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.ĪI-Mediated Communication (AI-MC) is interpersonal communication that involves an artificially intelligent system that can modify, augment, or even generate content to achieve communicative and relational goals. We theorize our findings to detail the ways the platform's algorithm and its users co-produce knowledge of identity on the platform. 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 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. 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 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. 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. Participants identified potential harms that can accompany algorithms' tailoring content to their person identities. 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. 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. We propose a model of writer-suggestion interaction for writing with GPT-2 for a movie review writing task, followed by ways in which the model can be used for future research, along with outlining opportunities for research and design.Īlgorithms in online platforms interact with users' identities in different ways. The suggestion system also had various effects on the writing processes - contributing to different aspects of the writing process in unique ways. We found writers interact with next-phrase suggestions in various complex ways - writers are able to abstract multiple parts of the suggestions and incorporate them within their writing - even when they disagree with the suggestion as a whole. We conducted a study where amateur writers were asked to write two movie reviews each, one without suggestions and one with. While there has been some quantitative research on the effects of suggestion systems on writing, there has been little qualitative work to understand how writers interact with suggestion systems and how it affects their writing process - specifically for a non-native but English writer. You can find out more details about this great deal on the Lightkey Pro Text Prediction Software: Lifetime Subscription over at our deals store at the link below.We present an exploratory qualitative study to understand how writers interact with next-phrase suggestions. For any other application, simply activate Lightkey Anywhere & benefit from predictive text.Intuitive dashboard helps you unlock your key productivity metrics.Never submits your content to the cloud.Natively supports MS Office Outlook, Word, & PowerPoint and Google Chrome apps.Offers the most relevant terms for your professional content.
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