Abstract：Influencer marketing, in which companies sponsor social media personalities to promote their brands, has exploded in popularity in recent years. One common criterion for selecting an influencer partner is popularity. While some firms collaborate with “mega” influencers with millions of followers, other firms partner with “micro” influencers with only several thousand followers, but who also cost less to sponsor. To quantify this trade-off between popularity and cost, the authors develop a framework for estimating the follower elasticity of impressions (FEI), which measures a video’s percentage gain in impressions (i.e., views) corresponding to a percentage increase in the number of followers of its creator. Computing FEI involves estimating the causal effect of an influencer’s popularity on the view counts of their videos, which is achieved through a combination of (1) a unique data set collected from TikTok, (2) a representation learning model for quantifying video content, and (3) a machine learning-based causal inference method. The authors find that FEI is always positive, averaging .10, but often nonlinearly related to follower size. They examine the factors that predict variation in these FEI curves and show how firms can use these results to better determine influencer partnerships.

主要发现：粉丝规模的边际价值并非线性，选 mega 还是 micro 应看粉丝弹性与成本，而不是只看总粉丝数。

Abstract：On user-generated content platforms, individuals and firms alike seek to build and expand their follower base to eventually increase the reach of the content they upload. The bulk of the seeding literature in marketing suggests targeting users with a large follower base, that is, high-status influencers. In contrast, some recent studies find targeting lower-status influencers to be a more effective seeding policy. This multimethod article shifts the focus from the follower base of the seeding target to the focal content creator. The authors propose accelerating natural triadic closure by leveraging first-degree followers as interconnectors to target second-degree followers, that is, the nearby (low-status) influencers (who are interconnected with the focal content creator). Empirical studies document that this seeding target is much more effective for building and expanding the follower base, compared with targeting influencers who are not interconnected with the focal content creator-that is, the remote (both high- and low-status) influencers-by 2,300% and 46%, respectively. These studies on the acceleration of natural triadic closure are augmented by a preregistered field experiment to obtain convergent validity of the findings.

主要发现：网红选择要看网络位置；与创作者已有连接的“近邻网红”可能比遥远的大网红更能带来增长。

Abstract：Excessive monetary compensation and existing contractual agreements of influencers limit the ability of many firms to engage in effective influencer seeding. The authors suggest a forward-looking approach of targeting prospective influencers-while they are still largely unknown (e.g., a few months after their platform registration)-and signing them to endorse the firm in the future (e.g., more than a year later). This approach has the potential to significantly reduce costs. However, as only rarely do newly registered users ultimately become influencers (and as signals are weak), the authors propose a novel framework to cope with this rare-event problem. For empirical demonstration and application, the authors conduct data-based simulations using a data set from a worldwide leading audio platform. Every wave of newly registered users is associated with a profit potential stemming from future endorsements by prospective influencers. With knowledge about the order of magnitude of the return on successful influencer spend, managers applying the framework can extract around 20% of this profit potential (if the return is around three times the spend).

主要发现：与其高价追成熟网红，不如早期识别潜在网红；难点是处理“极少数人成为网红”的稀有事件预测。

Abstract：This paper explores the impact of using advanced technology such as artificial intelligence (AI) to match marketers with social media influencers. We develop a theoretical model to examine how matching accuracy affects the competition between influencers and the profitability of a social media platform. Our findings show that improving matching accuracy may not always benefit the platform, especially for platforms with intermediate follower density. Two opposing effects of technology improvement affect the prices of influencer marketing campaigns: advanced technology, such as AI, enhances the matching between influencers and marketers and also intensifies competition between different types of influencers. The overall effect on prices can be negative for some influencers because of the asymmetric nature of such matching technology: the matching outcome for influencers with a narrower audience (niche influencers) is more sensitive to matching accuracy than that for those with a broader audience (general influencers). As a result, more niche influencers begin to participate in marketing campaigns when matching accuracy improves, which reduces the prices offered by sufficiently general influencers and may lead to a decline in platform revenue. Additionally, we find that adjusting commission rates in response to technology improvements could help mitigate the negative impact although it may not eliminate it entirely. Our findings offer valuable insights for social media platforms seeking to remain competitive in the influencer marketing landscape.

主要发现：AI 匹配提高效率的同时也改变竞争结构；平台要同时设计匹配技术和佣金机制。

Abstract：In this paper, we propose a Deep-DiD method that incorporates two deep neural networks in a difference-in-difference (DiD) framework to estimate heterogeneous treatment effects (HTEs). The dual-network architecture contains one neural network modeling HTEs as a nonparametric function of pretreatment features and another neural network capturing individual and time fixed effects. Through a series of simulations, we show that our method can uncover the true HTEs with high accuracy under various settings and demonstrates more robust estimation performance compared with existing methods like linear models and random forests. We apply this method to an empirical setting where a large videosharing platform introduced a “Creator Signing Program” aimed at signing creators and motivating them to generate more high-quality video content. Leveraging a matched data set of signed and unsigned creators, we employ our Deep-DiD method to estimate the HTEs of the signing program. Our method can help the platform optimize creator selection by identifying creators with the highest-estimated treatment effects. Through out-of-sample tests, we show that creators selected by the Deep-DiD method experience substantially larger actual performance jumps than those selected by the platform. Creator selection based on the Deep-DiD method also consistently outperforms that based on linear models.

主要发现：创作者选择可以被视为异质性处理效应问题；平台应选择“签约后提升最大”的人，而不只是当前表现最好的人。