Navigating the complex landscape of news articles involves understanding the
various actors or entities involved, referred to as news stakeholders. These
stakeholders, ranging from policymakers to opposition figures, citizens, and
more, play pivotal roles in shaping news narratives. Recognizing their
stakeholder types, reflecting their roles, political alignments, social
standing, and more, is paramount for a nuanced comprehension of news content.
Despite existing works focusing on salient entity extraction, coverage
variations, and political affiliations through social media data, the automated
detection of stakeholder roles within news content remains an underexplored
domain. In this paper, we bridge this gap by introducing an effective approach
to classify stakeholder types in news articles. Our method involves
transforming the stakeholder classification problem into a natural language
inference task, utilizing contextual information from news articles and
external knowledge to enhance the accuracy of stakeholder type detection.
Moreover, our proposed model showcases efficacy in zero-shot settings, further
extending its applicability to diverse news contexts.

新闻利益相关者的类型分类方法，利用新闻文章的上下文信息和外部知识，将利益相关者识别问题转化为自然语言推理任务，在不同的新闻背景中展示出了高效性。

从文本到背景：一种新闻利益相关者分类的蕴涵方法

From Text to Context: An Entailment Approach for News Stakeholder  Classification

Recent legislation proposals have significantly increased the demand for
eXplainable Artificial Intelligence (XAI) in many businesses, especially in
so-called `high-risk' domains, such as recruitment. Within recruitment, AI has
become commonplace, mainly in the form of job recommender systems (JRSs), which
try to match candidates to vacancies, and vice versa. However, common XAI
techniques often fall short in this domain due to the different levels and
types of expertise of the individuals involved, making explanations difficult
to generalize. To determine the explanation preferences of the different
stakeholder types - candidates, recruiters, and companies - we created and
validated a semi-structured interview guide. Using grounded theory, we
structurally analyzed the results of these interviews and found that different
stakeholder types indeed have strongly differing explanation preferences.
Candidates indicated a preference for brief, textual explanations that allow
them to quickly judge potential matches. On the other hand, hiring managers
preferred visual graph-based explanations that provide a more technical and
comprehensive overview at a glance. Recruiters found more exhaustive textual
explanations preferable, as those provided them with more talking points to
convince both parties of the match. Based on these findings, we describe
guidelines on how to design an explanation interface that fulfills the
requirements of all three stakeholder types. Furthermore, we provide the
validated interview guide, which can assist future research in determining the
explanation preferences of different stakeholder types.

为满足招聘中不同利益相关者类型对解释的不同偏好，我们创建并验证了一种半结构化访谈指南，并使用基于实证的理论对访谈结果进行了结构化分析，发现候选人、招聘人员和公司确实具有强烈不同的解释偏好。基于这些发现，我们提出了如何设计满足三种利益相关者需求的解释界面的指南，并提供了验证过的访谈指南，可用于协助未来研究确定不同利益相关者类型的解释偏好。