Generative AI and AI-generated Contents on Social Media

Permanent URI for this collectionhttps://hdl.handle.net/10125/112451

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    Understanding the Impact of AI Label Disclosure on Engagement with Deepfakes Videos
    (2026-01-06) Wang, Qinhui; Bao, Chenzhang; Delen, Dursun; Okçular, Emre
    The rapid proliferation of AI-generated content on digital platforms—particularly deepfakes involving synthetic audio and videos—has intensified concerns about misinformation, content authenticity, and user trust. In response, major platforms have implemented AI labeling policies to enhance transparency and mitigate the societal risks posed by deepfakes. Drawing on the Elaboration Likelihood Model, this study empirically examines the effectiveness of such interventions by analyzing the relationship between video inauthenticity and user engagement, and how AI label disclosure moderates this effect. We analyzed a dataset of 787 TikTok videos, generated by 30 unique content creators between November 12, 2024, and April 14, 2025. We found that video inauthenticity negatively impacts user engagement, such as liking and sharing. The AI label disclosure has a positive moderating role in the negative relationship between video inauthenticity and user engagement. Our research informs policy and design strategies for platform governance in an era increasingly shaped by generative AI.
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    Beyond Memory: Emotional and Ethical Responses to AI-Generated Digital Resurrection Content
    (2026-01-06) Nam, Kwang Min; Jo, Gi Jun; Choi, Hanbyul
    This study explores consumer perceptions of AI-generated “digital resurrection” content, in which deceased individuals are recreated through artificial intelligence. YouTube user comments were collected and analyzed using KoBERTopic for topic modeling, KoELECTRA for sentiment analysis, and a 2-mode network approach to examine linguistic patterns. The results reveal that viewers expressed multifaceted reactions, including emotional empathy, appreciation for technology, and ethical concerns. High-frequency keywords such as “tears,” “memory,” “technology,” and “heaven” indicate a convergence of emotional resonance and technical evaluation. Sentiment analysis showed relatively positive responses, shaped by users’ personal experiences, cultural values, and attitudes toward AI. These findings suggest that digital resurrection is perceived not merely as a technological feat, but as a meaningful medium for posthumous communication. This study contributes to ongoing discussions regarding cultural acceptance, emotional impact, and ethical implications of AI technologies in commemorative and social contexts.
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    Messaging Maneuvers: Generating and Evaluating Strategic Counternarratives with Large Language Models
    (2026-01-06) Leekha, Rohan; Simek, Olga; Tse, Adam; Loof, Travis; Pant, Nishka
    As Large Language Models (LLMs) become increasingly embedded in content moderation and public communication, their potential to both generate and evaluate strategic counterspeech demands close study. Our work introduces a novel pipeline for producing contextualized counterspeech aligned with the BEND maneuver taxonomy, and evaluates the quality and effectiveness of generated countermessages by using an LLM-as-a-judge guided by Tree-of-Thought prompting. We compare automated LLM-as-a-judge evaluations against human surveys grounded in cognitive science. We analyze their alignment across multiple dimensions that help us assess the overall persuasiveness of the BEND aligned counterspeech. LLM-as-a-judge and human feedback have strong alignment in particular to messages human raters find dismissive or distortive. This indicates the LLM-as-a-judge is well aligned for detecting manipulation, incoherence, and threatening behaviors. There is also significant perceived persuasiveness alignment for explanatory messages, which suggests that these types of messages have the potential to change people's attitudes and behaviors.
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