A Mixed-Methods Approach to Ethical Data Extraction in Social Media Studies


Across social media platforms users (sub)consciously represent themselves in a way that is appropriate for their intended audience. This has unknown impacts on studies with unobtrusive designs based on digital (social) platforms, and studies of contemporary social phenomena in online settings. A lack of appropriate methods to identify, control for, and mitigate the effects of self-representation, the propensity to express socially responding characteristics or self-censorship in digital settings, hinders the ability of researchers to confidently interpret and generalize their findings. This article discusses a methodological approach to fill this research gap. A case study of paid Amazon Mechanical Turk respondents (n = 509) is presented where respondents completed psychometric surveys and provided fully anonymized access to their Facebook timelines. This case study concentrates on data collection and anonymization processes, and discusses issues in data harvested over the Internet.

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