Formation mechanism of contributors' self-identity based on social identity in online knowledge communities

Online identity work dynamics of Instagram micro-influencers: an extreme case approach

Background

Advanced media technologies have become an integral part of people's daily lives, providing them with new tools and environments for the formation and enactment of their identities. To date, the literature acknowledges that media technologies, such as social networking sites, are used to form and enact online identities, and that these platforms can simultaneously pose challenges to individuals' identity work. However, we know little about the precise online identity work strategies that individuals employ in response to the challenges they face over time.

Objective

This paper examines the online identity work dynamics of Instagram micro-influencers, for whom social network sites enable and guide them in forming and enacting their online identities on a daily basis. The study was guided by the following research question: what are the challenges that Instagram micro-influencers perceive online and what are the online identity work strategies that they employ in response to these challenges over time?

Methods

This study employs an extreme case approach to rigorously explore the lives of seven micro-influencers on Instagram. We combine in-depth data from narrative interviews, longitudinal data from online autobiographical narratives revealed through the participants' Instagram timelines, and follow-up interviews.

Results

Our analysis revealed three main themes that highlight the challenges that Instagram micro-influencers face online: (1) amplified social expectations, (2) feelings of inauthenticity, and, as a result thereof, (3) psychological distress. We found that these challenges were viewed as catalysts for their online identity work processes. We identified three key online identity work strategies that the Instagram micro-influencers employed in response over time: (1) experimenting with their online identities, followed by either (2) segmenting between their online and offline identities, or (3) adding identities through online multiplicity.

Conclusion

Our research provides new insights into how individuals may respond to the challenge of managing their online identities over time by engaging in different online identity work strategies. This study highlights the importance of designing online media technologies that enable individuals to cope with online challenges. We emphasize the need to design online spaces for (1) the expression of authentic identities, (2) community building, and (3) online multiplicity.

Consistently estimating network statistics using aggregated relational data

Collecting complete network data is expensive, time-consuming, and often infeasible. Aggregated Relational Data (ARD), which ask respondents questions of the form "How many people with trait X do you know?" provide a low-cost option when collecting complete network data is not possible. Rather than asking about connections between each pair of individuals directly, ARD collect the number of contacts the respondent knows with a given trait. Despite widespread use and a growing literature on ARD methodology, there is still no systematic understanding of when and why ARD should accurately recover features of the unobserved network. This paper provides such a characterization by deriving conditions under which statistics about the unobserved network (or functions of these statistics like regression coefficients) can be consistently estimated using ARD. We first provide consistent estimates of network model parameters for three commonly used probabilistic models: the beta-model with node-specific unobserved effects, the stochastic block model with unobserved community structure, and latent geometric space models with unobserved latent locations. A key observation is that cross-group link probabilities for a collection of (possibly unobserved) groups identify the model parameters, meaning ARD are sufficient for parameter estimation. With these estimated parameters, it is possible to simulate graphs from the fitted distribution and analyze the distribution of network statistics. We can then characterize conditions under which the simulated networks based on ARD will allow for consistent estimation of the unobserved network statistics, such as eigenvector centrality, or response functions by or of the unobserved network, such as regression coefficients.