Social & Environmental Data Analysis Lab, Department of Computer Science, University of Exeter, Exeter, United Kingdom.
PLoS One. 2019 Jul 24;14(7):e0218454. doi: 10.1371/journal.pone.0218454. eCollection 2019.
Twitter has become an important platform for geo-spatial analyses, providing high-volume spatial data on a wide variety of social processes. Understanding the relationship between population density and Twitter activity is therefore of key importance. This study reports a systematic relationship between population density and Twitter use. Number of tweets, number of users and population per unit area are related by power law functions with exponents greater than one. These relations are consistent with each other and hold across a range of spatial scales. This implies that population density can accurately predict Twitter activity, but importantly, it also implies that correct predictions are not given by a naive linear scaling analysis. The observed super-linearity has implications for any spatial analyses performed with Twitter data and is important for understanding the relationship between Twitter use and demographics. For example, the robustness of this relationship means that we can identify 'anomalous' geographic areas that deviate from the observed trend, identifying several towns with high/low usage relative to expectation; using the scaling relationship we are able to show that these anomalies are not caused by age structure, as has been previously proposed. Proper consideration of this scaling relationship will improve robustness in future geo-spatial studies using Twitter.
推特已成为地理空间分析的重要平台，提供了各种社会进程的大容量空间数据。因此，了解人口密度与推特活跃度之间的关系至关重要。本研究报告了人口密度与推特使用之间的系统关系。推文数量、用户数量和单位面积人口之间的关系呈幂律函数，指数大于 1。这些关系相互一致，并在各种空间尺度上成立。这意味着人口密度可以准确预测推特活动，但重要的是，它还意味着简单的线性缩放分析并不能给出正确的预测。观察到的超线性关系对任何使用推特数据进行的空间分析都有影响，对于理解推特使用与人口统计学之间的关系也很重要。例如，这种关系的稳健性意味着我们可以识别偏离观察趋势的“异常”地理区域，确定几个使用量相对于预期较高或较低的城镇；使用缩放关系，我们能够表明这些异常不是由之前提出的年龄结构引起的。在未来使用推特进行地理空间研究时，适当考虑这种缩放关系将提高稳健性。
