Prediction of Facebook Post Metrics using Machine Learning

2018/05/15 by Emmanuel Sam, Sam, Emmanuel, Sergey Yarushev +5
Computer Science · Physics and Astronomy · #Complex Network Analysis Techniques #FOS: Computer and information sciences #Machine Learning (cs.LG) #Neural Networks and Reservoir Computing #Opinion Dynamics and Social Influence #Social and Information Networks (cs.SI)

paper · pdf · doi:10.48550/arxiv.1805.05579

openalex publication_date 2018/05/15 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

In this short paper, we evaluate the performance of three well-known Machine Learning techniques for predicting the impact of a post in Facebook. Social medias have a huge influence in the social behaviour. Therefore to develop an automatic model for predicting the impact of posts in social medias can be useful to the society. In this article, we analyze the efficiency for predicting the post impact of three popular techniques: Support Vector Regression (SVR), Echo State Network (ESN) and Adaptive Network Fuzzy Inject System (ANFIS). The evaluation was done over a public and well-known benchmark dataset.

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