Pertinent questions on the measurement of social indicators are: the verification of data gained online (e.g., controlling for self-representation on social networks), and appropriate uses in community management and policy-making. Across platforms like Facebook, LinkedIn, Twitter, and blogging services, users (sub)consciously represent themselves in a way which is appropriate for their intended audience (Qui et al., 2012; Zhao et al., 2008). However, scholars in the social sciences and computer science have not yet adequately addressed controlling for self-representation, or the propensity to display or censor oneself, in their analyses (Zhao et al., 2008; Das and Kramer, 2013). As such researchers on these platforms risk working with ‘gamified’, socially responding, or online disinhibitive (trolls) personas which goes above and beyond efforts to contain Common Method Biases (CMB) (Linville, 1985; Suler, 2004; Podsakoff et al., 2003). What has not been approached in a systematic way is the verification of such data on offline and actual personality. In this paper, we focus on the alignment of traditional survey methods with unobtrusive methods to gather profile data from online social media via crowdsourcing platforms.
Hall, Margeret A. and Caton, Simon, "A Crowdsourcing Approach to Identify Common Method Bias and Self-Representation" (2014). Interdisciplinary Informatics Faculty Proceedings & Presentations. 5.