在线不信任生态系统的复杂性及其演化
Complexity of the Online Distrust Ecosystem and its Evolution
AI总结：
本文通过分析 2019 至 2023 年约 1 亿用户的 Facebook 疫苗不信任网络，揭示在线不信任生态系统在平台缓解措施后能快速自我修复，表明 Facebook 疫情期间干预无效，并指出未来干预需跨主题、跨地理尺度设计。
摘要：
集体性人类不信任（及其相关的错误信息/虚假信息）是我们这个时代最复杂的现象之一，例如对医学专业知识、气候变化科学、民主选举结果的不信任，甚至对当前以色列-哈马斯和乌克兰-俄罗斯冲突中经事实核查事件的不信任。那么，是什么让在线不信任生态系统如此具有韧性？它在疫情期间及之后是如何演化的？Facebook 的缓解政策在此期间的效果如何？我们分析了一个由相互关联的内建社区（Facebook 页面）组成的 Facebook 网络，该网络总计约 1 亿用户，在疫情前仅聚焦于对疫苗的不信任。通过绘制这一从 2019 年到 2023 年的动态网络，我们表明，在 Facebook 包括关停在内的缓解行动之后，该网络迅速实现了自我修复。这证实并扩展了我们此前的发现，即 Facebook 在新冠疫情期间的强化措施（例如 2020 年 11 月）是无效的。我们的研究结果表明，未来的干预措施必须选择能够跨越多个主题和多个地理尺度产生共鸣的方案。与许多近期研究不同，我们的发现不依赖第三方黑箱工具——这些工具在严谨科学研究中的准确性尚未得到证实，因而令人对此类研究的结论产生怀疑；我们的网络也并非基于转瞬即逝的超链接提及构建，这类提及的相关性存疑。
英文摘要：
Collective human distrust (and its associated mis-disinformation) is one of the most complex phenomena of our time. e.g. distrust of medical expertise, or climate change science, or democratic election outcomes, and even distrust of fact-checked events in the current Israel-Hamas and Ukraine-Russia conflicts. So what makes the online distrust ecosystem so resilient? How has it evolved during and since the pandemic? And how well have Facebook mitigation policies worked during this time period? We analyze a Facebook network of interconnected in-built communities (Facebook pages) totaling roughly 100 million users who pre-pandemic were just focused on distrust of vaccines. Mapping out this dynamical network from 2019 to 2023, we show that it has quickly self-healed in the wake of Facebook's mitigation campaigns which include shutdowns. This confirms and extends our earlier finding that Facebook's ramp-ups during COVID were ineffective (e.g. November 2020). Our findings show that future interventions must be chosen to resonate across multiple topics and across multiple geographical scales. Unlike many recent studies, our findings do not rely on third-party black-box tools whose accuracy for rigorous scientific research is unproven, hence raising doubts about such studies' conclusions, nor is our network built using fleeting hyperlink mentions which have questionable relevance.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
