With the prevalence of sponsorship practice using social media posts, the detection of sponsored content becomes crucial for platforms to regulate the generated content and prevent users from being misinformed. However, there is a paucity of investigations on the detection of sponsored content in existing research. To fill this research gap, we first identify several task-related clues by referring to relevant psychological theories and practical observations. Based on the clues, we conceptualize four types of sponsored content features and propose a unified deep learning detection framework, which also learns the relative importance of each feature. Experiments conducted on 26,823 social media posts demonstrate the performance of our proposed model compared with competitive alternatives and the value of each feature. The learned feature importance also enables deeper phenomena understanding. The research findings provide actionable insights into the narrative strategies influencers adopt and how to distinguish online sponsored content.

随着使用社交媒体帖子的赞助实践的盛行，赞助内容的检测对于平台规范生成的内容和防止用户被误导变得至关重要。然而，现有研究中对赞助内容检测的调查很少。为了填补这一研究空白，我们首先通过参考相关的心理学理论和实际观察来确定几个与任务相关的线索。基于这些线索，我们概念化了四种类型的赞助内容特征，并提出了一个统一的深度学习检测框架，该框架还学习了每个特征的相对重要性。对 26,823 个社交媒体帖子进行的实验证明了我们提出的模型与竞争替代品相比的性能以及每个特征的价值。学习到的特征重要性还可以实现更深入的现象理解。研究结果为影响者采用的叙事策略以及如何区分在线赞助内容提供了可行的见解。