# Identifying Offensive Posts and Targeted Offense from Twitter

arXiv: https://arxiv.org/abs/1904.09072 · HTML: https://mlsanity.com/paper/1904.09072/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Apr 2019.

## Built on by (most cited first, 4 of 4)

- [On The Robustness of Offensive Language Classifiers](https://mlsanity.com/paper/2203.11331/) (2022)
- [HLE-UPC at SemEval-2021 Task 5: Multi-Depth DistilBERT for Toxic Spans Detection](https://mlsanity.com/paper/2104.00639/) (2021)
- [Suum Cuique: Studying Bias in Taboo Detection with a Community Perspective](https://mlsanity.com/paper/2203.11401/) (2022)
- [Improved two-stage hate speech classification for twitter based on Deep Neural Networks](https://mlsanity.com/paper/2206.04162/) (2022)

## Abstract (arXiv)

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.