A Multimodal Analysis of Influencer Content on Twitter

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.Comment: Accepted at AACL 202

Throw the Book at Them: Why the FTC Needs to Get Tough with Influencers

The Federal Trade Commission is an administrative agency that has traditionally been aggressive when deploying its delegated authority. At the core of these actions is the FTC’s interpretive definition of deception as based upon a reasonable consumer standard. Specifically, the commission has regularly used Section 5(a) of the FTC Act, in tandem with its interpretive definition of deception, as a sword in a variety of contexts, including enforcement actions for deceptive advertising, endorsements, and claim substantiation against a range of industries. These include successfully brought actions or consent decrees obtained in enforcement proceedings against powerful economic entities, including Google and Facebook. Yet, in one area, the FTC has been reluctant to engage in the hard tactics it regularly deploys in other areas. The Commission has struggled to employ a coherent enforcement strategy for deceptive practices by Social Media Influencers. The Commission has taken significant steps towards deception and disclosure enforcement for influencers, including publication of a set of guidelines for disclosure. However, with the exception of a series of warning letters sent to high profile influencers in April of 2017, the Commission has not engaged in a significant enforcement action—choosing instead to launch an inquiry in February 2020 to review the disclosure guidelines. As empirical research demonstrates that consumers do not understand the nature of the influencer process, this Article argues that the FTC should employ a commitment to a robust enforcement stance. The FTC’s failure to “make an example” of high-profile influencers or to take a hardline approach with influencers, as the Commission did with native advertising online, represents a parting with the manner with which the Commissions has traditionally enforced the deception standard in endorsement ads. This departure, this Article argues, is undermining the FTC’s consumer protections

A Multimodal Analysis of Influencer Content on Twitter

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

Closing the Loop: Testing ChatGPT to Generate Model Explanations to Improve Human Labelling of Sponsored Content on Social Media

Regulatory bodies worldwide are intensifying their efforts to ensure transparency in influencer marketing on social media through instruments like the Unfair Commercial Practices Directive (UCPD) in the European Union, or Section 5 of the Federal Trade Commission Act. Yet enforcing these obligations has proven to be highly problematic due to the sheer scale of the influencer market. The task of automatically detecting sponsored content aims to enable the monitoring and enforcement of such regulations at scale. Current research in this field primarily frames this problem as a machine learning task, focusing on developing models that achieve high classification performance in detecting ads. These machine learning tasks rely on human data annotation to provide ground truth information. However, agreement between annotators is often low, leading to inconsistent labels that hinder the reliability of models. To improve annotation accuracy and, thus, the detection of sponsored content, we propose using chatGPT to augment the annotation process with phrases identified as relevant features and brief explanations. Our experiments show that this approach consistently improves inter-annotator agreement and annotation accuracy. Additionally, our survey of user experience in the annotation task indicates that the explanations improve the annotators' confidence and streamline the process. Our proposed methods can ultimately lead to more transparency and alignment with regulatory requirements in sponsored content detection.Comment: Accepted to The World Conference on eXplainable Artificial Intelligence, Lisbon, Portugal, July 202

“It’s Just Addictive People That Make Addictive Videos”: Children’s Understanding of and Attitudes towards Influencer Marketing of Food and Beverages by YouTube Video Bloggers

Exposure to influencer marketing of foods and beverages high in fat, sugar, and/or salt (HFSS) increases children’s immediate intake. This study qualitatively explored children’s understanding of, and attitudes towards, this marketing, to elucidate potential mechanisms through which exposure affects behavior. In six focus groups (n = 4) children (10–11 years) were shown a YouTube video featuring influencer marketing of an HFSS product. Inductive thematic analysis identified six themes from children’s discussions of this marketing: (1) YouTubers fill a gap in children’s lives, (2) the accessibility of YouTubers increases children’s understanding of their actions, (3) influencer marketing impacts all—the influencer, the brand, and the viewer, (4) attitudes towards influencer marketing are most affected by a YouTuber’s familiarity, (5) YouTuber influencer marketing is effective because they are not ‘strangers’, (6) children feel able to resist influencer marketing of HFSS products. Children had an understanding of the persuasive intent of this marketing, and although most were sceptical, familiar YouTubers elicited particularly sympathetic attitudes. Children felt affected by influencer marketing of HFSS products, but believed they were able to resist it. Beyond theoretical insight, this study adds to the growing body of evidence to suggest children’s exposure to HFSS influencer marketing should be reduced