Antisocial Behavior Monitoring Services of Indonesian Public Twitter Using Machine Learning
Keywords:Twitter antisocial behavior, machine learning, deep learning, feature extraction, multi-class classification
Antisocial behavior is a personality disorder that has characteristics such as repetitive actions that violate social norms, deceit and lying, impulsiveness, irritability and aggression, reckless disregard for the safety of oneself and others, consistently irresponsible, and lack of remorse. The cause can be from various factors, including genetics, psychological conditions, interactions in the environment, and wrong parenting. The impact of antisocial behavior on social life can cause people to tend to be aggressive and take it into action by not having feelings of guilt for their actions. Thus, a monitoring of antisocial behavior disorders is needed so that it can be a warning for the public to be more concerned about the difficulties experienced by each other. The potential gained from the availability of tweet data access from the Twitter API opens up opportunities for monitoring antisocial behavior. By utilizing traditional machine learning and deep learning methods, it can be an opportunity to automate labeling on Twitter data that contains elements of antisocial behavior. Based on the description of the problems and opportunities found, this study proposes a multi-class classification monitoring service to identify public antisocial behavior on Twitter Indonesia using machine learning.