A Multimodal Analysis of Influencer Content on Twitter
Danae Sánchez Villegas Catalina Goanta Nikolaos Aletras
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.