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Researcher profile

Hunjun Shin

Hunjun Shin contributes to research discovery and scholarly infrastructure.

ResearcherAffiliation not importedOpen to collaborate
Artificial Intelligencecs.CYHuman-Computer InteractionSocial and Information Networks

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Published work

2 published item(s)

preprint2026arXiv

How Diplomacy Reshapes Online Discourse:Asymmetric Persistence in Online Framing of North Korea

Public opinion toward foreign adversaries shapes and constrains diplomatic options. Prior research has largely relied on sentiment analysis and survey based measures, providing limited insight into how sustained narrative changes (beyond transient emotional reactions) might follow diplomatic engagement. This study examines the extent to which high stakes diplomatic summits shape how adversaries are framed in online discourse. We analyze U.S.-North Korea summit diplomacy (2018-2019) using a Difference-in-Difference(DiD) design on Reddit discussions. Using multiple control groups (China, Iran, Russia) to adjust for concurrent geopolitical shocks, we integrate a validated Codebook LLM framework for framing classification with graph based discourse network analysis that examines both edge level relationships and community level narrative structures. Our results reveal short term asymmetric persistence in framing responses to diplomacy. While both post level and comment level sentiment proved transient (improving during the Singapore Summit but fully reverting after the Hanoi failure),framing exhibited significant stability: the shift from threat oriented to diplomacy oriented framing was only partially reversed. Structurally, the proportion of threat oriented edges decreased substantially (48% -> 28%) while diplomacy oriented structures expanded, and these shifts resisted complete reversion after diplomatic failure. These findings suggest that diplomatic success can leave a short-term but lasting imprint on how adversaries are framed in online discourse, even when subsequent negotiations fail.

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preprint2026arXiv

Upskilling with Generative AI: Practices and Challenges for Freelance Knowledge Workers

Freelance workers must continually acquire new skills to remain competitive in online labor markets, yet they lack the organizational training, mentorship, and infrastructure available to traditional employees. Generative AI-powered tools like ChatGPT are reshaping market skill demands while also offering new forms of on-demand learning support to meet those demands. Despite growing interest in AI-powered learning tools, little is known about how freelancers actually use these tools to learn, the challenges they encounter, and how generative AI for learning interacts with precarity and competition in platform-based work. We present a mixed-methods study combining a survey and semi-structured interviews with freelance knowledge workers. Grounded in self-directed learning theory, we examine how freelancers integrate generative AI tools into their learning practices. Our findings show that freelancers increasingly rely on generative AI to structure learning and support exploratory skill acquisition, but do not treat it as their primary learning resource due to inconsistency, lack of contextual relevance, and verification overhead. We identify a shift from learning as growth to learning as survival, where upskilling is oriented toward immediate market viability rather than long-term development. We also surface a structural challenge we term invisible competencies, in which workers acquire skills through generative AI tools but lack credible ways to signal or validate these skills in competitive freelance markets. Based on these insights, we offer design recommendations for generative AI-powered learning tools for freelancers.

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