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Comparison of Clustering Algorithms for Knowledge Discovery in Social Media Publications: A Case Study of Mental Health Analysis

  • Autores: Manuel Couto, Javier Parapar Árbol académico, David Enrique Losada Carril Árbol académico
  • Localización: Procesamiento del lenguaje natural, ISSN 1135-5948, Nº. 73, 2024, págs. 69-81
  • Idioma: inglés
  • Títulos paralelos:
    • Comparación de algoritmos de agrupamiento para el descubrimiento de conocimiento en publicaciones de redes sociales: un caso de estudio en salud mental
  • Enlaces
  • Resumen
    • español

      En la era de las redes sociales, el contenido generado por los usuarios es fundamental para detectar los primeros signos de trastornos mentales. En este estudio utilizamos el agrupamiento de publicaciones por tópicos para analizar el contenido de la plataforma Reddit. Nuestro objetivo primordial es utilizar técnicas de agrupamiento para descubrir temas centrales, con un enfoque en la identificación de temas comunes entre los grupos de usuarios que sufren enfermedades mentales como la depresión, la anorexia, la adicción a los juegos de azar y las autolesiones. Nuestros hallazgos muestran que ciertos clusters son más cohesivos, por ejemplo mostrando una mayor proporción de textos de personas con depresión. Además, hemos descubierto subreddits que están fuertemente vinculados a textos escritos por usuarios deprimidos. Estos hallazgos arrojan luz sobre cómo las interacciones en línea y los temas que se tratan en los subreddits reflejan aspectos de salud mental, abriendo el camino para futuras investigaciones e intervenciones dirigidas a la prevención de trastornos.

    • English

      In the age of social media, user-generated content is critical for detecting early signs of mental disorders. In this study, we use thematic clustering to analyze the content of the social media platform Reddit. Our primary goal is to use clustering techniques for comprehensive topic discovery, with a focus on identifying common themes among user groups suffering from mental illnesses such as depression, anorexia, gambling addiction, and self-harm. Our findings show that certain clusters are more cohesive, e.g., with a higher proportion of texts indicating depression. Furthermore, we discovered subreddits that are strongly linked to texts from the depressed user group. These findings shed light on how online interactions and subreddit themes may impact users’ mental health, paving the way for future research and more targeted interventions in the field of online mental health.

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