Identifying Offensive Posts and Targeted Offense from Twitter

2019/04/19 by Haimin Zhang, Debanjan Mahata, Zhang, Haimin +12
Computer Science · #Computation and Language (cs.CL) #FOS: Computer and information sciences #Hate Speech and Cyberbullying Detection

paper · pdf · doi:10.48550/arxiv.1904.09072

openalex publication_date 2019/04/19 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

In this paper we present our approach and the system description for Sub-task A and Sub Task B of SemEval 2019 Task 6: Identifying and Categorizing Offensive Language in Social Media. Sub-task A involves identifying if a given tweet is offensive or not, and Sub Task B involves detecting if an offensive tweet is targeted towards someone (group or an individual). Our models for Sub-task A is based on an ensemble of Convolutional Neural Network, Bidirectional LSTM with attention, and Bidirectional LSTM + Bidirectional GRU, whereas for Sub-task B, we rely on a set of heuristics derived from the training data and manual observation. We provide detailed analysis of the results obtained using the trained models. Our team ranked 5th out of 103 participants in Sub-task A, achieving a macro F1 score of 0.807, and ranked 8th out of 75 participants in Sub Task B achieving a macro F1 of 0.695.

Related

• Combination of multiple Deep Learning architectures for Offensive Language Detection in Tweets
• Identifying and Categorizing Offensive Language in Social Media
• Duluth at SemEval-2019 Task 6: Lexical Approaches to Identify and\n Categorize Offensive Tweets
• Towards NLP with Deep Learning: Convolutional Neural Networks and\n Recurrent Neural Networks for Offensive Language Identification in Social\n Media
• SemEval-2019 Task 6: Identifying and Categorizing Offensive Language in\n Social Media (OffensEval)