#PoetsOfInstagram: Navigating The Practices And Challenges Of Novice Poets On Instagram

• URL: http://arxiv.org/abs/2402.19347v1
• Date: Thu, 29 Feb 2024 16:55:44 GMT
• Title: #PoetsOfInstagram: Navigating The Practices And Challenges Of Novice Poets On Instagram
• Authors: Ankolika De, Zhicong Lu
• Abstract summary: We employ qualitative analysis to explore motivations, experiences, and algorithmic influence within Instagram's poetry community. We demonstrate that participants prioritize conforming to algorithmic constraints for visibility, yet maintain their community's values of integrity and originality. We introduce the concept of Algorithmically Mediated Creative Labor, a phenomenon specific to non-monetizing creative users.
• Score: 22.17344487934666
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Commencing as a photo-sharing platform, Instagram has since become multifaceted, accommodating diverse art forms, with poetry emerging as a prominent one. However, the academic understanding of Instagram's poetry community is limited, yet its significance emerges from its distinctive utilization of a primarily visual social media platform guided by recommendation algorithms for disseminating poetry, further characterized by a predominantly novice creative population. We employ qualitative analysis to explore motivations, experiences, and algorithmic influence within Instagram's poetry community. We demonstrate that participants prioritize conforming to algorithmic constraints for visibility, yet maintain their community's values of integrity and originality, illustrating the tension between algorithmic growth and participant authenticity. We introduce the concept of Algorithmically Mediated Creative Labor, a phenomenon specific to non-monetizing creative users who are impacted by the prioritization of professional creators and continually adapt their creative endeavors to align with platform logic, thereby affecting their motivation and creative outputs.

Related papers

• Influencer: Empowering Everyday Users in Creating Promotional Posts via AI-infused Exploration and Customization [11.9449656506593]
  Influen is an interactive tool to assist novice creators in crafting high-quality promotional post designs. Within Influencer, we contribute a multi-dimensional recommendation framework that allows users to intuitively generate new ideas. Influential implements a holistic promotional post design system that supports context-aware image and caption exploration.
  arXiv  Detail & Related papers  (2024-07-20T16:27:49Z)
• Safeguard Text-to-Image Diffusion Models with Human Feedback Inversion [51.931083971448885]
  We propose a framework named Human Feedback Inversion (HFI), where human feedback on model-generated images is condensed into textual tokens guiding the mitigation or removal of problematic images. Our experimental results demonstrate our framework significantly reduces objectionable content generation while preserving image quality, contributing to the ethical deployment of AI in the public sphere.
  arXiv  Detail & Related papers  (2024-07-17T05:21:41Z)
• Stylebreeder: Exploring and Democratizing Artistic Styles through Text-to-Image Models [38.5896770458884]
  We introduce textttSTYLEBREEDER, a comprehensive dataset of 6.8M images and 1.8M prompts generated by 95K users on Artbreeder. By documenting unique, user-generated styles that transcend conventional categories like 'cyberpunk' or 'Picasso,' we explore the potential for unique, crowd-sourced styles. Our research demonstrates the potential of text-to-image diffusion models to uncover and promote unique artistic expressions.
  arXiv  Detail & Related papers  (2024-06-20T17:59:56Z)
• Social Reward: Evaluating and Enhancing Generative AI through Million-User Feedback from an Online Creative Community [63.949893724058846]
  Social reward as a form of community recognition provides a strong source of motivation for users of online platforms to engage and contribute with content. This work pioneers a paradigm shift, unveiling Social Reward - an innovative reward modeling framework. We embark on an extensive journey of dataset curation and refinement, drawing from Picsart: an online visual creation and editing platform.
  arXiv  Detail & Related papers  (2024-02-15T10:56:31Z)
• CreativeSynth: Creative Blending and Synthesis of Visual Arts based on Multimodal Diffusion [74.44273919041912]
  Large-scale text-to-image generative models have made impressive strides, showcasing their ability to synthesize a vast array of high-quality images. However, adapting these models for artistic image editing presents two significant challenges. We build the innovative unified framework Creative Synth, which is based on a diffusion model with the ability to coordinate multimodal inputs.
  arXiv  Detail & Related papers  (2024-01-25T10:42:09Z)
• No Longer Trending on Artstation: Prompt Analysis of Generative AI Art [7.64671395172401]
  We collect and analyse over 3 million prompts and the images they generate. Our study shows that prompting focuses largely on surface aesthetics, reinforcing cultural norms, popular conventional representations and imagery.
  arXiv  Detail & Related papers  (2024-01-24T08:03:13Z)
• Decoupled Textual Embeddings for Customized Image Generation [62.98933630971543]
  Customized text-to-image generation aims to learn user-specified concepts with a few images. Existing methods usually suffer from overfitting issues and entangle the subject-unrelated information with the learned concept. We propose the DETEX, a novel approach that learns the disentangled concept embedding for flexible customized text-to-image generation.
  arXiv  Detail & Related papers  (2023-12-19T03:32:10Z)
• Impressions: Understanding Visual Semiotics and Aesthetic Impact [66.40617566253404]
  We present Impressions, a novel dataset through which to investigate the semiotics of images. We show that existing multimodal image captioning and conditional generation models struggle to simulate plausible human responses to images. This dataset significantly improves their ability to model impressions and aesthetic evaluations of images through fine-tuning and few-shot adaptation.
  arXiv  Detail & Related papers  (2023-10-27T04:30:18Z)
• Catch You Everything Everywhere: Guarding Textual Inversion via Concept Watermarking [67.60174799881597]
  We propose the novel concept watermarking, where watermark information is embedded into the target concept and then extracted from generated images based on the watermarked concept. In practice, the concept owner can upload his concept with different watermarks (ie, serial numbers) to the platform, and the platform allocates different users with different serial numbers for subsequent tracing and forensics.
  arXiv  Detail & Related papers  (2023-09-12T03:33:13Z)
• On the Limits to Multi-Modal Popularity Prediction on Instagram -- A New Robust, Efficient and Explainable Baseline [5.859055059050023]
  We present a robust, efficient, and explainable baseline for population-based popularity prediction. We employ the latest methods in computer vision to maximize the information extracted from the visual modality. Our strongest models inform a lower limit to population-based predictability of popularity on Instagram.
  arXiv  Detail & Related papers  (2020-04-26T21:21:50Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.