Analysis of Mental Health During the Covid-19 Pandemic in Indonesia Using Twitter Data

EasyChair Preprint 6730

Abstract

Covid-19, which has infected Indonesia, has had a significant impact on Indonesia in various sectors and has a direct psychological impact on the entire community, such as a fear attack, anxiety, stress, and depression. Not being able to meet friends, study and work from home, the existence of the PSBB policy, the large number of news and hoaxes about Covid-19, and worrying about being infected are some of the factors that can cause psychological problems. At this time, social media was helpful to get the latest information, share various content, tell stories, and express opinions or thoughts. This study will conduct a classification and analysis related to mental health during the pandemic using tweets shared by Indonesian users and then compare the algorithms, which are Naïve Bayes, SVM, Logistic Regression, and Random Forest. From the labeling process, 612 tweets indicate psychological problems, and 168 tweets indicate anxiety problems. This study succeeded in building two classification models to detect psychological problems and anxiety problems. Model 1 was built using the Naïve Bayes because Naïve Bayes algorithm has the highest results of all evaluations with 74.36% accuracy, 74.28% precision, 74.35% recall, and 74.30% f1-score. While model 2 was built using SVM algorithm because SVM has the highest score for accuracy with 76.42%, precision with 74.91%, and f1-score with 75.19%.

Keyphrases: COVID-19, Twitter, machine learning, mental health

@booklet{EasyChair:6730,
  author    = {Nurriasih Fatimah and Indra Budi and Aris Budi Santoso and Prabu Kresna Putra},
  title     = {Analysis of Mental Health During the Covid-19 Pandemic in Indonesia Using Twitter Data},
  howpublished = {EasyChair Preprint 6730},
  year      = {EasyChair, 2021}}