Community-Aware Influence Maximization for Suppressing Cryptocurrency Scam Misinformation

Cryptocurrency fraud campaigns often rely on large-scale social-media diffusion to recruit victims, normalize false claims, and coordinate multi-level marketing behavior. This study examines the dynamics of the One Coin scam. It proposes an influence-maximization (IM)-driven workflow for identifying high-impact accounts whose intervention can reduce future misinformation diffusion. A directed Twitter engagement network from retweet/reply interactions is constructed and studied, and the accounts that should be prioritized for intervention to reduce the reach of future scam-promoting misinformation are identified. We evaluate six seed selection strategies: Degree, Betweenness, PageRank, k-core, CELF (lazy greedy), and Reverse Influence Sampling (RIS) under the classical Independent Cascade (IC) and Linear Threshold (LT) diffusion models using a weighted-cascade parameterization when ground-truth transmission probabilities are unavailable. Across the tested seed budgets, CELF achieves the highest expected spread, but with the highest computational cost. At the largest seed budget, Degree is effectively tied with CELF (within 0.09% under LT and 1.4% under IC), indicating a hub-dominated engagement structure in which simple reach-based heuristics can be highly competitive. RIS provides a strong quality–efficiency trade-off, remaining within approximately 9.7% (LT) and 9.5% (IC) of CELF while requiring substantially less computation. We further introduce a community-aware variant using Leiden partitions and proportional seed allocation to improve cross-community coverage; at larger budgets, this improves methods sensitive to seed over-concentration, increasing LT spread by about 9.8% for k-core and 8.6% for RIS. Overall, the results quantify practical trade-offs between spread and runtime for deployable suppression workflows and show when community-aware planning better aligns with the heterogeneous structure of scam recruitment ecosystems.