OranSim: Simulating Consumer Response to Social Media Campaigns Before Launch

A social media campaign must be chosen before its population response is observed. Creative, creator, targeting, and budget choices shape who encounters the content and how they respond; propagation turns these initial responses into population dynamics. We propose OranSim, a social simulation framework for social media marketing. It maps marketing actions to content matching, exposure allocation, and reach, computes individual responses, and propagates them among 60 population segments. Candidate scenarios share the initial population and random numbers to compare responses to action changes. In a controlled synthetic case, doubling the budget roughly doubles reach, lowers mean content match and engagement probability, and raises the mean 14-day cumulative simulated response mass to 1.96 times the baseline. For platform engagement objectives, predictors trained on historical notes estimate read, like, collect (save), and comment counts. On 39,000 private RedNote notes, they achieve log-scale $R^2$ of 0.56–0.62 in five-fold cross-validation; a separate set of 12,154 notes tests temporal, unseen-creator, and leave-one-niche-out prediction. Under a collect-first rule, predicted collects select the creative and simulated response selects the budget. Experiments on KuaiRand-Pure, X5 RetailHero, and the Open Bandit Dataset evaluate policy value and audience ranking under their assignment mechanisms, and paired synthetic outcomes test counterfactual scoring. By tracing how actions shape exposure, individual response, and propagation, OranSim supports prelaunch comparison and selection of campaigns for specific objectives.