Economics of Social Media Fake Accounts

• 成果类型: Article; Early Access
• 署名作者: Huang, Zihong; Liu, De
• 署名单位: Texas Tech University System; Texas Tech University; University of Minnesota System; University of Minnesota Twin Cities
• 刊物名称: MANAGEMENT SCIENCE
• ISSN/ISSBN: 0025-1909
• DOI: 10.1287/mnsc.2022.02616
• 发表日期: 2025
• 关键词: influencer economy; fake accounts; Social media; signaling; social media literacy

摘要

Amid the rise of the influencer economy, fake social media accounts have become prevalent on many social media platforms. Yet the problem of fake accounts is still poorly understood, and so is the effectiveness of coping strategies. This research models the ecosystem of fake accounts in an influencer economy and obtains insights on fake account purchasing behaviors, the impact of antifake efforts, and the roles of various contextual factors. We show that as the antifake effort increases, the equilibrium may transition from a pooling equilibrium, where a low-quality influencer buys fake accounts to mimic a high-quality one, to a costly separating equilibrium, where a high-quality influencer may buy fake accounts to prevent mimicry from a low-quality influencer, and to a naturally separating equilibrium where low- and high-quality influencers are separated without buying fake accounts. We find that increasing antifake efforts and increasing social media literacy may sometimes result in more fake accounts. A purely profit-driven platform always prefers a pooling equilibrium with zero antifake effort. As a platform puts more weight on consumer welfare, it may exert a positive effort to induce a separating equilibrium, but the platform's preferred antifake effort tends to be lower than that of consumers. We also find that the platform sometimes prefers a lower social media literacy and a lower fake account base price, whereas consumers prefer the opposite. In contrast, improving the antifake technology level can benefit both the platform and consumers. Our main insights are applicable to scenarios with more influencer types and repeated interactions.