RT info:eu-repo/semantics/article
T1 Machine Learning algorithms to address the polarity and stigma of mental health disclosures on Instagram
A1 Merayo Álvarez, Noemí
A1 Ayuso Lanchares, Alba
A1 González Sanguino, Teresa Clara
K1 Instagram
K1 machine learning
K1 mental health
K1 natural language processing
K1 sentiment analysis
K1 social networks
K1 stigma
K1 1203 Ciencia de Los Ordenadores
K1 61 Psicología
K1 5910.02 Medios de Comunicación de Masas
AB This research explores the social response to disclosures and conversations about mental health on social media, which is a pioneering and innovative approach. Unlike previous studies, which focused predominantly on psychopathological aspects, this study explores how communities react to conversations about mental health on Instagram, one of the favourite social media platforms among young people, breaking new ground not only in the Spanish context, but also on a global scale, filling a gap in international research. The study created a novel corpus by collecting and labelling comments on Instagram posts related to celebrity mental health disclosures, categorising them by polarity (positive, negative, neutral) and stigma. Additionally, the research implements machine learning algorithms to detect stigma and polarity in mental health disclosures on Instagram. While traditional techniques like Support Vector Machine (SVM) and RF (Random Forest) displayed decent performance with lower computational loads, advanced deep learning and BERT (Bidirectional Encoder Representation from Transformers) algorithms achieved outstanding results. In fact, BERT models achieve around 96% accuracy in polarity and stigma detection, while deep learning models achieve 80% for polarity and 87% for stigma, very high accuracy metrics. This research contributes significantly to understanding the impact of mental health discussions on social media, offering insights that can reduce stigma and raise awareness. Artificial intelligence can be used for more responsible use of social media and effective management of mental health problems in digital environments.
PB Wiley
SN 0266-4720
YR 2025
FD 2025
LK https://uvadoc.uva.es/handle/10324/75138
UL https://uvadoc.uva.es/handle/10324/75138
LA eng
NO Expert Systems, 2025, vol. 42, n. 2, e13832
NO Producción Científica
DS UVaDOC
RD 04-jun-2025