Article
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Preserved in Portico This version is not peer-reviewed
Popularity Prediction of Instagram Posts
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Version 1
: Received: 29 August 2020 / Approved: 30 August 2020 / Online: 30 August 2020 (15:56:34 CEST)
A peer-reviewed article of this Preprint also exists.
Carta, S.; Podda, A.S.; Recupero, D.R.; Saia, R.; Usai, G. Popularity Prediction of Instagram Posts. Information 2020, 11, 453. Carta, S.; Podda, A.S.; Recupero, D.R.; Saia, R.; Usai, G. Popularity Prediction of Instagram Posts. Information 2020, 11, 453.
Abstract
Predicting the popularity of posts on social networks has taken on significant importance in recent years, and several social media management tools now offer solutions to improve and optimize the quality of published content and to enhance the attractiveness of companies and organizations. Scientific research has recently moved in this direction, with the aim of exploiting advanced techniques such as machine learning, deep learning, natural language processing, etc., to support such tools. In light of the above, in this work we aim to address the challenge of predicting the popularity of a future post on Instagram, by defining the problem as a classification task and by proposing an original approach based on Gradient Boosting and feature engineering, which led us to promising experimental results. The proposed approach exploits big data technologies for scalability and efficiency and is general enough to be applied to other social media as well.
Keywords
Popularity Prediction; Classification; Social Network; Machine Learning; Instagram
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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