跨尺度变化：Twitter数据采样下的测量保真度
Variation across Scales: Measurement Fidelity under Twitter Data Sampling
摘要：
对数据质量的全面理解是社交媒体研究中测量研究的基石。本文介绍了在不同的时间尺度和不同的主题（实体，网络和级联）的Twitter数据采样的影响进行深入的测量。通过构建完整的推文流，我们表明，Twitter的速率限制消息是一个准确的指标，失踪的推文量。取样在不同的时间尺度上也有很大的不同。虽然每小时采样率受不同时区的昼夜节律的影响，但毫秒级采样受实现选择的严重影响。对于Twitter用户等实体，我们发现具有均匀速率的伯努利过程很好地逼近了经验分布。它还允许我们使用观察到的样本数据估计真实排名。对于Twitter上的网络，它们的结构发生了显着变化，一些组件更有可能被保留下来。对于转推级联，我们观察到的鸣叫到达时间和用户的影响，这将影响依赖于这些功能的模型分布的变化。这项工作呼吁关注社会数据中的噪音和潜在偏见，并提供了一些工具来衡量Twitter的抽样效果。
英文摘要：
A comprehensive understanding of data quality is the cornerstone of measurement studies in social media research. This paper presents in-depth measurements on the effects of Twitter data sampling across different timescales and different subjects (entities, networks, and cascades). By constructing complete tweet streams, we show that Twitter rate limit message is an accurate indicator for the volume of missing tweets. Sampling also differs significantly across timescales. While the hourly sampling rate is influenced by the diurnal rhythm in different time zones, the millisecond level sampling is heavily affected by the implementation choices. For Twitter entities such as users, we find the Bernoulli process with a uniform rate approximates the empirical distributions well. It also allows us to estimate the true ranking with the observed sample data. For networks on Twitter, their structures are altered significantly and some components are more likely to be preserved. For retweet cascades, we observe changes in distributions of tweet inter-arrival time and user influence, which will affect models that rely on these features. This work calls attention to noises and potential biases in social data, and provides a few tools to measure Twitter sampling effects.
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。
