Transparent Tagging for Strategic Social Nudges on User-Generated Misinformation

Social network platforms (SNP), such as X and TikTok, rely heavily on user-generated content to attract users and advertisers, yet they have limited control over content provision, which leads to the proliferation of misinformation across platforms. As countermeasures, SNPs have implemented various policies, such as tweet labeling, to notify users about potentially misleading information, influencing users' responses, either favorably or unfavorably, to the tagged contents. The population-level response creates a social nudge to the content provider that encourages it to supply more authentic content without exerting direct control over the provider. Yet, when designing such tagging policies to leverage social nudges, SNP must be cautious about the potential misdetection of misinformation (wrongly detecting factual content as misinformation and vice versa), which impairs its credibility to generic users and, hence, its ability to create social nudges. This work establishes a Bayesian persuaded branching process to study SNP's tagging policy design under misdetection. Misinformation circulation is modeled by a multi-type branching process, where users are persuaded through tagging to give positive and negative comments that influence the spread of misinformation. When translated into posterior belief space, the SNP's problem is reduced to an equality-constrained convex optimization, the optimal condition of which is given by the Lagrangian characterization. The key finding is that SNP's optimal policy is simply transparent tagging, i.e., revealing the content's authenticity to the user, albeit midsection, which nudges the provider not to generate misinformation. We corroborate our findings using numerical simulations.