Eunchae Jang
Biographic Data
| ID | 8835807 |
|---|---|
| NAME | Eunchae Jang |
| GIVEN NAMES | Eunchae |
| FAMILY NAME | Jang |
| SIGNATURE | JANG E |
| AFFILIATIONS | Pennsylvania State University |
| ORCID | 0000-0001-7547-9050 |
| VERIFIED | Yes |
| TOTAL WORKS | 5 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 5 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2024 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 0 |
Seeing isn't always believing
The proliferation of realistic-looking visuals such as maps and images that are deliberately fabricated to spread misinformation has ignited a public trust crisis. Does the visual nature of this misinformation make it more credible and memorable? Does debunking affect people’s credibility judgments and memory of misinformation? This study explores these questions by conducting an online pre-post test experiment (N = 317). Accordingly, we compare …
Mitigating the News-Finds-Me Perception
Social media foster a News-Finds-Me perception (NFM), whereby individuals believe important news will “find them” without actively seeking it. Although the negative consequences of NFM are well-documented, strategies to mitigate NFM’s effects remain unexplored. In a pre-registered 3 (educational warning: general vs. personalized vs. control) ✕ 2 (NFM level: low vs. high) between-subjects experiment ( N = 405), we test whether general educational …
Textual and Comparative Analyses on AI Policies
The rapid advancement of AI has raised pressing moral considerations, prompting many stakeholders to publish guidelines for responsible development. However, the specific virtues organizations prioritize in their AI policies, and the extent to which they acknowledge AI’s potential role as a moral agent, remain underexplored. We conducted a textual and comparative analysis of AI principles issued by Big Tech corporations (OpenAI, Google, Microsoft…
Racial Bias in AI Training Data
Given that the nature of training data is the primary cause of algorithmic bias, do laypersons realize that systematic misrepresentation and under-representation of certain races in the training data can affect AI performance in a way that privileges some races over others? To answer this question, we conducted three between-subjects online experiments (N = 769 in total) with a prototype of an AI system that recognizes emotion-based facial expres…
An Experimental Test of the Effects of Public Mockery of a Social Media Health Campaign
This study explored how social media users’ mocking of a public health campaign can affect other users’ emotions, cognitions, and behavioral intentions. Inspired by public mocking of the CDC’s “Say No to Raw Dough” campaign aiming to prevent food poisoning caused by eating raw flour-based products, this experiment (N = 681) employed a 2 (Public responses to a PSA: Mocking or serious) x 3 (Organizational response to public responses: Self-mocking,…
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An Experimental Test of the Effects of Public Mockery of a Social Media Health Campaign
This study explored how social media users’ mocking of a public health campaign can affect other users’ emotions, cognitions, and behavioral intentions. Inspired by public mocking of the CDC’s “Say No to Raw Dough” campaign aiming to prevent food poisoning caused by eating raw flour-based products, this experiment (N = 681) employed a 2 (Public responses to a PSA: Mocking or serious) x 3 (Organizational response to public responses: Self-mocking,…
Textual and Comparative Analyses on AI Policies
The rapid advancement of AI has raised pressing moral considerations, prompting many stakeholders to publish guidelines for responsible development. However, the specific virtues organizations prioritize in their AI policies, and the extent to which they acknowledge AI’s potential role as a moral agent, remain underexplored. We conducted a textual and comparative analysis of AI principles issued by Big Tech corporations (OpenAI, Google, Microsoft…
Racial Bias in AI Training Data
Given that the nature of training data is the primary cause of algorithmic bias, do laypersons realize that systematic misrepresentation and under-representation of certain races in the training data can affect AI performance in a way that privileges some races over others? To answer this question, we conducted three between-subjects online experiments (N = 769 in total) with a prototype of an AI system that recognizes emotion-based facial expres…
Seeing isn't always believing
The proliferation of realistic-looking visuals such as maps and images that are deliberately fabricated to spread misinformation has ignited a public trust crisis. Does the visual nature of this misinformation make it more credible and memorable? Does debunking affect people’s credibility judgments and memory of misinformation? This study explores these questions by conducting an online pre-post test experiment (N = 317). Accordingly, we compare …
Mitigating the News-Finds-Me Perception
Social media foster a News-Finds-Me perception (NFM), whereby individuals believe important news will “find them” without actively seeking it. Although the negative consequences of NFM are well-documented, strategies to mitigate NFM’s effects remain unexplored. In a pre-registered 3 (educational warning: general vs. personalized vs. control) ✕ 2 (NFM level: low vs. high) between-subjects experiment ( N = 405), we test whether general educational …
Media Influence and Health (3 works) · Misinformation and Its Impacts (3 works) · Perception (3 works) · Behavioral Health and Interventions (2 works) · Ethics and Social Impacts of AI (2 works) · Social media (2 works) · Advertising (1 work) · Affect (linguistics) (1 work) · Anger (1 work) · Artificial Intelligence in Law (1 work)