InstaSynth: Opportunities and Challenges in Generating Synthetic Instagram Data with ChatGPT for Sponsored Content Detection

Large Language Models (LLMs) raise concerns about lowering the cost of generating texts that could be used for unethical or illegal purposes, especially on social media. This paper investigates the promise of such models to help enforce legal requirements related to the disclosure of sponsored content online. We investigate the use of LLMs for generating synthetic Instagram captions with two objectives: The first objective (fidelity) is to produce realistic synthetic datasets. For this, we implement content-level and network-level metrics to assess whether synthetic captions are realistic. The second objective (utility) is to create synthetic data that is useful for sponsored content detection. For this, we evaluate the effectiveness of the generated synthetic data for training classifiers to identify undisclosed advertisements on Instagram. Our investigations show that the objectives of fidelity and utility may conflict and that prompt engineering is a useful but insufficient strategy. Additionally, we find that while individual synthetic posts may appear realistic, collectively they lack diversity, topic connectivity, and realistic user interaction patterns.

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.

The Case for a Legal Compliance API for the Enforcement of the EU's Digital Services Act on Social Media Platforms

In the course of under a year, the European Commission has launched some of the most important regulatory proposals to date on platform governance. The Commission's goals behind cross-sectoral regulation of this sort include the protection of markets and democracies alike. While all these acts propose sophisticated rules for setting up new enforcement institutions and procedures, one aspect remains highly unclear: how digital enforcement will actually take place in practice. Focusing on the Digital Services Act (DSA), this discussion paper critically addresses issues around social media data access for the purpose of digital enforcement and proposes the use of a legal compliance application programming interface (API) as a means to facilitate compliance with the DSA and complementary European and national regulation. To contextualize this discussion, the paper pursues two scenarios that exemplify the harms arising out of content monetization affecting a particularly vulnerable category of social media users: children. The two scenarios are used to further reflect upon essential issues surrounding data access and legal compliance with the DSA and further applicable legal standards in the field of labour and consumer law.