Detalhes do Documento

Se analizan las dinámicas visuales y expresivas de TikTok mediante técnicas de inteligencia artificial aplicadas a un corpus de 7.398 clips. El objetivo es mostrar cómo los métodos visuales apoyados en aprendizaje automático pueden contribuir a comprender fenómenos sociocomunicativos y culturales en entornos digitales. Para ello se entrenó un modelo de aprendizaje no supervisado (SOM exploratorio y K-means clustering) que permitió identificar cuatro clústeres principales de organización audiovisual. El primero revela composiciones densas y saturadas de elementos que, más allá de lo técnico, permiten interpretar la pantalla como un croma algorítmico: un espacio flexible de ensamblaje que organiza gestos, efectos y trayectorias visuales. El segundo corresponde a los bucles y repeticiones rítmicas propios de la plataforma, que crean circularidad y continuidad en la experiencia. El tercero muestra cómo cuerpos y efectos digitales se combinan en superficies inestables, donde lo humano y lo técnico se entrelazan sin fronteras claras. El cuarto clúster señala un régimen de expresividad que estimula la participación constante y la autoexposición. En conjunto, estos hallazgos evidencian que TikTok no puede comprenderse únicamente como un espacio transmediático, sino como una interfaz algorítmica que organiza ritmos, afectos y subjetividades digitales, contribuyendo a reflexionar sobre el impacto político y sensorial de las plataformas en la vida contemporánea.

This article analyzes the visual and expressive dynamics of TikTok through artificial intelligence techniques applied to a corpus of 7,398 videos. The aim is to show how visual methods supported by machine learning can contribute to understanding sociocommunicative and cultural phenomena in digital environments. For this purpose, an unsupervised learning model (exploratory SOM and K-means clustering) was trained, which made it possible to identify four main clusters of audiovisual organization. The first reveals dense and saturated compositions of elements that, beyond the technical, allow us to interpret the screen as an algorithmic chroma: a flexible space of assemblage that organizes gestures, effects, and visual trajectories. The second corresponds to the loops and rhythmic repetitions characteristic of the platform, which create circularity and continuity in the experience. The third shows how bodies and digital effects combine in unstable surfaces, where the human and the technical intertwine without clear boundaries. The fourth cluster points to a regime of expressivity that stimulates constant participation and self-exposure. Taken together, these findings demonstrate that TikTok cannot be understood solely as a transmedial space, but rather as an algorithmic interface that organizes rhythms, affects, and digital subjectivities, encouraging reflection on the political and sensory impact of platforms in contemporary life.

This article analyzes the visual and expressive dynamics of TikTok through artificial intelligence techniques applied to a corpus of 7,398 videos. The aim is to show how visual methods supported by machine learning can contribute to understanding sociocommunicative and cultural phenomena in digital environments. For this purpose, an unsupervised learning model (exploratory SOM and K-means clustering) was trained, which made it possible to identify four main clusters of audiovisual organization. The first reveals dense and saturated compositions of elements that, beyond the technical, allow us to interpret the screen as an algorithmic chroma: a flexible space of assemblage that organizes gestures, effects, and visual trajectories. The second corresponds to the loops and rhythmic repetitions characteristic of the platform, which create circularity and continuity in the experience. The third shows how bodies and digital effects combine in unstable surfaces, where the human and the technical intertwine without clear boundaries. The fourth cluster points to a regime of expressivity that stimulates constant participation and self-exposure. Taken together, these findings demonstrate that TikTok cannot be understood solely as a transmedial space, but rather as an algorithmic interface that organizes rhythms, affects, and digital subjectivities, encouraging reflection on the political and sensory impact of platforms in contemporary life.

This article analyzes the visual and expressive dynamics of TikTok through artificial intelligence techniques applied to a corpus of 7,398 videos. The aim is to show how visual methods supported by machine learning can contribute to understanding sociocommunicative and cultural phenomena in digital environments. For this purpose, an unsupervised learning model (exploratory SOM and K-means clustering) was trained, which made it possible to identify four main clusters of audiovisual organization. The first reveals dense and saturated compositions of elements that, beyond the technical, allow us to interpret the screen as an algorithmic chroma: a flexible space of assemblage that organizes gestures, effects, and visual trajectories. The second corresponds to the loops and rhythmic repetitions characteristic of the platform, which create circularity and continuity in the experience. The third shows how bodies and digital effects combine in unstable surfaces, where the human and the technical intertwine without clear boundaries. The fourth cluster points to a regime of expressivity that stimulates constant participation and self-exposure. Taken together, these findings demonstrate that TikTok cannot be understood solely as a transmedial space, but rather as an algorithmic interface that organizes rhythms, affects, and digital subjectivities, encouraging reflection on the political and sensory impact of platforms in contemporary life.