Buzzer Detection on Indonesian Twitter using SVM and Account Property Feature Extension

Abstract

The rapid use of Twitter social media in recent times has an impact on the faster dissemination of disinformation which is very dangerous to followers. Detection of disinformation is very important to do and can be done manually by conducting in-depth information analysis. But given the huge amount of information, this approach is less effective. Another, more effective approach is to use a machine learning-based approach. Several studies on hoax information detection based on machine learning have been carried out where some studies analyze the content of a tweet and some others analyze hashtags which are the context of a tweet. The feature usually used to analyze hashtag sentiment data is the property feature of the creator's account. The creator accounts of disinformation are called buzzer accounts. This research proposes account property feature expansion of buzzer accounts combined with the SVM classifier which in several previous similar studies has a very good performance to detect the buzzer hashtag. The experimental results show that expanding the proposed feature can increase SVM's performance in detecting hashtag buzzers by more than 24% compared to using the baseline feature, and the average F1 score obtained from the combination of methods is 84%.