DIRECT: Decomposing Audience Preference and Creative Effect in Visual Content Analytics
DIRECT：分解视觉内容分析中的受众偏好与创作效应AI 解读 ↓
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DIRECT：分解视觉内容分析中的受众偏好与创作效应
- 研究问题
- 创作者之间的受众差异与同一创作者改变视觉风格后的互动变化，如何共同影响合并估计及其创作建议？
- 方法与思路
- 作者结合 Mundlak 组间与组内分解及双重机器学习，在创作者面板中分析潜在视觉语言处理，区分两类效应。
- 材料陈述的贡献
- 摘要称部分视觉属性的两类效应符号相反，合并系数可能误导创作方向，并提出估计目标错配诊断及三个审计工具。
- 阅读关注点
- 核对组内变化获得因果解释的条件、潜在视觉处理的构造与共同变化处理，以及留出创作者上的收益评价设计。
- 阅读边界
- 这是 arXiv 预印本，同行评审及接收未核实；摘要未说明完整识别条件，不能据此确认建议具有可推广的因果收益。
原文摘要
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.