Event-centric human value understanding in news-domain texts: An actor-conditioned benchmark across multi-scope event contexts

This work presents NEVU , a benchmark for actor-conditioned , event-centric , and direction-aware human value recognition in news-domain texts. NEVU evaluates whether models can infer values from event-structured evidence, attribute them to the correct social actors, and determine their aligned or contradictory direction. Built from 2865 English news articles, NEVU represents news at four semantic levels, from subevents to composite events and full articles. Using a hierarchical taxonomy of 54 fine-grained and 20 coarse-grained values, the benchmark contains 46,589 semantic units, 72,905 annotated unit–actor pairs, and 168,061 directed value instances. NEVU is constructed through a staged LLM-assisted annotation and verification pipeline, with targeted human verification for unresolved cases. Candidate-level acceptance and agreement are further examined through a multi-group assessment. The experiments show that prompting-only models remain limited, whereas LoRA-tuned open-weight models substantially improve performance, with overall Micro-F1 gains of 29.77 and 8.21 percentage points over the strongest prompting-only open-weight and proprietary baselines, respectively. These gains primarily reflect learnability under the NEVU reference-label setting. NEVU provides a structured benchmark for systematic evaluation and supervised adaptation of actor-conditioned human value recognition in English news.