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Badges are a common, and sometimes the only, method of incentivizing users toperform certain actions on online sites. However, due to many competing factorsinfluencing user temporal dynamics, it is difficult to determine whether thebadge had (or will have) the intended effect or not.  In this paper, we introduce two complementary approaches for determiningbadge influence on users. In the first one, we cluster users' temporal traces(represented with point processes) and apply covariates (user features) toregularize results. In the second approach, we first classify users' temporaltraces with a novel statistical framework, and then we refine theclassification results with a semi-supervised clustering of covariates.  Outcomes obtained from an evaluation on synthetic datasets and experiments ontwo badges from a popular Q&A platform confirm that it is possible to validate,characterize and to some extent predict users affected by the badge.

Determining Impact of Social Media Badges through Joint Clustering of Temporal Traces and User Features