论文标题
通过停留时间和在社交媒体浏览环境中的参与度量化注意力
Quantifying attention via dwell time and engagement in a social media browsing environment
论文作者
论文摘要
现代计算系统具有前所未有的检测，利用和影响人类注意力的能力。先前的工作将用户参与度和停留时间确定为数字环境中关注的两个关键指标，但是这些指标尚未集成到一个统一的模型中，该模型可以推进数字关注的理论和实践。我们借鉴了认知科学，数字广告和AI的工作，为社交媒体环境提出了两阶段的关注模型，该模型可以使参与和居住。在一个在线实验中，我们表明注意力在这两个阶段的运作方式不同，并找到明确的分离证据：当居住在帖子上（第1阶段）时，用户对耸人听闻而不是可靠的内容更多，但是在决定是否参与内容（第2阶段）时，用户会出现更多的可信度，而不是轰动性内容。这些发现对衡量和建模人类注意力的计算系统的设计和开发具有影响，例如社交媒体上的新闻源算法。
Modern computational systems have an unprecedented ability to detect, leverage and influence human attention. Prior work identified user engagement and dwell time as two key metrics of attention in digital environments, but these metrics have yet to be integrated into a unified model that can advance the theory andpractice of digital attention. We draw on work from cognitive science, digital advertising, and AI to propose a two-stage model of attention for social media environments that disentangles engagement and dwell. In an online experiment, we show that attention operates differently in these two stages and find clear evidence of dissociation: when dwelling on posts (Stage 1), users attend more to sensational than credible content, but when deciding whether to engage with content (Stage 2), users attend more to credible than sensational content. These findings have implications for the design and development of computational systems that measure and model human attention, such as newsfeed algorithms on social media.
