Online platforms rely on moderation interventions to curb harmful behavior such hate speech, toxicity, and the spread of mis- and disinformation. Yet research on the effects and possible biases of such interventions faces multiple limitations. For example, existing works frequently focus on single or a few interventions, due to the absence of comprehensive datasets. As a result, researchers must typically collect the necessary data for each new study, which limits opportunities for systematic comparisons. To overcome these challenges, we introduce The Big Ban Theory (TBBT), a large dataset of moderation interventions. TBBT covers 25 interventions of varying type, severity, and scope, comprising in total over 339K users and nearly 39M posted messages. For each intervention, we provide standardized metadata and pseudonymized user activity collected three months before and after its enforcement, enabling consistent and comparable analyses of intervention effects. In addition, we provide a descriptive exploratory analysis of the dataset, along with several use cases of how it can support research on content moderation. With this dataset, we aim to support researchers studying the effects of moderation interventions and to promote more systematic, reproducible, and comparable research. TBBT is publicly available at: https://doi.org/10.5281/zenodo.18245670.

翻译：在线平台依赖审核干预来遏制有害行为，如仇恨言论、毒性言论以及错误与虚假信息的传播。然而，关于此类干预效果及潜在偏见的研究面临多重局限。例如，由于缺乏综合性数据集，现有研究通常仅关注单一或少数几种干预措施。因此，研究人员往往需要为每项新研究单独收集必要数据，这限制了对干预措施进行系统性比较的机会。为克服这些挑战，我们提出了大禁令理论（TBBT），一个大规模的审核干预数据集。TBBT涵盖25种类型、严重程度和范围各异的干预措施，总计包含超过33.9万用户及近3900万条发布消息。针对每项干预，我们提供了标准化的元数据以及在干预执行前后三个月内收集的匿名化用户活动数据，从而支持对干预效果进行一致且可比较的分析。此外，我们对数据集进行了描述性探索分析，并展示了若干应用案例，说明其如何支持内容审核研究。通过该数据集，我们旨在支持研究审核干预效果的研究人员，并推动更系统化、可复现且可比较的研究。TBBT数据集公开发布于：https://doi.org/10.5281/zenodo.18245670。