Join Our Humour Style Dataset Creation Project!
Put your acting skills to use while having fun
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Welcome!
Thank you for your interest in participating in our study. To get started, please:
Read the Participant Information Sheet (PIS): This document provides important details about the study, including its purpose, risks, and benefits.
Sign the Participant Consent Form: Once you've read the PIS, please click on this consent form link or the button below to complete your registration.
Complete a brief demographic form: After signing the consent form, you'll automatically be redirected to participant form to provide some basic demographic information to help us better understand our participants.
Video Dataset Creation
Video Dataset Creation
Are you ready to showcase your talents and contribute to groundbreaking research? We're inviting YOU to participate in our captivating project focused on creating a comprehensive humour style dataset!
Project Summary
Project Summary
We're creating a multimodal humour style recognition system, using text, audio, and video to identify self-enhancing, self-deprecating, affiliative, and aggressive humour styles.
Compensation Offered
Compensation Offered
£50 for your great contributions
Time Commitment
Time Commitment
Approximately 2.5 hours
Eligibility Criteria
Eligibility Criteria
Individuals, 18 years and older.
Fluent in English.
Trained in role-playing or acting.
What You Will Do
What You Will Do
Act out written jokes, conveying emotions and humour styles through expressions, tone, and actions while being recorded.
Fill an emotion form
Study Starts
Study Starts
September 2024
Location
Location
Imperial College London, South Kensington
Approved by
Approved by
SETREC Imperial College London Ethics
Why it Matters
Why it Matters
Your involvement supports research benefiting psychology, therapy, computer science, and the social sciences. Help enhance well-being and expand our understanding of humour-related behaviours for improved mental health interventions.
Why Create a Video Dataset
Why Create a Video Dataset
Humour, a vital element of human interaction, plays a pivotal role in promoting mental and physical well-being. Yet the challenge lies in automatically recognising and categorising humour styles linked to health. Currently, there is a notable absence of machine learning (ML) models and annotated datasets tailored for the precise detection of the four main humour styles: self-enhancing, self-deprecating, affiliative, and aggressive.
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