
arXiv: 2405.10231
Social media influencers account for a growing share of marketing worldwide. We demonstrate the existence of a novel form of market failure in this advertising market: influencer cartels, where groups of influencers collude to increase their advertising revenue by inflating their engagement. Our theoretical model shows that influencer cartels can improve consumer welfare if they expand social media engagement to the target audience, or reduce welfare if they divert engagement to less relevant audiences. Drawing on the model's insights, we empirically examine influencer cartels using novel datasets and machine learning tools, and derive policy implications.
FOS: Economics and business, FOS: Computer and information sciences, Computer Science - Computers and Society, Computer Science - Machine Learning, General Economics (econ.GN), Computers and Society (cs.CY), Economics - General Economics, Machine Learning (cs.LG)
FOS: Economics and business, FOS: Computer and information sciences, Computer Science - Computers and Society, Computer Science - Machine Learning, General Economics (econ.GN), Computers and Society (cs.CY), Economics - General Economics, Machine Learning (cs.LG)
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 2 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
