Washii/Cyberbullying-Detection-CB2

Cyberbullying Detection — CB2

Dataset Description

CB2 is a relational, conversation-level cyberbullying detection dataset. Unlike single-post datasets, each instance in CB2 represents a pair of users and their full message exchange. The cyberbullying label is determined at the conversation level (i.e., whether the interaction between two users constitutes cyberbullying), enriched with demographic information, social closeness (peerness), and message-level aggression statistics.

The dataset was constructed from a school-age online communication study involving students aged 8–17 across 15 anonymised schools.

This dataset is part of the Cyberbullying-Detection collection on Hugging Face.

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Dataset Structure

Data Fields

Field	Type	Description
user1_id	int	Unique ID of User 1 (initiator)
user2_id	int	Unique ID of User 2 (recipient)
user1_age	int	Age of User 1
user1_gender	string	Gender of User 1 (Male, Female, Others)
user1_grade	int	School grade of User 1
user2_age	int	Age of User 2
user2_gender	string	Gender of User 2 (Male, Female, Others)
user2_grade	int	School grade of User 2
total_messages	int	Total number of messages exchanged between the pair
aggressive_count	int	Number of messages classified as aggressive
intent_to_harm	float	Computed intent-to-harm score (0.0–1.0)
peerness	float	Social closeness / similarity score between the two users (0.0–1.0)
conversation	list[dict]	Ordered list of messages: each entry is {"message": str, "label": int} where label is 1 (aggressive) or 0 (non-aggressive)
label	int	Binary cyberbullying label: 1 = cyberbullying, 0 = not cyberbullying

Label Classes

Value	Meaning
1	The user-pair interaction constitutes cyberbullying
0	The user-pair interaction does not constitute cyberbullying

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Source Files

CB2 was assembled from 6 source files:

File	Role
1. users_data.csv	Demographic info per user (age, gender, school, grade)
2. peerness_values.csv	Pairwise social closeness scores
3. Aggressive_All.csv	Corpus of all aggressive messages (reference)
4. Non_Aggressive_All.csv	Corpus of all non-aggressive messages (reference)
5. Communication_Data_Among_Users.csv	Timestamped message log with per-message aggression labels
6. CB_Labels.csv	Pivot file — one row per user-pair with aggregated stats and final CB label

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Dataset Splits

The dataset is split as follows:

Split	Size	Description
train	75% of total	Training set
validation	2,000 rows	Development / validation set (sampled from the 25% held-out portion)
test	Remaining ~25% minus 2,000	Test set

Split Methodology

from sklearn.model_selection import train_test_split

# Step 1: 75% train, 25% test+dev (stratified on label)
train_df, test_dev_df = train_test_split(df, test_size=0.25, random_state=42, stratify=df["label"])

# Step 2: 2000 rows for dev, rest for test
dev_df = test_dev_df.sample(n=2000, random_state=42)
test_df = test_dev_df.drop(dev_df.index)

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Usage

from datasets import load_dataset

dataset = load_dataset("Washii/Cyberbullying-Detection-CB2")

# Access splits
train = dataset["train"]
validation = dataset["validation"]
test = dataset["test"]

# Example row
print(train[0])
# {
#   'user1_id': 1, 'user2_id': 2,
#   'user1_age': 11, 'user1_gender': 'Others', 'user1_grade': 5,
#   'user2_age': 15, 'user2_gender': 'Male', 'user2_grade': 9,
#   'total_messages': 36, 'aggressive_count': 23,
#   'intent_to_harm': 0.769, 'peerness': 0.5,
#   'conversation': [
#       {'message': 'bye bye dear bajaj...', 'label': 1},
#       {'message': 'Article updated', 'label': 0},
#       ...
#   ],
#   'label': 1
# }

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Construction Notes

• The conversation field is built by grouping all messages in 5. Communication_Data_Among_Users.csv by (User1 ID, User2 ID), sorted by Date and Time, and stored as a list of {message, label} dicts.
• The peerness field in the final table comes directly from 6. CB_Labels.csv (which already incorporates values from 2. peerness_values.csv).
• User demographics are joined twice (once for User 1, once for User 2) from 1. users_data.csv.
• Files 3. Aggressive_All.csv and 4. Non_Aggressive_All.csv are reference corpora and are not directly joined into the final table (their content is already represented via file 5).

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Source Data

The original data is sourced from https://data.mendeley.com/datasets/wmx9jj2htd/2 (A Comprehensive Dataset for Automated Cyberbullying Detection)

Citation

If you use this dataset, please cite the original source appropriately.

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Dataset Card Authors

Uploaded and curated by Washii.