Classification of Radicalism Content from Twitter Written in Indonesian Language using Long Short Term Memory

Abstract

Twitter was one of the most influential social media among users. It might give an either positive or negative impact. One of the negative impacts was the presence of radicalism content. In Indonesia radicalism was often connected to the issue of SARA (ethnicity, religion, race, and intergroup relations). It remained a public issue, requiring an analysis to process information related to radicalism. The research aimed to classify radical contents. The classification based on the types of radicalism and non-radicalism. Data were classified using LSTM. In finding higher accuracy, word2vec was used to transform words into vectors. The accuracy showed using LSTM method was compared with that obtained using SVM and k-NN. The two latest methods were the methods used by previous researchers regarding Indonesian radical contents of Twitter. Referring to the findings, LSTM showed higher accuracy 81.60%.