数据说话,但谁给它一个声音? 了解数据驱动新闻文章中的说服力策略
IEEE transactions on visualization and computer graphics
|December 15, 2025
概括
这项研究引入了一个新的分类系统,包括15种用于数据驱动新闻的说服力策略. 开发了一个机器学习模型来识别这些策略及其对公众论的影响.
科学领域:
- 计算社会科学 计算社会科学
- 沟通研究 沟通研究
- 数据可视化 数据可视化
背景情况:
- 数据驱动的新闻文章利用叙事和可视化来塑造公众论.
- 这些文章中的说服力策略,包括框架和视觉修辞,是影响解释的关键.
- 之前的研究还没有充分探讨数据驱动新闻创作者所做的具体战略选择.
研究的目的:
- 在数据驱动的新闻文章中开发一个全面的说服力策略分类.
- 为分析创建一个注释数据驱动新闻文章的数据集.
- 构建和评估一种多式联络,多任务学习模型,以识别说服力策略及其影响.
主要方法:
- 开发了15种说服力策略的分类法,分为五个维度:可信度,指导性解释,基于参考的框架,情感吸引力和参与邀请.
- 936篇数据驱动新闻文章的数据集被策划并注释了说服力策略及其感知效应.
- 开发了一种多模式,多任务的学习模型,整合了文本,可视化,视觉叙事和心理语言特征.
主要成果:
- 开发的模型成功地预测了说服力策略的存在及其影响.
- 该模型在识别说服力策略和衡量其影响方面表现优于现有的最先进的基线.
- 精心策划的数据集和分类学为数据驱动新闻中的说服力技术的大规模分析提供了基础.
结论:
- 这项研究为理解数据驱动新闻中的说服力策略提供了一个新的框架.
- 开发的计算模型为分析数据可视化和叙述的说服性提供了一个有效的工具.
- 这项研究通过提供研究信息影响的新方法,为计算社会科学和通信领域做出了贡献.
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