2020
DOI: 10.1371/journal.pone.0229354
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Artificial neural networks for predicting social comparison effects among female Instagram users

Abstract: Systematic exposure to social media causes social comparisons, especially among women who compare their image to others; they are particularly vulnerable to mood decrease, selfobjectification, body concerns, and lower perception of themselves. This study first investigates the possible links between life satisfaction, self-esteem, anxiety, depression, and the intensity of Instagram use with a social comparison model. In the study, 974 women age 18-49 who were Instagram users voluntarily participated, completin… Show more

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Cited by 21 publications
(11 citation statements)
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“…Detection of drug abuse and dealing on Instagram multimodal data (captions, hashtags, comments, and photos) was analyzed by Yang and Luo (2017) by first exploring drug-related posts and second, specifying drug dealers' accounts based on a Multi-Layer Perceptron (MLP) neural network. From the psychological perspective, Jabłońska and Zajdel (2020) studied the relationship between several factors such as life satisfaction, anxiety, depression, and the extent to which women use Instagram and employed artificial neural networks for the classification of results. De et al (2017) employed a deep neural network to predict the popularity of future posts of a famous Indian lifestyle magazine based on historical data from Instagram postse.g., the creation time and associated tags.…”
Section: Research Papermentioning
confidence: 99%
“…Detection of drug abuse and dealing on Instagram multimodal data (captions, hashtags, comments, and photos) was analyzed by Yang and Luo (2017) by first exploring drug-related posts and second, specifying drug dealers' accounts based on a Multi-Layer Perceptron (MLP) neural network. From the psychological perspective, Jabłońska and Zajdel (2020) studied the relationship between several factors such as life satisfaction, anxiety, depression, and the extent to which women use Instagram and employed artificial neural networks for the classification of results. De et al (2017) employed a deep neural network to predict the popularity of future posts of a famous Indian lifestyle magazine based on historical data from Instagram postse.g., the creation time and associated tags.…”
Section: Research Papermentioning
confidence: 99%
“…En ambos casos se descartaron, como criterio de exclusión, aquellas instagramers que no respondieran a la concepción de influencer. Esto es, no se contemplaron en la muestra final perfiles creados por celebridades de la industria cinematográfica, televisiva, moda, etc., sino aquellas que han construido desde la anonimidad su instafama (Jabłońska & Zajdel, 2020). En consecuencia y, como criterios de inclusión, se definieron perfiles con las siguientes características: a) Cuentas con más de un millón de seguidores; b) Mujeres; c) Sector moda; d) Edad máxima de 35 años; e) América Latina/España.…”
Section: Muestraunclassified
“…By using machine learning and artificial intelligence methods (especially artificial neural networks) it is possible to effectively solve such problems as classification, forecasting, anomaly detection and clustering (DiFranco & Santurro, 2020;Young et al, 2018). In the intelligent analysis of social networks, various types of neural networks are used, the most popular of which are classical perceptions (Jabłońska & Zajdel, 2020) and various types recurrent neural networks (RNN, GRU) (Balakrishnan & Geetha, 2020;Yang et al, 2017). It is important to note that recently developed deep learning neural networks based on graphs (GCNs) (Tan et al, 2019) are beginning to use quite effectively.…”
Section: Introductionmentioning
confidence: 99%