Lawyering Social: Navigating Legal Issues on Social Media Posts with a Low-cost Data Algorithm

Along with the increasing use of online communication during the current Covid-19 pandemic, people have become more active in the social media platforms like Facebook and Twitter, whether it be through by posting their content online or by reading the posts of others. Undoubtedly, it has a negative impact in the form of cybercrimes, especially when public understanding of cyber law is still low. To address this issue, we propose an approach to identifying cybercrime on social media posts, with case studies on Indonesian cybercrime law. The main challenge in solving the problem is the use of informal language and non-standard writing. Therefore, we exploit the informative value of terms in that post by paying attention to standard patterns in Indonesian grammar and writing structure with a classifier based on the voting ensemble learning model. The experimental results show that the proposed approach significantly outperformed baselines, with an accuracy of more than 90%. Thus, it proves the effectiveness of the approach in generalizing the content of social media posts according to the category of cybercrime law.