OranSim simulates social media marketing to predict campaign results
OranSim: Simulating Social Media Marketing
Social and Information Networks
Summary
Social media marketing works by showing content to certain people and seeing how they react and share it. The authors developed OranSim, a tool that simulates how different marketing actions like budget and target choices affect who sees the content and how it spreads. Their simulation divides a population into segments and models responses over time to compare marketing campaigns. They tested it using real historical data and synthetic examples to show how it predicts reach and engagement based on campaign settings. OranSim helps marketers choose strategies before spending money on real campaigns.
What this means in practice
- •For social media marketers: Design and compare marketing campaigns by simulating their reach and engagement before launching.
- •For digital advertising platforms: Improve targeting algorithms by evaluating campaign effects on simulated diverse user segments.
Authors
Jianxiang Ma, Mingfu Zhang, Xiaocui Yang, Yichen Gao, Junzhao Huang, Yuesong Hou
Abstract
Social simulation studies how individual behavior and social interaction produce collective outcomes. In social media marketing, campaign actions shape which consumers encounter the content and how they respond; these responses then spread through the population. We propose OranSim, a social simulation framework that connects creative, creator, targeting, and budget choices to this process. Heterogeneous consumers receive exposure according to content matching and platform allocation and generate initial responses, which propagate among 60 population segments. Candidate campaigns share the initial population and aligned random numbers, making their response trajectories comparable under action changes. In a controlled synthetic campaign, doubling the budget approximately doubles reach while lowering mean content match and engagement probability among the reached consumers; mean 14-day cumulative simulated response mass rises to 1.96 times the baseline. LightGBM predictors fitted to 39,000 historical RedNote notes estimate platform engagement with log-scale $R^2$ of 0.56--0.62 in five-fold cross-validation; a separate 12,154-note corpus supplies temporal, unseen-creator, and held-out-niche test splits. Public-data experiments evaluate policy value and audience ranking, and paired synthetic outcomes test counterfactual scoring. Together, scenario trajectories and engagement estimates support campaign selection according to a prespecified marketing objective. Code is available at https://github.com/OranAi-Ltd/oransim.