Understanding which posts spark conversation, and how large those conversations grow, is vital for moderation, resource allocation, and anticipating information cascades on Reddit and other social platforms. We study discussion initiation and growth on Reddit by modelling whether a root post receives any comments and how large the resulting thread becomes. Using reconstructed threads from r/politics, r/CryptoCurrency, and r/Conspiracy, we extract compact textual, semantic, temporal, domain, and author features from each post. We train subreddit-specific classifiers with small, transparent feature sets and use SHAP for interpretation. Across communities, the external domain a post links to, and, in news ecosystems, the domain’s centrality, consistently emerge as predictors of both the start and scale of discussion. Author activity is also predictive: posts from highly active users are more likely to receive comments. Simple textual cues help too: longer subjects and fewer question marks are associated with a higher likelihood of eliciting replies. Community context moderates these effects: in r/politics, linking familiar mid-tier but well-connected news sources is associated with larger threads, while the r/Conspiracy and r/CryptoCurrency communities prefer novel sources. Predicting whether a discussion will start is notably easier than forecasting its eventual size, as adjacent size classes are often confounded. Still, a concise, interpretable feature set captures a substantial proportion of the predictive signal. Our results suggest practical applications for triage: flagging posts likely to trigger substantial discussion could support targeted, pre-emptive moderation and fact-checking without relying on complex, opaque models.
Lynch et al. (Thu,) studied this question.