Who tweets? Deriving the demographic characteristics of age, occupation and social class from twitter user meta-data.
Who tweets? Deriving the demographic characteristics of age, occupation and social class from twitter user meta-data.
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DOI：
10.1371/journal.pone.0115545
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发表时间：
2015
期刊：
影响因子：
3.7
通讯作者：
Williams M
中科院分区：
文献类型：
--
作者：
Sloan L;Morgan J;Burnap P;Williams M
本文指定，设计和批判性地评估两个工具，用于自动识别人口数据（年龄，职业和社会阶层）从推特用户的个人资料描述在英国（英国）。通过协作社会媒体观察站（COSMOS: http://www.cosmosproject.net/）定期收集的与英国Twitter用户相关的元数据数据与国家统计局（ONS）使用SOC2010提供的工作和社会阶层之间的职业查找表相匹配。使用专家验证，自动化匹配过程的有效性和可靠性进行了严格评估，并提供了2011年人口普查基线比较的英国Twitter用户的预期班级分布。在讨论了如何最大限度地减少误报后，解释和制定了识别年龄的模式匹配规则。使用该工具确定的Twitter用户的年龄分布与2011年人口普查中英国人口的年龄分布一起呈现。自动化职业检测工具可靠地识别某些职业群体，例如专业人员，其职称不能与爱好混淆，或者在替代上下文中以常用的说法使用。另一种解释是，与2011年的人口普查数据相比，Twitter上的创意行业代表人数过多。根据2011年人口普查的数据，年龄检测工具显示了Twitter用户与英国总人口相比的年轻程度，但预测表明，仍有大量潜在的老年平台用户。从Twitter元数据中检测到职业和年龄的“特征”是可能的，准确度不同（尤其依赖于职业群体），但需要进一步的确认工作。
This paper specifies, designs and critically evaluates two tools for the automated identification of demographic data (age, occupation and social class) from the profile descriptions of Twitter users in the United Kingdom (UK). Meta-data data routinely collected through the Collaborative Social Media Observatory (COSMOS: http://www.cosmosproject.net/) relating to UK Twitter users is matched with the occupational lookup tables between job and social class provided by the Office for National Statistics (ONS) using SOC2010. Using expert human validation, the validity and reliability of the automated matching process is critically assessed and a prospective class distribution of UK Twitter users is offered with 2011 Census baseline comparisons. The pattern matching rules for identifying age are explained and enacted following a discussion on how to minimise false positives. The age distribution of Twitter users, as identified using the tool, is presented alongside the age distribution of the UK population from the 2011 Census. The automated occupation detection tool reliably identifies certain occupational groups, such as professionals, for which job titles cannot be confused with hobbies or are used in common parlance within alternative contexts. An alternative explanation on the prevalence of hobbies is that the creative sector is overrepresented on Twitter compared to 2011 Census data. The age detection tool illustrates the youthfulness of Twitter users compared to the general UK population as of the 2011 Census according to proportions, but projections demonstrate that there is still potentially a large number of older platform users. It is possible to detect “signatures” of both occupation and age from Twitter meta-data with varying degrees of accuracy (particularly dependent on occupational groups) but further confirmatory work is needed.
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