Optimization Learning Approaches in Predicting Facebook Metrics from User Posts Behavior

The current situation of the Covid-19 pandemic has an impact on increasing the use of social media. In various aspects, social media has a role in human activities, especially in working-age groups. Breaking the stigma that social media interferes with someones’ performance, we argue that using social media actually supports someones’ work activities. In this preliminary study, we explore post behavior on Facebook social media networks for understanding user productivity. The dataset used in this study is gained from an online survey with the respondent of social media users over age 15 years old. Later on, based on surveys’ responses, web scraping of Facebook post were set to complete the data needed. From the dataset, demographic features, metadata-based features, and behavior-based features are examined with some regression algorithms such Support Vector Regression (SVR) and Particle Swarm Optimization Extreme Learning Machine (PSO-ELM). The result from this study is only one feature that positively correlated to almost all other features during the pandemic.