The Language of Approval: Identifying the Drivers of Positive Feedback Online

Figure 1: Overview of our three-part research approach for studying drivers of positive feedback online. Panel 1 (Identify Causal Effects): We apply a selection-on-observables causal inference framework to 11M posts from 100 subreddits to isolate the causal impact of linguistic attributes on three forms of positive feedback (upvotes, awards, gold) while controlling for confounding factors like author reputation and timing. Panel 2 (Predictive Modeling): We evaluate whether the linguistic features used in our causal analysis can support real-time detection of high-quality posts through both global and local predictive models, testing their ability to surface high quality contributions before community votes fully accrue. Panel 3 (Audit & Contextualize): We systematically compare our empirical findings against prior user and moderator surveys, descriptive studies, and community guidelines through a manual audit to identify where our causal estimates align with, refine, or contradict current understanding of what drives positive reception in online communities. Our work combines causal inference, predictive modeling, and comparative analysis to provide both theoretical insights and practical implications for community design and moderation.