aiqbench analysis
IMPACTeen represents a significant advancement in NLP by providing a specialized dataset for detecting social influence techniques within adolescent communication contexts. The resource leverages constrained LLM generation combined with rigorous multi-perspective human validation to ensure high-fidelity annotations across five distinct stakeholder viewpoints. This tool will facilitate more robust training and evaluation of language models specifically designed to understand manipulation, persuasion, and resistance in youth-centric scenarios.
IMPACTeen: Intentions, Manipulation, Persuasion, Annotations, and Consequences in Teen Communication Dataset
arXiv:2606.16910v1 Announce Type: new Abstract: 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.
Abstract: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.
Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
arXiv:2606.16910 [cs.CL]
(or arXiv:2606.16910v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2606.16910 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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Full text under the publisher's open licence · 217 words
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