TY - GEN
T1 - Optimization Learning Approaches in Predicting Facebook Metrics from User Posts Behavior
AU - Yuliazmi,
AU - Purwitasari, Diana
AU - Sumpeno, Surya
AU - Purnomo, Mauridhi Hery
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021/7/21
Y1 - 2021/7/21
N2 - 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.
AB - 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.
KW - Facebook
KW - Particle Swarm Optimization Extreme Learning Machine (PSO-ELM)
KW - Support Vector Regression
KW - behavior
KW - social media
UR - http://www.scopus.com/inward/record.url?scp=85114644890&partnerID=8YFLogxK
U2 - 10.1109/ISITIA52817.2021.9502245
DO - 10.1109/ISITIA52817.2021.9502245
M3 - Conference contribution
AN - SCOPUS:85114644890
T3 - Proceedings - 2021 International Seminar on Intelligent Technology and Its Application: Intelligent Systems for the New Normal Era, ISITIA 2021
SP - 426
EP - 431
BT - Proceedings - 2021 International Seminar on Intelligent Technology and Its Application
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2021 International Seminar on Intelligent Technology and Its Application, ISITIA 2021
Y2 - 21 July 2021 through 22 July 2021
ER -