Cinthia Sánchez
Cinthia Sánchez es Doctora (c) en Computación de la Universidad de Chile, con un Magíster en Ciencias mención Computación y una Ingeniería en Informática. Sus intereses de investigación abarcan Minería de Datos, Procesamiento de Lenguaje Natural, Aprendizaje Automático y Análisis de Redes. Su investigación doctoral se enfocó en mejorar el alcance del análisis automatizado de las redes sociales durante emergencias.
Fue estudiante investigadora del Instituto Milenio Fundamentos de los Datos y del Centro Nacional de Inteligencia Artificial. En 2024, realizó una pasantía de investigación en el Instituto de Ciencias de la Información de la Universidad del Sur de California. Durante su formación, ha combinado la investigación, la docencia y el liderazgo en comunidades estudiantiles (Rama Estudiantil IEEE y Grupo IEEE Women in Engineering de la Universidad de Chile). Ha dictado cursos de Minería de Datos, Visualización de Datos, y Aspectos Técnicos de la Inteligencia Artificial.
PUBLICACIONES
This article presents an exploration of the use of pre-trained Large Language Models (LLMs) for crisis classification to address labeled data dependency issues. We present a methodology that enhances open LLMs through fine-tuning, creating zero-shot and few-shot classifiers that approach traditional supervised models in classifying crisis-related messages. A comparative study evaluates crisis classification tasks using general domain pre-trained LLMs, crisis-specific LLMs, and traditional supervised learning methods, establishing a benchmark in the field.
Our task-specific fine-tuned Llama model achieved a 69% macro F1 score in classifying humanitarian information–a remarkable 26% improvement compared to the Llama baseline, even with limited training data. Moreover, it outperformed ChatGPT4 by 3% in macro F1. This improvement increased to 71% macro F1 when fine-tuning Llama with multitask data. For the binary classification of messages as
related
vs.
not related
to crises, we observed that pre-trained LLMs, such as Llama 2 and ChatGPT4, performed well without fine-tuning, achieving an 87% macro F1 score with ChatGPT4.
This research expands our knowledge of how to exploit the potential of LLMs for crisis classification, representing a great opportunity for crisis scenarios that lack labeled data. The findings emphasize the potential of LLMs in crisis informatics to address cold start challenges, especially critical in the initial phases of a disaster, while also showcasing their capacity to attain high accuracy even with limited training data.
The pervasive expectations about ideal body types in Western society can lead to body image concerns, dissatisfaction, and in extreme cases, eating disorders and other psychopathologies related to body image. While previous research has focused on online pro-anorexia communities glorifying the "thin ideal", less attention has been given to the broader spectrum of body image concerns or how emerging disorders like muscle dysmorphia ("bigorexia") present in online platforms. To address this gap, we analyze 46 Reddit forums related to diet, fitness, and mental health. We mapped these communities along gender and body ideal dimensions, revealing distinct patterns of emotional expression and community support. Feminine-oriented communities, especially those endorsing the thin ideal, expressed higher levels of negative emotions and received caring comments in response. In contrast, muscular ideal communities displayed less negativity, regardless of gender orientation, but received aggressive compliments in response, marked by admiration and toxicity. Mental health discussions aligned more with thin ideal, feminine-leaning spaces. By uncovering these gendered emotional dynamics, our findings can inform the development of moderation strategies that foster supportive interactions while reducing exposure to harmful content.
Edificio de Innovación UC, Piso 2
Vicuña Mackenna 4860
Macul, Chile
Vicuña Mackenna 4860
Macul, Chile