# Characterising and Detecting Sponsored Influencer Posts on Instagram

arXiv: https://arxiv.org/abs/2011.05757 · HTML: https://mlsanity.com/paper/2011.05757/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Nov 2020.

## Built on by (most cited first, 7 of 7)

- [Closing the Loop: Testing ChatGPT to Generate Model Explanations to Improve Human Labelling of Sponsored Content on Social Media](https://mlsanity.com/paper/2306.05115/) (2023)
- [Across Platforms and Languages: Dutch Influencers and Legal Disclosures on Instagram, YouTube and TikTok](https://mlsanity.com/paper/2407.12451/) (2024)
- [InstaSynth: Opportunities and Challenges in Generating Synthetic Instagram Data with ChatGPT for Sponsored Content Detection](https://mlsanity.com/paper/2403.15214/) (2024)
- [Evaluating LLM-Generated Legal Explanations for Regulatory Compliance in Social Media Influencer Marketing](https://mlsanity.com/paper/2510.08111/) (2025)
- [A Multimodal Analysis of Influencer Content on Twitter](https://mlsanity.com/paper/2309.03064/) (2023)
- [Computational Studies in Influencer Marketing: A Systematic Literature Review](https://mlsanity.com/paper/2506.14602/) (2025)
- [N"urnberg NLP at ChildSafeAds 2026: Structurally Dissimilar Voter Ensembles under Four Levels of Data Access](https://mlsanity.com/paper/2609.34986/) (2026)

## Abstract (arXiv)

Recent years have seen a new form of advertisement campaigns emerge: those involving so-called social media influencers. These influencers accept money in return for promoting products via their social media feeds. Although this constitutes a new and interesting form of marketing, it also raises many questions, particularly related to transparency and regulation. For example, it can sometimes be unclear which accounts are officially influencers, or what even constitutes an influencer/advert. This is important in order to establish the integrity of influencers and to ensure compliance with advertisement regulation. We gather a large-scale Instagram dataset covering thousands of accounts advertising products, and create a categorisation based on the number of users they reach. We then provide a detailed analysis of the types of products being advertised by these accounts, their potential reach, and the engagement they receive from their followers. Based on our findings, we train machine learning models to distinguish sponsored content from non-sponsored, and identify cases where people are generating sponsored posts without officially labelling them. Our findings provide a first step towards understanding the under-studied space of online influencers that could be useful for researchers, marketers and policymakers.