How to apply zero-shot learning to text data in substance use research: An overview and tutorial with media data

Area of Science:

• Natural Language Processing
• Machine Learning
• Substance Use Research

Background:

• Vast amounts of daily media-related text data offer insights into substance use discussions.
• Traditional analysis methods like content analysis and deep learning models require significant manual annotation and resources.
• Zero-shot learning presents a more efficient alternative for analyzing large text datasets.

Purpose of the Study:

• To introduce the concept of zero-shot learning (ZSL) for text analysis.
• To demonstrate the application of ZSL in substance use research.
• To provide a practical tutorial on using ZSL for media-related text data.

Main Methods:

• Utilizing pre-existing zero-shot learning models trained on large, unlabelled datasets.
• Applying ZSL to classify previously unseen text data into relevant categories without task-specific training.
• Leveraging ZSL for analyzing media-related text data in substance use research.

Main Results:

• Zero-shot learning models can classify text data into categories they were not explicitly trained on.
• This approach significantly reduces the need for manual annotation and computational resources.
• ZSL enables quicker and more flexible analysis of media text concerning substance use.

Conclusions:

• Zero-shot learning is a powerful and resource-efficient machine learning technique for analyzing large volumes of text data.
• Its application in substance use research allows for rapid and flexible analysis of media discussions.
• ZSL holds significant potential for time-critical research and understanding evolving substance use trends.