mlsanityData to 7 Oct 2026

IMAGINE: An Integrated Model of Artificial Intelligence-Mediated Communication Effects

arXiv 2212.08658 ↗First posted 13 Dec 2022cs.AI cs.CY cs.HCcited by 0

Not on the map. Why?

Abstract (arXiv)

Artificial Intelligence (AI) is transforming all fields of knowledge and production. From surgery, autonomous driving, to image and video creation, AI seems to make possible hitherto unimaginable processes of automation and efficient creation. Media and communication are not an exception, and we are currently witnessing the dawn of powerful AI tools capable of creating artistic images from simple keywords, or to capture emotions from facial expression. These examples may be only the beginning of what can be in the future the engines for automatic AI real time creation of media content linked to the emotional and behavioural responses of individuals. Although it may seem we are still far from there, it is already the moment to adapt our theories about media to the hypothetical scenario in which content production can be done without human intervention, and governed by the controlled any reactions of the individual to the exposure to media content. Following that, I propose the definition of the Integrated Model of Artificial Intelligence-Mediated Communication Effects (IMAGINE), and its consequences on the way we understand media evolution (Scolari, 2012) and we think about media effects (Potter, 2010). The conceptual framework proposed is aimed to help scholars theorizing and doing research in a scenario of continuous real-time connection between AI measurement of people's responses to media, and the AI creation of content, with the objective of optimizing and maximizing the processes of influence. Parasocial interaction and real-time beautification are used as examples to model the functioning of the IMAGINE process.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 8 of its 118 references that are in the map. First posted on arXiv 13 Dec 2022.

Builds on

8 of 118 references.

References · 8

  1. 2015Deep Unsupervised Learning using Nonequilibrium Thermodynamicscited by 7,005
  2. 2020Face2Face: Real-time Face Capture and Reenactment of RGB Videoscited by 631
  3. 2018StoryGAN: A Sequential Conditional GAN for Story Visualizationcited by 137
  4. 2020DeepFaceLab: Integrated, flexible and extensible face-swapping frameworkcited by 129
  5. 2019The role of artificial intelligence in achieving the Sustainable Development Goalscited by 100
  6. 2021On the Effect of Selfie Beautification Filters on Face Detection and Recognitioncited by 10
  7. 2020What is "Intelligent" in Intelligent User Interfaces? A Meta-Analysis of 25 Years of IUIcited by 4
  8. 2022Automation Slicing and Testing for in-App Deep Learning Modelscited by 1

Built on by

Not cited yet.