Rushes to adopt the latest technologies to the field of community moderation are generally inequitable for volunteer communities. The closed-door nature of product development at the majority of tech companies means that the logic underlying the creation of new features is opaque. What does this mean for those who want to equitably employ newer technologies in service of volunteer moderators? We present the development and deployment of the Wikimedia Foundation’s Automoderator product as a contemporary alternative to product development processes.

We focus on the collaborative process undertaken between the Moderator Tools product team at the Wikimedia Foundation, and volunteer moderator communities, to design and build Automoderator. Automoderator is an automated anti-vandalism tool, which uses a language-agnostic ML model that predicts the probability of an edit being reverted. The product team integrated volunteer feedback and direction on a continuous basis. This included the use of existing community-created tools to guide Automoderator's direction, the creation and dissemination of a spreadsheet-based testing tool, soliciting user feedback on a central project page, and integrating an extension to allow communities to control Automoderator's behavior directly. We conclude by discussing the limitations and trade-offs of this approach to product development.

In this paper, I conduct a systematic review of the use of VirtualHumans.org in academic studies of the virtual influencer industry. To do this, I analyze all references to and use of content from the Virtual Humans website in relevant published research from 2019 to 2024 (n=189), including the website’s database of existing virtual influencers, interviews with virtual influencer creators and virtual influencer characters themselves, articles on the state of the virtual influencer industry, and profiles of each listed virtual influencer. I pair this analysis with a brief history of VirtualHumans.org from a political economy perspective, noting the factors which went into the website’s creation; its acquisition by Offbeat Media Group, a digital marketing agency; and its organizational shifts following the sudden departure of its founder in 2023. Ultimately, I question whether academic viewpoints of the emergent virtual influencer industry, many which refer to the Virtual Humans website as a valuable resource for grasping a sense of the size and scope of the virtual influencer phenomenon, adequately consider the rooted biases and commercial interests represented by the website as not only a database but a powerful broker within the industry. Moreover, by narrating the organizational development of VirtualHumans.org as an enterprise, I contribute detailed context into the formation of these biases and commercial interests which inform its position in the virtual influencer scene.

This research investigates the complexities of algorithmic management (AM) within the gig economy, focusing on platforms like Upwork. It delves into how gig workers employ algorithmic sensemaking (AS) to navigate and counter the challenges presented by AM. The study emphasizes the paradox of algorithms serving as both tools for operational efficiency and mechanisms of control, highlighting the resultant power imbalances and their impact on worker autonomy. It explores the dualistic nature of algorithms as facilitators of operational efficiency and as mechanisms of control and surveillance, by articulating how they inherently embed power asymmetries that can undermine worker autonomy.

Through a mixed-methods approach that incorporates thematic analysis of community discussions and natural language processing (NLP) techniques, this research uncovers the nuanced strategies developed by gig workers. Significantly, it contributes to the discourse on labor strategies, digital literacy, and the democratization of work in the digital age, offering insights into how gig workers adapt to and negotiate with algorithmic systems. This work not only advances academic understanding of AM's implications for the labor market but also proposes avenues for future research on enhancing gig workers' agency and equity in the platform economy.