This letter studies how opinions and subsequent actions of groups of individuals are shaped by opinion leaders, nowadays denoted influencers. We model an influencer as a pinner that exerts a control input on a small subset of individuals, and leverages the interaction network to affect the action of a large fraction of individuals. We provide sufficient conditions so that a given agent takes the same action as the pinner. Based on these conditions, we design a heuristic for the pinned node selection that maximizes the number of nodes taking the action elected by the pinner. The performance of the heuristic is then numerically tested against standard pinning strategies.