推特的职业代表性

S. Kim, Stephen Wan, Cécile Paris
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引用次数: 3

摘要

本文描述了一种方法来检测一个特定的人口统计特征,职业(或专业)在Twitter用户配置文件。在本文中,我们通过将澳大利亚Twitter的职业人口统计数据与2011年澳大利亚人口普查数据中获得的现实世界人口相关联,展示了该方法对估计澳大利亚Twitter的职业人口统计数据的有效性。我们还证明,如果将非标准职业名称映射为标准职业名称,我们可以在Twitter的职业代表性背景下获得更可靠的社交媒体见解。据我们所知,这是第一次尝试建立一个机器学习模型,自动识别Twitter上语言嘈杂或开放式的职业,从而产生更可靠的职业人群。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Occupational Representativeness in Twitter
This paper describes an approach to detect one particular demographic characteristic, occupation (or profession) in Twitter user profiles. In this paper, we show how effective the approach is for estimating occupational population statistics in Australian Twitter by correlating them with real-world population obtained from 2011 Australian census data. We also demonstrate that we can gain more reliable social media insights in the context of occupational representativeness in Twitter if a non-standard occupation name is mapped into a standard occupation name. To our knowledge, this is the first attempt to build a machine learning model that automatically identifies linguistically noisy or open-ended occupations in Twitter, resulting in more reliable occupational population.
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