AI 中文总结
本研究利用大规模 Instagram 赞助帖子数据集，通过可解释的回归模型预测参与度，并发现按实体类型或受众层级细分账户可提高预测效果。
AI 中文摘要
在这项工作中，我们通过收集和分析大规模赞助 Instagram 帖子数据集，探索用户如何在社交媒体平台上与赞助内容互动。为了保持透明度，我们倾向于使用稳健的统计分析和可解释模型，而非深度学习技术。我们的流程从多个维度对 Instagram 账户进行分类，包括受众规模和实体类型。我们辅以从帖子标题和标签中提取的语义特征，并利用这些元素训练回归模型来预测参与度。我们在一个包含超过 1500 万条 Instagram 帖子、由超过 70 万个账户发布且包含赞助内容的数据集上验证了我们的方法。我们的分析表明，当账户按实体类型或受众层级进行细分时，每条帖子的参与度在一定程度上是可预测的，并且高度优化。我们的模型在保持完全透明（即哪些特征驱动参与度）的同时，实现了具有竞争力的预测能力。
英文摘要
In this work we explore how users interact with sponsored content on social media platforms by collecting and analyzing a large-scale dataset of sponsored Instagram posts. To maintain transparency, we favor robust statistical analysis and explainable models over deep learning techniques. Our pipeline categorizes Instagram accounts along multiple dimensions, including audience size and entity type. We complement this with semantic features extracted from post captions and hashtags, and use these elements to train regression models that forecast engagement. We validate our approach on a dataset comprising over 15M Instagram posts authored by over 700K accounts featuring sponsored content. Our analysis shows that per-post engagement is partly predictable and highly optimized when accounts are segmented by entity type or audience tier. Our models achieve competitive predictive power, while remaining fully transparent about which features drive engagement.
