Predicting consumers engagement on Facebook based on what and how companies write

TL;DR

This paper presents a method to predict Facebook post engagement by analyzing content, style, behavioral attributes, and metadata, validated on 14,000 posts from public pages.

Contribution

It introduces a comprehensive approach combining multiple attributes to accurately forecast social media engagement, which is a novel integration for this purpose.

Findings

Content and behavioral attributes significantly improve prediction accuracy

The model effectively predicts engagement across diverse posts

Metadata also contributes to the prediction performance

Abstract

Engaged costumers are a very import part of current social media marketing. Public figures and brands have to be very careful about what to post online. That is why the need for accurate strategies for anticipating the impact of a post written for an online audience is critical to any public brand. Therefore, in this paper, we propose a method to predict the impact of a given post by accounting for the content, style, and behavioral attributes as well as metadata information. For validating our method we collected Facebook posts from 10 public pages, we performed experiments with almost 14000 posts and found that the content and the behavioral attributes from posts provide relevant information to our prediction model.