Thesis Description & Objectives
Context: Social media have a profound impact on the way individuals choose to (re)present themselves in the digital era. We have developed in collaboration with the Department of Philosophy at Università di Torino, visual big data analytics tools through computational image analysis and deep learning techniques that borrow from other disciplines, such as socio-semiotics and visual semiotics. Our goal is to extend these techniques to develop powerful, easy-to-use tools to power the next generation of cultural analytics.
Open research questions to be tackled in a research thesis are varied and will be selected based on candidates skills and research interests. Examples of research questions include:
– Extending the available computational pipeline (FRESCO) with new tools and techniques to extract semantic characteristics from images (composition, content, etc.)
– Designing user-friendly data analytics and visual analytics pipelines to apply the FRESCO pipeline for strategic and cultural research (e.g., marketing, trend analysis, network analysis)
– Extending FRESCO to work on multi-modal and short video collections (e.g, TikTok)
– Adapting the proposed pipeline to the analysis of different types of images (advertisement, meme, artworks, AI-generated images, …). Particularly relevant is the use of the proposed pipeline to detect and quantify biases in AI-generated images
– Design, train and evaluate deep neural networks that mimic high-level semantic analysis (which emotions are solicited by a given image? Which values are expressed? what are the narrative roles in an image?)
– Investigating the applicability of Multi-modal Large Language Model (LLM) to extract high-level interpretation from images and reproduce the type of in-depth analysis performed by experts, such as semioticians
Prerequisites: good knowledge of big data analytics, computer vision and deep learning frameworks, and a willingness to engage in multi-disciplinary research
For more information on FRESCO see: https://arxiv.org/abs/2407.03268
