license: apache-2.0
task_categories:
- text-classification
- clustering
- tabular-classification
language:
- en
pretty_name: Ultimate Werewolf Bluffing Structured Datasets (SD1 & SD2)
size_categories:
- 1K<n<10K
tags:
- social-deduction
- ultimate-werewolf
- bluffing
- deception
- game-analysis
- behavioral-analysis
- machine-learning
- clustering
Ultimate Werewolf Bluffing Structured Datasets (SD1 & SD2)
Dataset Summary
This repository contains two structured datasets, Structured Dataset 1 (SD1) and Structured Dataset 2 (SD2), designed for the study of bluffing behavior in Ultimate Werewolf, a social deduction game. The datasets provide player-level behavioral representations extracted from game transcripts shared by slhleosun and bolinlai.
Each record represents a single player during a single game round, summarizing the player's behavior throughout that round rather than individual utterances.
Dataset Creation
Source Data
The structured datasets were generated from a temporary dataset whose utterances were previously annotated for bluffing behavior using DeepSeek API. Each sentence was classified according to whether it contained:
- Role Bluff
- Vote Bluff
- Both Role and Vote Bluff
- No Bluff
After bluff identification, the original 13 attributes were transformed into 35 behavioral features through feature engineering. These features include:
Behavioral metrics
- Number of accusations
- Number of denials
- Other conversational behavior statistics
Bluff-related metrics
- Number of bluffs
- Bluff temporal distribution
- Bluff frequency
Interaction features
- Voting targets
- Player interaction statistics
- Conversation dynamics
Role-based features
- Role changes
- Werewolf indicator
- Other role-related information
The data were then grouped by:
- Game
- Round
- Player
A player-round was considered a bluffer whenever at least one utterance during that round was classified as a bluff.
Structured Dataset 1 (SD1)
SD1 contains 3,782 player-round records, each represented by 35 engineered attributes.
Bluff Distribution
| Bluff Type | Records |
|---|---|
| Role Bluff | 2,603 |
| Vote Bluff | 973 |
| Both Role and Vote Bluff | 206 |
The imbalance between bluff types is expected because vote bluffs are generally easier for other players to verify during gameplay, making them less frequent than role bluffs.
Role Distribution
| Role Category | Records |
|---|---|
| Werewolf | 1,957 |
| Other Roles | 1,825 |
Statistical Analysis
The dataset was inspected through graphical exploration and statistical testing.
Although the Shapiro–Wilk test rejected global normality (statistic = 0.261, p-value = 5.124 × 10⁻¹⁷⁰), inspection of the model residuals showed that most attributes presented approximately normal or only slightly skewed residual distributions. This supports the application of Analysis of Variance (ANOVA) for feature relevance assessment.
Feature Selection
ANOVA Type II was employed to evaluate the statistical relevance of each engineered attribute.
Type II ANOVA was selected because:
- bluff categories are naturally unbalanced;
- the remaining variables present comparatively balanced distributions;
- Type II provides appropriate significance estimates under these conditions.
Attributes with:
- p-value > 0.05
- low F-statistic
were considered statistically non-significant and removed.
The discarded attributes were:
- Round duration
- Mean utterance length
- Number of indecisive statements
- Number of decisive statements
- Players mentioned
- Vote consistency rate
Structured Dataset 2 (SD2)
SD2 is a filtered version of SD1 containing the same 3,782 player-round records after removing statistically non-significant attributes identified through ANOVA Type II.
The objective of SD2 is to provide a cleaner feature space for machine learning algorithms while preserving all player-round observations.
Compared to SD1, SD2:
- contains fewer features;
- removes redundant or statistically irrelevant variables;
- maintains identical player and bluff distributions.
Potential Applications
These datasets can be used for:
- Deception detection
- Bluff classification
- Player profiling
- Behavioral pattern discovery
- Unsupervised clustering
- Explainable AI
- Social interaction modeling
- Human behavior analysis
- Conversational analytics
- Representation learning
Limitations
- Only player-rounds containing at least one identified bluff are included.
- The datasets originate from Ultimate Werewolf gameplay and may not generalize to other social deduction games.
- Bluff labels depend on the annotation methodology used during dataset construction.
- Bluff categories are naturally imbalanced, particularly between role and vote bluffs.
License
This dataset is distributed under the Apache License 2.0.
You are free to:
- use
- modify
- redistribute
- build upon
the dataset in accordance with the terms of the Apache 2.0 License.
For the full license text, see: