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

arXiv 2606.16910 ↗First posted 15 Jun 2026cs.CL cs.AIcited by 0

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Abstract (arXiv)

IMPACTeen is a dataset of textual social influence scenarios spanning interpersonal, media-based, and digital settings in an adolescent context. It contains 1,021 texts, 5,100 individual annotation records, and gold labels for social influence techniques, with each text annotated from five distinct perspectives: teenagers, parents, psychologists, communication experts, and teachers. The resource was constructed through constrained LLM generation, followed by a two-step human editing and validation phase aimed at ensuring youth-context realism. A multi-dimensional annotation covered influence presence, techniques, intentions, consequences, resistance, reactions, and annotation confidence. The dataset supports research on social influence detection, annotator disagreement, cross-lingual modeling, and the training and evaluation of language models. The dataset was created in Polish and is accompanied by a corresponding English version.

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References · 10

  1. 2020DeBERTa: Decoding-enhanced BERT with Disentangled Attentioncited by 2,181
  2. 2019Persuasion for Good: Towards a Personalized Persuasive Dialogue System for Social Goodcited by 209
  3. 2016Winning Arguments: Interaction Dynamics and Persuasion Strategies in Good-faith Online Discussionscited by 110
  4. 2024MentalManip: A Dataset For Fine-grained Analysis of Mental Manipulation in Conversationscited by 21
  5. 2024PIRB: A Comprehensive Benchmark of Polish Dense and Hybrid Text Retrieval Methodscited by 14
  6. 2024CoDa: Constrained Generation based Data Augmentation for Low-Resource NLPcited by 9
  7. 2025Persuasion Should be Double-Blind: A Multi-Domain Dialogue Dataset With Faithfulness Based on Causal Theory of Mindcited by 8
  8. 2025Unraveling SITT: Social Influence Technique Taxonomy and Detection with LLMscited by 2
  9. 2024Effective Unsupervised Constrained Text Generation based on Perturbed Maskingcited by 1
  10. 2026How Annotation Trains Annotators: Competence Development in Social Influence Recognitioncited by 1

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