Influencer marketing involves a wide range of strategies in which brands
collaborate with popular content creators (i.e., influencers) to leverage their
reach, trust, and impact on their audience to promote and endorse products or
services. Because followers of influencers are more likely to buy a product
after receiving an authentic product endorsement rather than an explicit direct
product promotion, the line between personal opinions and commercial content
promotion is frequently blurred. This makes automatic detection of regulatory
compliance breaches related to influencer advertising (e.g., misleading
advertising or hidden sponsorships) particularly difficult. In this work, we
(1) introduce a new Twitter (now X) dataset consisting of 15,998 influencer
posts mapped into commercial and non-commercial categories for assisting in the
automatic detection of commercial influencer content; (2) experiment with an
extensive set of predictive models that combine text and visual information
showing that our proposed cross-attention approach outperforms state-of-the-art
multimodal models; and (3) conduct a thorough analysis of strengths and
limitations of our models. We show that multimodal modeling is useful for
identifying commercial posts, reducing the amount of false positives, and
capturing relevant context that aids in the discovery of undisclosed commercial
posts.

本研究介绍了一个新的推特数据集，并使用文本和视觉信息实验了多模型预测方法，表明我们提出的交互注意力方法在检测商业推广内容方面优于现有的多模态模型。我们还对模型的优势和局限性进行了彻底分析，结果显示多模态建模有助于识别商业帖子，减少误报，并捕捉有助于发现未公开商业帖子的相关上下文。