Hyelim Lee

This study examines the ambivalent effects of corporate social responsibility (CSR) during organizational crises. Although CSR aims to enhance reputation and legitimacy, unfulfilled expectations can intensify negative crisis perceptions. Drawing on expectation violation theory, this experiment investigates how CSR associations, crisis types, and relationship norms shape public perceptions of expectancy violation and crisis responsibility, and how these perceptions translate into organizational attitudes and negative secondary crisis communication (SCC) intentions on social media. Using a 2 (CSR association vs. none) × 3 (crisis type: performance vs. CSR-related morality vs. no CSR-related morality) × 2 (relationship norms: exchange vs. communal) between-subjects design, the results show no direct main effects of CSR association on attitudes. In morally aligned CSR crises, stronger CSR associations heighten expectancy violation and responsibility compared to non-aligned crises, even under communal relationship norms. However, in performance crises, CSR association reduces responsibility via a halo effect, yet increased violation and responsibility drive negative attitudes and secondary crisis communication.

Analysis of public opinions collected from digital media helps organizations maintain positive relationships with the public. Such public relations (PR) analysis often involves assessing opinions, for example, measuring how strongly people trust an organization. Pre-trained Large Language Models (LLMs) hold great promise for supporting Organization-Public Relationship Assessment (OPRA) because they can map unstructured public text to OPRA dimensions and articulate rationales through prompting. However, adapting LLMs for PR analysis typically requires fine-tuning on large labeled datasets, which is both labor-intensive and knowledge-intensive, making it difficult for PR researchers to apply these models. In this paper, we present OPRA-Vis, a visual analytics system that leverages LLMs for OPRA without requiring extensive labeled data. Our framework employs Chain-of-Thought prompting to guide LLMs in analyzing public opinion data by incorporating PR expertise directly into the reasoning process. Furthermore, OPRA-Vis provides visualizations that reveal the clues and reasoning paths used by LLMs, enabling users to explore, critique, and refine model decisions. We demonstrate the effectiveness of OPRA-Vis through two real-world use cases and evaluate it quantitatively, through comparisons with alternative LLMs and prompting strategies, and qualitatively, through assessments of usability, effectiveness, and expert feedback.

The study introduces new approaches that integrate public relations concepts to identify adopters of AI technologies. It seeks to discover novel methods for identifying target groups likely to adopt AI technologies, even in contexts where these technologies are not yet familiar. The study achieves this by employing latent profile analysis, regression, and structural equation modeling using data from a South Korean online panel survey (N = 625). The results identify five distinct public profiles based on their relationship assessments, problem recognition, and general trust in AI: the Cautious, Balanced, Uninterested, Confident, and Enthusiastic. Notably, the Enthusiastic group—characterized by high trust and strong relationships with the service provider—showed the strongest interest in adopting AI, highlighting their potential as key publics for introducing new AI services in business contexts. Additionally, the article contributes to public segmentation theory through the use of the situational theory of problem-solving and enhances the applicability of established public relations frameworks to the evolving field of AI.

Government agencies tasked with soliciting comments from the American public in response to changes in rulemaking have long been interested in finding effective techniques to automatically process spam comments, including flagging and filtering duplicate comments. Duplicate submissions are problematic because they obscure genuine public input and may amplify fringe opinions nonrepresentative of the majority of the American public. Given that duplicate comments are generated from templates or predrafted texts, they can easily overwhelm the commenting process, and massive opinion spam campaigns have made manually flagging duplicate comments a costly and time-consuming task for rulemaking agencies. To help agencies in their deduplication efforts, we developed and tested a highly accurate and cost-effective technique based on unsupervised deep machine learning. Our method used the graph augmented deep learning model to embed the comment texts into vectors, and then we applied the clustering method to the embedded vectors to identify similar comments. To test the efficacy of the proposed approach, we applied our technique to the raw data from the Federal Communications Commission’s (FCC) proposed rule change on Net Neutrality in 2017. Our study results demonstrate the superiority of the proposed deep learning technique at identifying duplicate comments, relative to other state-of-the-art deduplication approaches.

Seongbum Seo, Sangbong Yoo, Hyelim Lee, Yun Jang, Ji Hwan Park, Jeong-Nam Kim. Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (System Demonstrations). 2025.

Purpose: Although belief in conspiracy theories has been researched since the 1970s, specific research on conspiratorial thinking in the workplace is scarce. Conspiratorial thinking could be fostered among employees in workplaces because of unequal power relations resulting from the organizational hierarchy. This study examines workplace conspiracy attribution (WCA) as employees’ attribution of problematic events in the workplace as being plotted by powerful actors within their organizations and tests its antecedents and consequences.Design/methodology/approach: A survey dataset collected from employees in South Korea (N = 600) was used. This study tested three variables (i.e. two-way communication, employee–organization relationship quality, and perceived ethical orientation) as antecedent conditions of WCA and two outcome variables (i.e. turnover intention and whistleblowing potential) as consequences.Findings: Perceived ethical orientation mediates the relationship between two-way communication and WCA. WCA was found to be positively associated with turnover intention and whistleblowing potential.Originality/value: This study adopts a public relations lens to understand the significant roles of WCA in reducing turnover intention and whistleblowing potential. It expands existing knowledge of the significance of power and power disparities in organizations.

This chapter reviews the theoretical origins of the situational theory of problem solving (STOPS) by discussing how it evolved from the central concept of publics in public relations and its predecessor, the situational theory of publics (STP). Both theories were proposed to guide public relations practice in identifying and segmenting publics by explaining the situational factors which motivate individuals to act for or against organizations. The chapter provides a summary of how STOPS has been applied to different disciplines including health communication, employee communication, government communication, public affairs, crisis communication, tourism, and disaster communication. It also offers insights into how STOPS can be applied in future research, especially in today’s post-truth society characterized by the rise of misinformation and the erosion of trust.