发表机构
OranAI; Northeastern University; OranAI Ltd.（奥兰智能; 东北大学; 奥兰人工智能有限公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出OranSim社会模拟框架，连接创意、定向和预算选择与消费者回应传播，支持根据营销目标进行广告活动选择。
AI 中文摘要
社会模拟研究个体行为和社会互动如何产生集体结果。在社交媒体营销中，广告活动行为决定了哪些消费者会接触到内容以及他们如何回应；这些回应随后在人群中传播。我们提出了OranSim，一个将创意、创作者、定向和预算选择连接到这一过程的社会模拟框架。异质消费者根据内容匹配和平台分配获得曝光，并产生初始回应，这些回应在60个人口细分群体中传播。候选广告活动共享初始人口和一致的随机数，使得它们在行动变化下的回应轨迹具有可比性。在一个受控的合成广告活动中，将预算翻倍大约使触达人数翻倍，同时降低了被触达消费者中的平均内容匹配度和参与概率；平均14天累积模拟回应量上升至基线的1.96倍。基于39,000条历史小红书笔记拟合的LightGBM预测器在五折交叉验证中预测平台参与度的对数尺度$R^2$为0.56--0.62；一个单独的12,154条笔记语料库提供了时间、未见创作者和保留利基测试分割。公共数据实验评估了策略价值和受众排名，配对合成结果测试了反事实评分。总之，场景轨迹和参与度估计支持根据预设营销目标进行广告活动选择。代码可在https://github.com/OranAi-Ltd/oransim获取。
英文摘要
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 estimation 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.
Comments26 pages, 5 figures. Updated title and revised manuscript. Code: https://github.com/OranAi-Ltd/oransim
