No abstract

Social media influencers-individuals who utilize various forms of network power on social networks occupy a unique identity space. On the one hand, their network power is often tied to their social identity as creators of engaging material. On the other hand, their ability to promote commercial products and services steps outside the traditionally distinct commercial–social, occupational–personal divides. In this work, the network morphologies of influencers are explored in relation to their delivery of sponsored and non-sponsored content. This article explores how the disclosure of content as ‘sponsored’ affects audience reception. We show how that the promotion of content on social media often generates higher levels of engagement and receptiveness amongst their audience despite the platform’s assumption of organic non-commercial relationships. We find that engagement levels are highest among smaller out-degree networks. Additionally, we demonstrate that sponsored content not only returns a higher level of engagement, but that the effect of sponsorship is relatively consistent across out-degree network sizes. In sum, we suggest that social media audiences are not sensitive to commercial sponsorship when tied to identity, as long as that performance is convincing and consistent.

The gig economy, which is also referred to as the sharing or on-demand economy, involves the use of online platforms to offer and find short-term work, goods, and services on a flexible basis. These platforms, which allow freelancers and independent contractors to connect with clients in need of their services, have gained widespread popularity in recent years. However, the gig economy has been the subject of much controversy, particularly regarding the fairness of platform rating systems and their impact on workers' income and job security. This article presents an analysis of the distribution of fairness and perceived satisfaction with ranking systems in these work markets, and discusses the ways in which these systems may lead to unfair outcomes for workers. It also examines the effects of these systems on workers' income and job security, and investigates the potential influence of factors such as gender, age, and employment status on the fairness of these rating systems. The article suggests directions for further research on this topic and considers the implications of these findings for policymakers and practitioners.

Where do people meet? And how does their region’s wealth effect where they meet? Investigating these patterns across the United States, we explore community organization and association venues based on data provided from Meetup.com. Examining how individuals associate for business, social, and cultural reasons, we discover that a region’s median income significantly affects the type of venue for the meeting. However, certain types of associations centered on a select group of topics mitigate that effect. We discover that in the United States, personal social capital that is built and maintained “in person” is deeply embedded in commercial activities. As a result, access to various types of community is often limited to economically advantaged geographies.

The digital age has brought about significant changes in the way we communicate, access information, and conduct our daily lives. However, not all individuals have equal access to and proficiency with digital technology. This study examines how digital vulnerability, digital ability, and digital literacy vary across individuals with different education levels and income levels. This study finds that there is significant variation in digital vulnerability, digital ability, and digital literacy across education levels and income levels in the platform economy. Specifically, the higher the education level, the higher the digital vulnerability score and the lower the digital ability score. Similarly, the higher the income level, the lower the digital vulnerability score and the lower the digital ability score. Additionally, significant variation in digital literacy was found across education and income levels. These findings suggest that socio-economic factors such as education and income level are important predictors of digital inequality, and that individuals with higher education and income levels may be more cautious with online platforms and thus experience higher levels of digital vulnerability and lower digital ability.