DIRECT：视觉内容分析中受众偏好与创意效果的分解
DIRECT: Decomposing Audience Preference and Creative Effect in Visual Content Analytics
AI总结：
该研究提出DIRECT框架，分解视觉内容分析中受众偏好与创意效果，发现二者在部分属性上符号相反，合并系数推荐的创意方向可能错误，使用DIRECT可减少参与度损失。
AI中文摘要：
哪些视觉选择能让帖子表现更好？现有越来越多的文献通过对大量创作者估算的合并系数回答该问题，平台将这些系数转化为创意推荐。我们发现这些系数融合了两种不同模式，同一属性的两种模式可能指向相反方向。第一种是受众偏好：偏好某一风格的创作者会吸引构成不同的受众，因此他们的帖子表现不同是因为观看者的构成，而非单篇帖子本身的效果。第二种是创意效果：衡量创作者偏离其常规视觉风格时，受众的反应。合并估计会将两种效果平均，受众偏好的影响可能大到足以反转创意方向所需的信号。我们提出DIRECT（通过因果工具分解识别响应效应），这是一种基于面板数据的因果推理框架，将Mundlak组间-组内分解与双机器学习结合，处理创作者内部共变的潜在视觉-语言处理。我们将其应用于1527位创作者发布的232088条Instagram美妆赞助帖子，以及11个CLIP衍生的视觉风格轴。结果显示，11个属性中有4个的受众偏好与创意效果符号相反，11个属性中有2个的合并系数本身会推荐错误的创意方向：在肤色属性上，合并系数推荐更浅的呈现，而创意效果指向相反方向，因为创作者的受众对比其基线更深的肤色参与度更高。在留存创作者样本中，使用合并系数制定推荐会损失31%的可实现参与度增益。我们的贡献包括：诊断视觉内容分析中的估计量不匹配问题；提出从观测面板数据中恢复决策相关估计量的框架；以及三个用于审计合并估计是否支持其决策的可移植诊断工具。
英文摘要：
Which visual choices make a post perform better? A growing literature answers this question with pooled coefficients estimated across many creators, which platforms translate into creative recommendations. We show that these coefficients blend two distinct patterns that can point in opposite directions for the same attribute. The first, audience preference, arises because creators who favor a style attract differently composed audiences, so their posts perform differently because of who is watching, not what any single post does. The second, creative effect, captures how a creator's audience responds when she departs from her usual look. Pooled estimation averages the two, and audience preference can be large enough to reverse the signal that creative direction requires. We propose DIRECT (Decomposed Identification of Response Effects via Causal Tools), a panel-based causal-inference framework that separates them, combining the Mundlak between-within decomposition with double machine learning over latent vision-language treatments that co-vary within a creator. We apply it to 232,088 sponsored Instagram beauty posts across 1,527 creators and 11 CLIP-derived visual style axes. The two carry opposite signs on 4 of 11 attributes, and on 2 of 11 the pooled coefficient itself recommends the wrong creative direction: on skin tone, it favors lighter representations while the creative effect points the other way, since a creator's audience engages more with tones darker than her baseline. On held-out creators, prescribing from the pooled coefficient forgoes 31% of the achievable engagement gain. We contribute a diagnosis of estimand mismatch in visual content analytics, a framework that recovers the decision-relevant estimand from observational panel data, and three portable diagnostics for auditing whether pooled estimates support the decisions they inform.
