With the widespread usage of social media, there has been much effort on detecting influential users for different information propagation applications. An inherent limitation of such methods is that they can only detect influential users after such users show observable signals of influence. However, in many real world applications including a countercampaign, an organization needs a way to detect influencers early, so that they can take appropriate measure before it is too late to intervene. In this work, we present a method to detect such would-be influencers from their prior word usage in social media. We compute psycholinguistic category scores from word usage, and investigate how people with different scores exhibited different influence behaviors on Twitter. We also found psycholinguistic categories that show significant correlations with such behaviors, and built predictive models of influence from such category based features. Our experiments using a real world dataset validates that such predictions can be done with reasonable accuracy.