Prediction of Facebook Post Metrics using Machine Learning
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.
Keywords
Cite
@article{arxiv.1805.05579,
title = {Prediction of Facebook Post Metrics using Machine Learning},
author = {Emmanuel Sam and Sergey Yarushev and Sebastián Basterrech and Alexey Averkin},
journal= {arXiv preprint arXiv:1805.05579},
year = {2018}
}
Comments
This is a draft version of a manuscript accepted in the XXI International Conference on Soft Computing and Measurement (SCM'2018), Saint Petersburg, Russia, May 23 - 25, 2018 (http://scm.eltech.ru/. It contains 4 pages