Purpose

This paper aims to estimate the population in a specific space from the numbers of posted tweets and their senders, using Twitter's real-time property and location information data.

Design/methodology/approach

The population to be estimated was set to be the attendance at each game among the six baseball teams of the Japan Professional Baseball Pacific League held at the main stadium of each team. The relation between the attendance and Twitter data was analyzed, and regression models using Twitter data were used to estimate the attendances.

Findings

The correlation coefficient tended to be larger for the attendance and tweeting users than for the attendance and that of the number of tweets. Furthermore, the comparison and evaluation of several regression models combining Twitter data, game data and weather data for estimating the attendance showed the usefulness of Twitter data, and that using the number of tweeting users improved the accuracy of population estimation.

Originality/value

While there are many studies on event detection or location identification using Twitter data, no study has been reported on the estimation of the population in a specific space using “time information” and “location information” characteristic of Twitter data. Using Twitter data, which contains users' messages, for estimating the population can be extended to various types of analyses, such as the analysis of feelings and opinions of the groups in the space.

中文翻译：

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使用 Twitter 估计特定空间的人口

目的

本文旨在使用 Twitter 的实时属性和位置信息数据，根据发布的推文数量及其发送者估计特定空间中的人口。

设计/方法/方法

估计的人数是日本职业棒球太平洋联盟的六支棒球队在每支球队的主体育场举行的每场比赛的上座率。分析出勤率与推特数据的关系，利用推特数据回归模型估计出勤率。

发现

出席人数和推文用户的相关系数往往大于出席人数和推文数量的相关系数。此外，结合推特数据、游戏数据和天气数据估计出勤率的几种回归模型的比较和评估显示了推特数据的有用性，利用推特用户数提高了人口估计的准确性。

原创性/价值

虽然有许多使用 Twitter 数据进行事件检测或位置识别的研究，但尚未有关于使用 Twitter 数据的“时间信息”和“位置信息”特征估计特定空间中的人口的研究。使用包含用户消息的 Twitter 数据来估计人口可以扩展到各种类型的分析，例如对空间中群体的感受和意见的分析。