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Social Media Analyses for Social Measurement
Michael F Schober1, Josh Pasek1, Lauren Guggenheim1
1M ichael F. S chober is a professor of psychology at The New School for Social Research, New York, NY, USA, and Associate Provost for Research at The New School. J osh P asek is an assistant professor of Communication Studies and faculty associate in the Center for Political Studies, Institute for Social Research, at the University of Michigan, Ann Arbor, MI, USA. L auren G uggenheim is a senior research specialist in the Center for Political Studies, Institute for Social Research, at the University of Michigan, Ann Arbor, MI, USA. C liff L ampe is an associate professor in the School of Information at the University of Michigan, Ann Arbor, MI, USA. F rederick G. C onrad is a research professor in the Survey Research Center, Institute for Social Research, and director of the Michigan Program in Survey Methodology at the University of Michigan, Ann Arbor, MI, USA, and research professor and director of the Joint Program in Survey Methodology at the University of Maryland, College Park, MD, USA. This work was supported by the M-Cubed Fund at the University of Michigan [grant to J.P., C.L., and F.G.C.]; and The New School for Social Research funding [to M.F.S.].
Abstract:
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.
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