叙事性
讲故事
计算机科学
标题
阅读(过程)
叙述的
交互式叙事方式
社会化媒体
比例(比率)
序列(生物学)
语言学
人机交互
多媒体
语言模型
心理学
选择(遗传算法)
用户参与度
万维网
钥匙(锁)
分散注意力
启发式
工作(物理)
新媒体
简单(哲学)
作者
Debashish Ghose,Susan Mudambi,Subodha Kumar,Joydeep Srivastava
标识
DOI:10.1287/isre.2022.0650
摘要
Social media has transformed how people consume news, making storytelling design as important as headline design. We examine three storytelling design features—narrativity (linear versus summary first), emotional sequence (good to bad versus bad to good), and reading level (simple versus complex)—and show that their effects on engagement depend on both news format and audience motivation. We find that effective engagement requires conditional rather than universal rules. For satirical content, humor enhances engagement mainly among motivated audiences, where higher narrativity and complex language paired with bad-to-good emotional sequences work best. For less motivated audiences, simpler satire is more effective when presented in summary-first form with good-to-bad sequences. For traditional news, prior work suggests that simple language helps; whereas we find this to be generally true, our results show that when complex language is unavoidable, pairing it with linear narrativity and bad-to-good sequences can enhance engagement. These results provide guidance for publishers who must balance clarity, complexity, and audience expectations. Beyond news, our findings generalize to videos, podcasts, and interactive media, where storytelling design similarly shaped engagement. We also demonstrate how large language models (LLMs) can generate controlled story variations, enabling creators to scale production and platforms to optimize recommendations in audience-specific ways.
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