Análisis de modelos predictivos en Instagram: Tendencias actuales y brechas en la investigación

Briggitte Nathaly Loor-Bravo; Lucía Bernarda Rivadeneira-Barreiro

Autores/as

Loor-Bravo Briggitte Nathaly Universidad Técnica de Manabí. Portoviejo, Ecuador. https://orcid.org/0009-0005-5075-5609
Rivadeneira-Barreiro Lucía Bernarda Universidad Técnica de Manabí. Portoviejo, Ecuador. https://orcid.org/0000-0001-5989-7703

Palabras clave:

modelo predictivo, Instagram, redes sociales, tendencias, brechas

Resumen

DOI: https://doi.org/10.46296/ig.v8i15.0231

Resumen

Las predicciones en redes sociales permitieron comprender el comportamiento de los usuarios e identificar tendencias, optimizando las estrategias de contenido. A pesar de investigaciones previas en diversas plataformas, persistió una brecha en el análisis de modelos predictivos enfocados en Instagram. Por ello, este estudio tuvo como propósito realizar una revisión sistemática de la literatura en bases de datos académicas sobre modelos de predicción de popularidad en esta red social. Los resultados revelaron que las organizaciones emplearon estos modelos para mejorar decisiones de marketing, detectar tendencias y personalizar contenido. Se encontró que las publicaciones con fotos individuales fueron las más investigadas, mientras que formatos como reels y carruseles plantearon desafíos analíticos debido a la necesidad de técnicas avanzadas de preprocesamiento de texto para mejorar la precisión. Aunque se avanzó en el desarrollo de modelos para otras redes, los estudios específicos en Instagram presentaron limitaciones en cuanto a transparencia, ética y privacidad, además de un uso reducido de ciertos tipos de contenido para realizar predicciones. Las implicaciones de este estudio son prácticas, ya que proporcionan a los profesionales de marketing y a los creadores de contenido una comprensión integral de cómo los modelos predictivos en Instagram pueden optimizar la estrategia de contenido en esta plataforma.

Palabras clave: modelo predictivo, Instagram, redes sociales, tendencias, brechas.

Abstract

Social media predictions allowed for an understanding of user behavior and the identification of trends, optimizing content strategies. Despite prior research across various platforms, a gap remained in the analysis of predictive models focused on Instagram. Therefore, this study aimed to conduct a systematic literature review in academic databases on models for predicting popularity on this social network. The findings revealed that organizations employed these models to improve marketing decisions, detect trends, and personalize content. It was found that individual photo posts were the most studied, while formats like reels and carousels posed analytical challenges due to the need for advanced text preprocessing techniques to enhance accuracy. Although progress has been made in developing models for other networks, studies specific to Instagram still presented limitations regarding transparency, ethics, and privacy, as well as a limited use of certain types of content for making predictions. The implications of this study are practical, as they provide marketing professionals and content creators with a comprehensive understanding of how predictive models on Instagram can be used to optimize content strategy on this platform.

Keywords: predictive model, Instagram, social media, trends, gaps.

Información del manuscrito:
Fecha de recepción: 03 de octubre de 2024.
Fecha de aceptación: 19 de diciembre de 2024.
Fecha de publicación: 10 de enero de 2025.

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