arXiv AI By Maciej Markiewicz, Beata Bajcar, Wiktoria Mieleszczenko-Kowszewicz, Aleksander Szcz\k{e}sny, Tomasz Adamczyk, Grzegorz Chodak, Karolina Ostrowska, Aleksandra Sawczuk, Jolanta Babiak, Jagoda Szklarczyk, Przemys{\l}aw Kazienko

How Annotation Trains Annotators: Competence Development in Social Influence Recognition

Read the original on arXiv AI →

arXiv:2604. 02951v2 Announce Type: replace-cross Abstract: Human data annotation, especially when involving experts, is often treated as an objective reference.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

arXiv AI
Jun 16

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

arXiv:2606. 16910v1 Announce Type: cross Abstract: IMPACTeen is a dataset of textual social influence scenarios spanning interpersonal, media-based, and digital settings in an adolescent context.

By Aleksander Szcz\k{e}sny, Wiktoria Mieleszczenko-Kowszewicz, Maciej Markiewicz, Beata Bajcar, Tomasz Adamczyk, Jolanta Babiak, Grzegorz Chodak, Przemys{\l}aw Kazienko
arXiv AI
Jun 2

Who Annotates in NLP? A Large-scale Assessment of Human Annotation Reporting between 2018 and 2025

arXiv:2606. 02255v1 Announce Type: cross Abstract: Human annotation is the empirical foundation of much NLP research, from dataset construction to model evaluation, but papers often leave unclear who produced the annotations and how the annotation process was controlled.

By Maria Kunilovskaya, Gagan Bhatia, Lisa Sophie Albertelli, Yanran Chen, Christian Greisinger, Lotta Kiefer, Christoph Leiter, Subhadeep Roy, Tewodros Achamaleh, Muhammad Arslan Manzoor, Sebastian Pohl, Yufang Hou, Steffen Eger