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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.].
Social media data mining can supplement survey research, but its trustworthiness for replacing official statistics is unknown. Differences in participant understanding, data nature, and ethics require further scholarly discussion.
Area of Science:
- Social Sciences
- Data Science
- Computational Social Science
Background:
- Social media content analysis shows alignment with sample surveys, prompting exploration of data mining as a survey research supplement or replacement.
- The trustworthiness of social media data for replacing official statistics remains unestablished, necessitating critical evaluation.
Purpose of the Study:
- To explore the potential of social media data mining to supplement or replace traditional survey research.
- To identify and analyze the differences in assumptions and methodologies between survey researchers and data scientists.
- To foster interdisciplinary dialogue on the alignment and non-alignment of survey and social media data approaches.
Main Methods:
- Comparative analysis of survey research methodologies and social media data mining techniques.
- Examination of differences in participant understanding of data-generating activities (surveys vs. social media posting).
- Evaluation of data characteristics, legitimate inferences, and practical/ethical considerations for both data types.
Main Results:
- Significant differences exist in participant perceptions, data nature, inferential legitimacy, and ethical considerations between survey respondents and social media posters.
- The degree of alignment between survey estimates and social media data varies based on research topic, population, platform features, and analytical techniques.
- Social media content may predict social phenomena if it effectively summarizes broader conversations also captured by surveys, potentially reducing the need for traditional population coverage.
Conclusions:
- While social media data offers a less costly alternative, its reliability for replacing surveys or official statistics requires careful, context-dependent assessment.
- Interdisciplinary collaboration is crucial to navigate the methodological, ethical, and interpretational challenges of integrating social media data with survey research.
- Future research should focus on understanding the conditions under which social media data can provide valid and reliable insights comparable to traditional survey methods.
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