| Revista: | Computación y sistemas |
| Base de datos: | |
| Número de sistema: | 000607908 |
| ISSN: | 1405-5546 |
| Autores: | Encinas Monroy, Iván A.1 Beltrán, Jessica2 Sánchez, Luis H.3 Felipe Rodríguez, Luis1 Macías, Adrián1 Pérez, Cynthia B.4 Domitsu, Manuel1 Castro, Luis A.1 |
| Instituciones: | 1Instituto Tecnológico de Sonora, Departamento de Computación y Diseño, México 2Universidad Autónoma de Coahuila, Centro de Investigación en Matemáticas Aplicada, México 3Instituto Politécnico Nacional, Centro de Investigación y Desarrollo de Tecnología Digital, México 4Instituto Tecnológico de Sonora, Guaymas. México |
| Año: | 2024 |
| Periodo: | Abr-Jun |
| Volumen: | 28 |
| Número: | 2 |
| Paginación: | 553-575 |
| País: | México |
| Idioma: | Inglés |
| Resumen en inglés | Patterns of use of social networking sites like Instagram can be indicators of the mental state of users. Of particular interest to the HCI community are those markers and patterns useful for inferring the mental health of users experiencing depressive episodes or moods. Detecting individuals’ depressive moods through their typical Instagram activity remains a challenge due to the diversity of the content posted. Previous research often focuses on retrieving content of hashtags related directly to depression for analysis. Thus, although based on real posts, results can be highly biased. Analyzing all user posts in individuals’ day-to-day lives can yield ecologically valid findings, but it is challenging. We conducted an observational study aimed at detecting the depressive moods of users from their Instagram posts. We analyzed text, images, and posting behavior using two approaches: inferential statistics and machine learning. Our results indicate that the time of day and the hue levels of a posted image could lead to the detection of depressive moods. Furthermore, our machine-learning approach yielded up to 65% of accuracy. Although our study yields ecologically valid findings, several challenges remain to be addressed due to the heterogeneity of the dataset, as it typically happens in real-world studies. |
| Keyword: | Social networking sites, Depressive mood detection, Instagram, Machine learning, Behavior analysis, Image analysis, Text analysis, Transfer learning |
| Texto completo: | Texto completo (Ver PDF) Texto completo (Ver HTML) |
