IMPACTeen: Intentions, Manipulation, Persuasion, Annotations, and Consequences in Teen Communication Dataset

2026-06-15 · Source: Artificial Intelligence · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics, Social Sciences & Behavioral Studies · Depth: Expert, quick

Summary

The IMPACTeen dataset is a new resource designed for studying social influence scenarios within an adolescent context, encompassing interpersonal, media-based, and digital communications. It comprises 1,021 texts and 5,100 individual annotation records, featuring gold labels for various social influence techniques. Each text was annotated from five distinct perspectives: teenagers, parents, psychologists, communication experts, and teachers. The dataset was developed using constrained LLM generation, followed by a rigorous two-step human editing and validation process to ensure realism relevant to youth. Its multi-dimensional annotations cover influence presence, techniques, intentions, consequences, resistance, reactions, and annotation confidence. IMPACTeen supports research in social influence detection, annotator disagreement, cross-lingual modeling, and the training and evaluation of language models, available in both Polish and English versions.

Key takeaway

For research scientists developing language models or analyzing social dynamics in adolescent communication, the IMPACTeen dataset offers a uniquely annotated resource. You should consider integrating this multi-perspective dataset to enhance model training for social influence detection, improve understanding of annotator disagreement, and validate cross-lingual capabilities. This resource can significantly refine your models' ability to interpret nuanced social cues and intentions in youth-centric digital interactions.

Key insights

The IMPACTeen dataset provides a multi-perspective, youth-contextualized resource for social influence research and language model development.

Principles

• Social influence is multi-faceted.
• Diverse annotator perspectives enrich data.
• LLM generation can be constrained for realism.

Method

The dataset was constructed via constrained LLM generation, followed by a two-step human editing and validation process to ensure youth-context realism and multi-dimensional annotation across five expert perspectives.

In practice

• Detect social influence techniques.
• Analyze annotator disagreement.
• Train and evaluate language models.

Topics

• IMPACTeen Dataset
• Social Influence Detection
• Adolescent Communication
• Language Model Training
• Multi-perspective Annotation
• Cross-lingual NLP

Best for: AI Scientist, NLP Engineer, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence.