中文摘要
随着网红经济的兴起,虚假社交媒体账号在许多社交平台上变得普遍。然而,虚假账号问题仍缺乏深入理解,应对策略的有效性亦是如此。本研究对网红经济中虚假账号的生态系统进行建模,获得关于虚假账号购买行为、反虚假努力的影响以及各类情境因素作用的洞见。我们表明,随着反虚假努力的提高,均衡可能从“混同”均衡——低质量网红购买虚假账号以模仿高质量网红——转变为“有成本分离”均衡——高质量网红可能购买虚假账号以防止被低质量网红模仿——再转变为“自然分离”均衡,即低质量与高质量网红无需购买虚假账号即可实现分离。我们发现,加强反虚假努力和提高社交媒体素养有时反而会导致更多虚假账号。纯粹以利润为导向的平台总是偏好零反虚假努力的混同均衡。当平台赋予消费者福利更大权重时,它可能付出正的努力以引致分离均衡,但平台偏好的反虚假努力水平往往低于消费者所偏好的水平。我们还发现,平台有时偏好较低的社交媒体素养和较低的虚假账号基础价格,而消费者则偏好相反。相比之下,提升反虚假技术水平可以同时使平台和消费者受益。我们的主要洞见适用于存在更多网红类型和重复互动的情形。
Abstract
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.