Whereas in most studies conducted previously the effect of automation bias has been investigated in terms of an instantaneous decision, this study is aimed at quantifying its duration.
This article interrogates platform-specific bias in the contemporary algorithmic media landscape through a comparative study of the representation of pregnancy on the Web and social media. Online visual materials such as social media content related to pregnancy are not void of bias, nor are they very diverse. The case study is a cross-platform analysis of social media imagery for the topic of pregnancy, through which distinct visual platform vernaculars emerge. The authors describe two visualization methods that can support comparative analysis of such visual vernaculars: the image grid and the composite image. While platform-specific perspectives range from lists of pregnancy tips on Pinterest to pregnancy information and social support systems on Twitter, and pregnancy humour on Reddit, each of the platforms presents a predominantly White, able-bodied and heteronormative perspective on pregnancy.
Reporting of research findings is often selective. This threatens the validity of the published body of knowledge if the decision to report depends on the nature of the results. The evidence derived from studies on causes and mechanisms underlying selective reporting may help to avoid or reduce reporting bias. Such research should be guided by a theoretical framework of possible causal pathways that lead to reporting bias. We build upon a classification of determinants of selective reporting that we recently developed in a systematic review of the topic. The resulting theoretical framework features four clusters of causes. There are two clusters of necessary causes: (A) motivations (e.g. a preference for particular findings) and (B) means (e.g. a flexible study design). These two combined represent a sufficient cause for reporting bias to occur. The framework also features two clusters of component causes: (C) conflicts and balancing of interests referring to the individual or the team, and (D) pressures from science and society. The component causes may modify the effect of the necessary causes or may lead to reporting bias mediated through the necessary causes. Our theoretical framework is meant to inspire further research and to create awareness among researchers and end-users of research about reporting bias and its causes.
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