The influence of fake accounts on sentiment analysis related to COVID-19 in Indonesia

Rivanda Putra Pratama, Aris Tjahyanto*

*Corresponding author for this work

Research output: Contribution to journalConference articlepeer-review

11 Citations (Scopus)

Abstract

Cases of the spread of COVID-19 that continue to increase in Indonesia have made the level of public satisfaction with the government in dealing with this virus fairly low. One way to measure the level of community satisfaction is by analyzing social media. Sentiment analysis can be used to analyze feedback from the public. Research related to sentiment analysis has been mostly carried out, but so far, it has focused more on opinions contained in sentences and comments and has not considered the subject of the account that posted it. On the other hand, the use of fake accounts or bots on social media is becoming more and more prevalent, so that the credibility of opinion makers is reduced. Based on these problems, this research conducted several experiments related to sentiment analysis using a machine learning approach and fake account categories to see the influence of fake accounts on sentiment analysis. The data used in this research were taken from social media Twitter. The results showed that there was an influence from fake accounts that can reduce the performance of sentiment classification. The experimental results of the two algorithms also prove that the Support Vector Machine algorithm has a better performance than the Naïve-Bayes algorithm for this case with the highest Accuracy value of 80.6%. In addition, the results of the sentiment visualization showed that there was an influence from fake accounts which actually leads to positive sentiment although it is not significant.

Original languageEnglish
Pages (from-to)143-150
Number of pages8
JournalProcedia Computer Science
Volume197
DOIs
Publication statusPublished - 2021
Event6th Information Systems International Conference, ISICO 2021 - Virtual, Online, Italy
Duration: 7 Aug 20218 Aug 2021

Keywords

  • Fake account
  • Machine learning
  • Sentiment analysis
  • Twitter

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