Network preprocessing techniques can simplify combinatorial optimization problems without compromising solution quality. In the context of information diffusion, the Perfect Awareness Problem seeks the smallest set of initial spreaders to ensure complete awareness across a social network. This work introduces two novel reduction strategies for this problem: edge reduction and leaf blindness. The edge reduction strategy identifies and eliminates redundant edges based on domination relationships, while leaf blindness handles terminal nodes that do not contribute to the spreading process. These preprocessing methods are integrated within an Iterated Local Search framework that employs reservoir-based local search and perturbation mechanisms. The proposed reductions significantly decrease instance complexity, enabling more efficient exploration of the solution space across networks of varying sizes and densities. Experimental evaluation on 840 benchmark instances demonstrates the effectiveness of our approach, achieving 809 best-known values and discovering 33 new best solutions.