AI 中文总结
该研究针对Twitter数值型用户元数据，引入时间可移植性视角，用2020-2022年季度数据评估其跨时间复用的属性保留情况，发现特征保留度随时间下降，需按需评估时间可移植性。
AI 中文摘要
社交媒体中的数值型用户元数据常随时间被重复使用，但其可复用性可能取决于分析需要保留的内容。本文引入时间可移植性作为评估用户特征及基于特征的规则跨时间复用的分析视角，具体探究在源时间点定义的特征与规则被复用至目标时间点时，相关属性的保留程度。研究使用2020年第一季度至2022年第三季度Twitter 1%样本流中日语推文直接获取或衍生的用户特征季度数据，每季度包含约1010万至1100万唯一用户，从特征分布、同用户相对排名、选择率、选中用户成员身份四个维度评估13个数值型用户特征。结果显示，各季度间特征分布发生变化，多数特征的同用户相对排名在季度间隔更长时保留度更低；复用源季度阈值会产生选择率漂移，目标季度重新校准后选择率几乎与源季度匹配，但成员身份更替仍存在且随季度间隔延长而增加。研究表明，时间可移植性应根据分析所需保留的属性进行评估。
英文摘要
Numeric user metadata in social media are often reused over time. However, their reusability may depend on what an analysis needs to preserve. We introduce temporal portability as an analytical perspective for assessing the cross-time reuse of user features and feature-based rules. Specifically, we ask how well relevant properties are preserved when features and rules defined at a source time point are reused at a target time point. We used quarterly data on user features obtained directly from or derived from Japanese-language tweets in Twitter's 1% sample stream from 2020-Q1 to 2022-Q3. Each quarter included approximately 10.1--11.0 million unique users. We evaluated 13 numeric user features in terms of feature distributions, same-user relative ranks, selection rates, and selected-user membership. Across quarters, feature distributions changed and, for many features, same-user relative ranks were less well preserved at longer quarter lags. Reusing source-quarter thresholds also produced selection-rate drift. Target-quarter recalibration nearly matched source-quarter selection rates. However, membership turnover persisted and increased at longer quarter lags. Our results show that temporal portability should be assessed in terms of the property that an analysis needs to preserve.
Comments12 pages, 6 figures
