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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2024
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| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2412.18300 |
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| _version_ | 1866917878783016960 |
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| author | Ku, Wen Liu, Yihan Zhang, Wei An, Pengcheng |
| author_facet | Ku, Wen Liu, Yihan Zhang, Wei An, Pengcheng |
| contents | AI-generated media products are increasingly prevalent in the news industry, yet their impacts on audience perception remain underexplored. Traditional media often employs negative framing to capture attention and capitalize on news consumption, and without oversight, AI-generated news could reinforce this trend. This study examines how different framing styles-constructive versus non-constructive-affect audience responses in AI-generated podcasts. We developed a pipeline using generative AI and text-to-speech (TTS) technology to create both constructive and non-constructive news podcasts from the same set of news resources. Through empirical research (N=65), we found that constructive podcasts significantly reduced audience's negative emotions compared to non-constructive podcasts. Additionally, in certain news contexts, constructive framing might further enhance audience self-efficacy. Our findings show that simply altering the framing of AI generated content can significantly impact audience responses, and we offer insights on leveraging these effects for positive outcomes while minimizing ethical risks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_18300 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | GenPod: Constructive News Framing in AI-Generated Podcasts More Effectively Reduces Negative Emotions Than Non-Constructive Framing Ku, Wen Liu, Yihan Zhang, Wei An, Pengcheng Human-Computer Interaction AI-generated media products are increasingly prevalent in the news industry, yet their impacts on audience perception remain underexplored. Traditional media often employs negative framing to capture attention and capitalize on news consumption, and without oversight, AI-generated news could reinforce this trend. This study examines how different framing styles-constructive versus non-constructive-affect audience responses in AI-generated podcasts. We developed a pipeline using generative AI and text-to-speech (TTS) technology to create both constructive and non-constructive news podcasts from the same set of news resources. Through empirical research (N=65), we found that constructive podcasts significantly reduced audience's negative emotions compared to non-constructive podcasts. Additionally, in certain news contexts, constructive framing might further enhance audience self-efficacy. Our findings show that simply altering the framing of AI generated content can significantly impact audience responses, and we offer insights on leveraging these effects for positive outcomes while minimizing ethical risks. |
| title | GenPod: Constructive News Framing in AI-Generated Podcasts More Effectively Reduces Negative Emotions Than Non-Constructive Framing |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2412.18300 |