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Binary Characterization Of SNS Use

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First, typologies based on amount of SNS use focus on the frequency of SNS uses or the intensity of individual SNS activities. Throughout all such studies, the goal of analysis is to deduce clusters of people from their time spent using sites like Facebook and Twitter or using particular features of such sites. For example, Lampe and colleagues (2013b) draw the line of distinction between ‘light’ and ‘heavy’ SNS users, as compared to ‘non-users’. While it seems evident that a simple binary characterisation of SNS behaviours into users and non-users would result in a typology that is too broad and lacks analytical nuance, this perspective has been remarkably popular among SNS scholars. With the same motivation, other scholars differentiate …show more content…

Typically, studies focusing on this dimension start out by recognising that an analysis of the mere frequency or breadth of SNS activities may be insufficient to distinguish between different categorical types of SNS users. This is why, a number of typologies have proposed to take into account the qualitative differences in the ways how people use social media, e.g. how they use SNSs and what psychological gratifications can be associated with different types of media uses (Donohew et al. 1987; Palmgreen & Rayburn 1979; Schlosser 2005). This line of studies, in particular, has produced very promising research. Despite the fact that these three dimensions are frequently conflated in the literature, it is important to conceptually distinguish them. Failing to do so can result in incoherent categories in its respective property space (Barton 1955). For example, a recent McKinsey study (2011) locates users along a behavioural continuum of information seeking from online communities and informational contributions to such communities. According to the study, this results in six broad segments of users, which are subsequently named ‘simplifiers’, ‘surfers’, ‘bargainers’, ‘connectors’, ‘sportsters’ and ‘routiners’. The problem with this approach is that the scholars have used the number of hours users spend online alongside the variety and types of users’ online activities as their main clustering dimensions. Based on the available

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