Social networks are creating a digital world in which the cognitive, emotional, and pragmatic value of the imagery of human faces and bodies is arguably changing. However, researchers in the digital humanities are often ill-equipped to study these phenomena at scale. This work presents FRESCO (Face Representation in E-Societies through Computational Observation), a framework designed to explore the socio-cultural implications of images on social media platforms at scale. FRESCO deconstructs images into numerical and categorical variables using state-of-the-art computer vision techniques, aligning with the principles of visual semiotics. The framework analyzes images across three levels: the plastic level, encompassing fundamental visual features like lines and colors; the figurative level, representing specific entities or concepts; and the enunciation level, which focuses particularly on constructing the point of view of the spectator and observer. These levels are analyzed to discern deeper narrative layers within the imagery. Experimental validation confirms the reliability and utility of FRESCO, and we assess its consistency and precision across two public datasets. Subsequently, we introduce the FRESCO score, a metric derived from the framework's output that serves as a reliable measure of similarity in image content.

翻译：社交网络正在创造一个数字世界，其中人脸和身体图像的认知、情感及语用价值正在发生显著变化。然而，数字人文领域的研究者往往缺乏大规模研究这些现象的有效工具。本研究提出FRESCO（通过计算观察的电子社会中人脸表征）框架，旨在大规模探索社交媒体平台图像的社会文化意涵。FRESCO运用前沿计算机视觉技术将图像解构为数值与分类变量，并与视觉符号学原理相契合。该框架从三个层面分析图像：塑性层面，涵盖线条与色彩等基本视觉特征；具象层面，表征特定实体或概念；以及陈述层面，特别聚焦于构建观看者与观察者的视角。通过对这些层面的分析，可辨识图像中更深层的叙事结构。实验验证证实了FRESCO的可靠性与实用性，我们在两个公开数据集上评估了其一致性与精确度。随后，我们提出FRESCO分数——一种基于框架输出衍生的度量指标，可作为图像内容相似性的可靠衡量标准。