AI 中文总结
研究发现X社区笔记的单一极性轴不足，需二维空间（政治立场与机构信任）才能准确预测评价，并建议采用多轴桥接模型及多语言招募。
AI 中文摘要
社区笔记是X平台的众包事实核查系统。只有当通常意见相左的评价者都认为某条笔记有帮助时，该笔记才会被发布在被其纠正的帖子下方，这一设计被称为“桥接”。为了应用该规则，系统仅从评价中学习谁与谁意见相左，将每位评价者和每条笔记置于一条线上，即极性轴。生产流程中的每个评分器都使用单一轴。在完整公开数据（2.129亿条评价、233万条笔记、107万评价者）上重新拟合这些评分器共享的基础模型后，我们发现单一轴是不够的。该空间至少是二维的。第一个轴是左/右政治立场，而第二个轴（我们将其解释为对机构的信任）在很大程度上独立于第一个轴。一项留出测试证实，第二个轴能改善对未见评价的预测，而第三个轴则增益甚微。从一组主题中学到的第二个评价者维度，能够预测评价者如何评判未纳入拟合的COVID和乌克兰笔记，因此它不仅仅是主题的复述。在政治分歧极小的重度评价笔记中，随着第二轴分歧的增加，已发布比例从71.5%降至11.7%。单轴拟合仅将这些笔记记录为弱极化且帮助性较低；而第二轴一端评价者支持它们的信息则丢失了。作者撰写的笔记与其自身在两个轴上的位置相匹配（r = 0.538和0.358），且少数评价者贡献了大部分评价（基尼系数 = 0.718）。在最小的语言社区中发布的笔记较少，但缺口在于收到的评价数量，而非规则对待它们的方式。在保持每条笔记评价数不变的情况下，只有印地语仍低于全球10.85%的比率，而希腊语从7.76%升至11.68%。我们主张采用具有多个分歧轴的桥接模型，并在当前设计覆盖最少的语言中招募评价者。
英文摘要
Community Notes is X's crowdsourced fact-checking system. A note is published beneath the post it corrects only when raters who usually disagree both rate it helpful, a design called bridging. To apply that rule, the system learns who disagrees with whom from the ratings alone, placing every rater and note on one line, the polarity axis. Every scorer in the production pipeline uses a single axis. Refitting the base model these scorers share on the full public data (212.9M ratings, 2.33M notes, 1.07M raters), we find that one axis is too few. The space is at least two-dimensional. The first axis is left/right politics, while the second, which we interpret as trust in institutions, is largely independent of the first. A held-out test confirms that the second axis improves prediction of unseen ratings, while a third adds little. A second rater dimension learned from one set of topics predicts how raters judge COVID and Ukraine notes excluded from the fit, so it does not merely restate subject matter. Among heavily rated notes that barely divide raters politically, the published share falls from 71.5% to 11.7% as second-axis disagreement grows. A one-axis fit records these notes only as weakly polarised and less helpful; the information that raters at one end of the second axis support them is lost. Authors write notes matching their own position on both axes (r = 0.538 and 0.358), and a small minority of raters cast most ratings (Gini = 0.718). Fewer notes are published in the smallest language communities, but the shortfall is in ratings received, not in how the rule treats them. Keeping ratings per note constant, only Hindi stays below the global rate of 10.85%, and Greek moves from 7.76% to 11.68%. We argue for a bridging model with more than one axis of disagreement, and for recruiting raters in the languages the current design reaches least.
