Social Media Analyses for Social Measurement.
Social Media Analyses for Social Measurement.
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用于社会衡量的社交媒体分析。
作者：
M. F. Schober;Josh Pasek;Lauren Guggenheim;Cliff Lampe;F. Conrad
对社交媒体内容的分析可以与抽样调查的测量相一致，这提出了一个问题，即调查研究是否可以用对已经存在或“发现”的社交媒体内容进行成本更低、负担更少的数据挖掘来补充甚至取代。但是，这种测量方法究竟有多可靠——比如说，取代官方统计数据——还不得而知。调查研究人员和数据科学家从不同的假设和分析传统开始处理关键问题，例如，需要从完全覆盖人口的框架中提取代表性样本。需要在这些学术团体之间进行新的对话，以了解结盟和不结盟的潜在点。在这些方法中，主要存在以下差异：(a)参与者（调查受访者和社交媒体海报）如何理解他们所从事的活动；(b)调查回复和社交媒体帖子产生的数据的性质，以及基于数据的合法推论；(c)围绕数据使用的实际和道德考虑。根据研究主题和所考虑的人群，调查和所涉及的社交媒体网站的特定特征，以及从社交媒体中提取意见和经验的分析技术，估计可能会在不同程度上保持一致。社交媒体内容可能不需要传统的人口覆盖率来有效地预测社会现象，因为社交媒体内容提炼或总结了更广泛的对话，这些对话也可以通过调查来衡量。
Demonstrations that analyses of social media content can align with measurement from sample surveys have raised the question of whether survey research can be supplemented or even replaced with less costly and burdensome data mining of already-existing or "found" social media content. But just how trustworthy such measurement can be-say, to replace official statistics-is unknown. Survey researchers and data scientists approach key questions from starting assumptions and analytic traditions that differ on, for example, the need for representative samples drawn from frames that fully cover the population. New conversations between these scholarly communities are needed to understand the potential points of alignment and non-alignment. Across these approaches, there are major differences in (a) how participants (survey respondents and social media posters) understand the activity they are engaged in; (b) the nature of the data produced by survey responses and social media posts, and the inferences that are legitimate given the data; and (c) practical and ethical considerations surrounding the use of the data. Estimates are likely to align to differing degrees depending on the research topic and the populations under consideration, the particular features of the surveys and social media sites involved, and the analytic techniques for extracting opinions and experiences from social media. Traditional population coverage may not be required for social media content to effectively predict social phenomena to the extent that social media content distills or summarizes broader conversations that are also measured by surveys.