishName': 'category',
    'affiliation': 'category',
    'subscriptionCount': 'int32',
    'videoCount': 'int16',
    'photo': 'category'
}
chats = pd.read_csv('../input/vtuber-livechat-elements/channels.csv', dtype=dtype_dict)

Chat Statistics (chat_stats.csv)

column type description
channelId string channel id
period string interested period (%Y-%M)
chats number number of chats
memberChats number number of chats with membership status attached
uniqueChatters number number of unique chatters
uniqueMembers number number of unique members appeared on live chat
bannedChatters number number of unique chatters marked as banned by mods
deletedChats number number of chats deleted by mods

Pandas usage

import pandas as pd

chat_stats = pd.read_csv('../input/vtuber-livechat-elements/chat_stats.csv'))
sc_stats = pd.read_csv('../input/vtuber-livechat-elements/superchat_stats.csv'))
stats = pd.merge(chat_stats, sc_stats, on=['period', 'channelId'], how='left')

Super Chat Statistics (superchat_stats.csv)

column type description
channelId string channel id
period string interested period (%Y-%M)
superChats number number of super chats
uniqueSuperChatters number number of unique super chatters
totalSC number total amount of super chats (JPY)
averageSC number average amount of super chat (JPY)
totalMessageLength number total message length
averageMessageLength number average mesage length
mostFrequentCurrency string most frequent currency
mostFrequentColor string most frequent color

Chats (chats_%Y-%m.parquet)

column type description in standard version
timestamp string ISO 8601 UTC timestamp limited accuracy
id string chat id N/A
authorName string author name N/A
authorChannelId string author channel id anonymized
body string chat message N/A
bodyLength number chat message length standard version only
membership string membership status N/A
isMember nullable boolean is member (null if unknown) standard version only
isModerator boolean is channel moderator N/A
isVerified boolean is verified account N/A
videoId string source video id
channelId string source channel id

Membership status

value duration
unknown Indistinguishable
non-member 0
new < 1 month
1 month >= 1 month, < 2 months
2 months >= 2 months, < 6 months
6 months >= 6 months, < 12 months
1 year >= 12 months, < 24 months
2 years >= 24 months

Pandas usage

import pandas as pd

chats = pd.read_parquet('../input/vtuber-livechat/chats_2022-02.parquet')

Superchats (chats_:year:-:month:.parquet)

column type description in standard version
timestamp string ISO 8601 UTC timestamp limited accuracy
id string chat id N/A
authorName string author name N/A
authorChannelId string author channel id anonymized
body nullable string chat message N/A
amount number purchased amount
currency string three-letter currency symbol
color string color N/A
significance number significance
videoId string source video id N/A
channelId string source channel id

Color and Significance

color significance purchase amount (Β₯) purchase amount ($) max. message length
blue 1 Β₯ 100 - 199 $ 1.00 - 1.99 0
lightblue 2 Β₯ 200 - 499 $ 2.00 - 4.99 50
green 3 Β₯ 500 - 999 $ 5.00 - 9.99 150
yellow 4 Β₯ 1000 - 1999 $ 10.00 - 19.99 200
orange 5 Β₯ 2000 - 4999 $ 20.00 - 49.99 225
magenta 6 Β₯ 5000 - 9999 $ 50.00 - 99.99 250
red 7 Β₯ 10000 - 50000 $ 100.00 - 500.00 270 - 350

Pandas usage

import pandas as pd
from glob import iglob

sc = pd.concat([
    pd.read_parquet(f)
    for f in iglob('../input/vtuber-livechat/superchats_*.parquet')
], ignore_index=False)
sc.sort_index(inplace=True)

Deletion Events (deletion_events.parquet)

column type description in standard version
timestamp string UTC timestamp
id string chat id
retracted boolean is deleted by author oneself
videoId string source video id
channelId string source channel id

Pandas usage

Insert deleted_by_mod column to chats DataFrame:

chats = pd.read_parquet('../input/vtuber-livechat/chats_2022-02.parquet')
delet = pd.read_parquet('../input/vtuber-livechat/deletion_events.parquet', columns=['id', 'retracted'])

delet = delet[delet['retracted'] == 0]

delet['deleted_by_mod'] = True
chats = pd.merge(chats, delet[['id', 'deleted_by_mod']], how='left')
chats['deleted_by_mod'].fillna(False, inplace=True)

Ban Events (ban_events.parquet)

Here Ban means either to place user in time out or to permanently hide the user's comments on the channel's current and future live streams. This mixup is due to the fact that these actions are indistinguishable from others with the extracted data from markChatItemsByAuthorAsDeletedAction event.

column type description in standard version
timestamp string UTC timestamp
authorChannelId string channel id anonymized
videoId string source video id
channelId string source channel id

Pandas usage

Insert banned column to chats DataFrame:

chats = pd.read_parquet('../input/vtuber-livechat/chats_2022-02.parquet')
ban = pd.read_parquet('../input/vtuber-livechat/ban_events.parquet', columns=['authorChannelId', 'videoId'])

ban['banned'] = True
chats = pd.merge(chats, ban, on=['authorChannelId', 'videoId'], how='left')
chats['banned'].fillna(False, inplace=True)

Consideration

Anonymization

id and authorChannelId are anonymized by SHA-1 hashing algorithm with a pinch of undisclosed salt.

Handling Custom Emojis

All custom emojis are replaced with a Unicode replacement character οΏ½ (U+FFFD).

Redundant Ban and Deletion Events

Bans and deletions from multiple moderators for the same person or chat will be logged separately. For simplicity, you can safely ignore all but the first line recorded in time order.

Citation

@misc{vtuber-livechat-dataset,
 author={Yasuaki Uechi},
 title={VTuber 1B: Large-scale Live Chat and Moderation Events Dataset},
 year={2022},
 month={2},
 version={37},
 url={https://holodata.org/vtuber-1b}
}

License