Content creators with marginalized identities are disproportionately affected by shadowbanning on social media platforms, which impacts their economic prospects online. Through a diary study and interviews with eight marginalized content creators who are women, pole dancers, plus size, and/or LGBTQIA+, this paper examines how content creators with marginalized identities experience shadowbanning. We highlight the labor and economic inequalities of shadowbanning, and the resulting invisible online labor that marginalized creators often must perform. We identify three types of invisible labor that marginalized content creators engage in to mitigate shadowbanning and sustain their online presence: mental and emotional labor, misdirected labor, and community labor. We conclude that even though marginalized content creators engaged in cross-platform collaborative labor and personal mental/emotional labor to mitigate the impacts of shadowbanning, it was insufficient to prevent uncertainty and economic precarity created by algorithmic opacity and ambiguity.

Shadowbanning is a unique content moderation strategy receiving recent media attention for the ways it impacts marginalized social media users and communities. Social media companies often deny this content moderation practice despite user experiences online. In this paper, we use qualitative surveys and interviews to understand how marginalized social media users make sense of shadowbanning, develop folk theories about shadowbanning, and attempt to prove its occurrence. We find that marginalized social media users collaboratively develop and test algorithmic folk theories to make sense of their unclear experiences with shadowbanning. Participants reported direct consequences of shadowbanning, including frustration, decreased engagement, the inability to post specific content, and potential financial implications. They reported holding negative perceptions of platforms where they experienced shadowbanning, sometimes attributing their shadowbans to platforms' deliberate suppression of marginalized users' content. Some marginalized social media users acted on their theories by adapting their social media behavior to avoid potential shadowbans. We contributecollaborative algorithm investigation : a new concept describing social media users' strategies of collaboratively developing and testing algorithmic folk theories. Finally, we present design and policy recommendations for addressing shadowbanning and its potential harms.

Marginalized social media users struggle to navigate inequitable content moderation they experience online. We developed the Online Identity Help Center (OIHC) to confront this challenge by providing information on social media users' rights, summarizing platforms' policies, and providing instructions to appeal moderation decisions. We discuss our findings from interviews (n = 24) and surveys (n = 75) which informed the OIHC's design, along with interviews about and usability tests of the site (n = 12). We found that the OIHC's resources made it easier for participants to understand platforms' policies and access appeal resources. Participants expressed increased willingness to read platforms' policies after reading the OIHC's summarized versions, but expressed mistrust of platforms after reading them. We discuss the study's implications, such as the benefits of providing summarized policies to encourage digital literacy, and how doing so may enable users to express skepticism of platforms' policies after reading them.

Social media sites use content moderation to attempt to cultivate safe spaces with accurate information for their users. However, content moderation decisions may not be applied equally for all types of users, and may lead to disproportionate censorship related to people's genders, races, or political orientations. We conducted a mixed methods study involving qualitative and quantitative analysis of survey data to understand which types of social media users have content and accounts removed more frequently than others, what types of content and accounts are removed, and how content removed may differ between groups. We found that three groups of social media users in our dataset experienced content and account removals more often than others: political conservatives, transgender people, and Black people. However, the types of content removed from each group varied substantially. Conservative participants' removed content included content that was offensive or allegedly so, misinformation, Covid-related, adult, or hate speech. Transgender participants' content was often removed as adult despite following site guidelines, critical of a dominant group (e.g., men, white people), or specifically related to transgender or queer issues. Black participants' removed content was frequently related to racial justice or racism. More broadly, conservative participants' removals often involved harmful content removed according to site guidelines to create safe spaces with accurate information, while transgender and Black participants' removals often involved content related to expressing their marginalized identities that was removed despite following site policies or fell into content moderation gray areas. We discuss potential ways forward to make content moderation more equitable for marginalized social media users, such as embracing and designing specifically for content moderation gray areas.

Algorithms in online platforms interact with users' identities in different ways. However, little is known about how users understand the interplay between identity and algorithmic processes on these platforms, and if and how such understandings shape their behavior on these platforms in return. Through semi-structured interviews with 15 US-based TikTok users, we detail users' algorithmic folk theories of the For You Page algorithm in relation to two inter-connected identity types: person and social identity. Participants identified potential harms that can accompany algorithms' tailoring content to their person identities. Further, they believed the algorithm actively suppresses content related to marginalized social identities based on race and ethnicity, body size and physical appearance, ability status, class status, LGBTQ identity, and political and social justice group affiliation. We propose a new algorithmic folk theory of social feeds-The Identity Strainer Theory-to describe when users believe an algorithm filters out and suppresses certain social identities. In developing this theory, we introduce the concept of algorithmic privilege as held by users positioned to benefit from algorithms on the basis of their identities. We further propose the concept of algorithmic representational harm to refer to the harm users experience when they lack algorithmic privilege and are subjected to algorithmic symbolic annihilation. Additionally, we describe how participants changed their behaviors to shape their algorithmic identities to align with how they understood themselves, as well as to resist the suppression of marginalized social identities and lack of algorithmic privilege via individual actions, collective actions, and altering their performances. We theorize our findings to detail the ways the platform's algorithm and its users co-produce knowledge of identity on the platform. We argue the relationship between users' algorithmic folk theories and identity are consequential for social media platforms, as it impacts users' experiences, behaviors, sense of belonging, and perceived ability to be seen, heard, and feel valued by others as mediated through algorithmic systems.