Community managers often struggle to ensure the viability of innovation communities (IC) due to their big data characteristics and inferior member participation, which result in minimal activity and low-quality input. In response to a recent call in the innovation literature for new approaches to dealing with the challenges of big data, we propose an IC-management strategy that relies on extracting linguistic-style cues from community posts to identify future inferior member participation. When future destructive IC behavior is signaled, the moderator can effectively select the correct member for corrective treatment to prevent negative community impact. This article uses text mining to extract self-interest-oriented and positive emotional writing style cues from 39,387 posts written by 1611 members of 10 ICs. Two multilevel regression models deliver novel insights into the relationship between these linguistic cues and the likelihood of inferior community participation (quantity and quality). First, a community member's use of a positive emotional writing style signals less inferior participation quantity and quality in the future. Second, a moderator's use of a self-interest-oriented writing style suggests more inferior participation quality, while a self-interest-oriented community indicates less inferior participation quality. Third, community managers should work to build a positive-emotion-driven community, as such communities experience constructive member participation. This article shows that community managers who struggle with their IC must realize that in addition to what people say, how they say it gives insights into the IC's viability. We conclude our study by revealing the theoretical and managerial implications for IC management and community moderators.