In this paper, we present the result of our experiment with a variant of 1 Dimensional Convolutional Neural Network (Conv1D) hyper-parameters value. We describe the system entered by the team of Information Retrieval Lab. Universitas Indonesia (3218IR) in the SemEval 2020 Task 11 Sub Task 1 about propaganda span identification in news articles. The best model obtained an F1 score of 0.24 in the development set and 0.23 in the test set. We show that there is a potential for performance improvement through the use of models with appropriate hyper-parameters. Our system uses a combination of Conv1D and GloVe as Word Embedding to detect propaganda in the fragment text level.

In this paper, we present our approach and the results of our participation in OffensEval 2020. There are three sub-tasks in OffensEval 2020 namely offensive language identification (sub-task A), automatic categorization of offense types (sub-task B), and offense target identification (sub-task C). We participated in sub-task A of English OffensEval 2020. Our approach emphasizes on how the emoji affects offensive language identification. Our model used LSTM combined with GloVe pre-trained word vectors to identify offensive language on social media. The best model obtained macro F1-score of 0.88428.

Hate speech and abusive language spreading on social media need to be detected automatically to avoid conflict between citizen. Moreover, hate speech has a target, category, and level that also needs to be detected to help the authority in prioritizing which hate speech must be addressed immediately. This research discusses multi-label text classification for abusive language and hate speech detection including detecting the target, category, and level of hate speech in Indonesian Twitter using machine learning approach with Support Vector Machine (SVM), Naive Bayes (NB), and Random Forest Decision Tree (RFDT) classifier and Binary Relevance (BR), Label Power-set (LP), and Classifier Chains (CC) as the data transformation method. We used several kinds of feature extractions which are term frequency, orthography, and lexicon features. Our experiment results show that in general RFDT classifier using LP as the transformation method gives the best accuracy with fast computational time.