# UM-IU@LING at SemEval-2019 Task 6: Identifying Offensive Tweets Using BERT and SVMs

arXiv: https://arxiv.org/abs/1904.03450 · HTML: https://mlsanity.com/paper/1904.03450/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Apr 2019.

## Built on by (most cited first, 7 of 7)

- [Detection of Hate Speech using BERT and Hate Speech Word Embedding with Deep Model](https://mlsanity.com/paper/2111.01515/) (2021)
- [Down the Rabbit Hole: Detecting Online Extremism, Radicalisation, and Politicised Hate Speech](https://mlsanity.com/paper/2301.11579/) (2023)
- [Ceasing hate withMoH: Hate Speech Detection in Hindi-English Code-Switched Language](https://mlsanity.com/paper/2110.09393/) (2021)
- [Offensive Language Detection with BERT-based models, By Customizing Attention Probabilities](https://mlsanity.com/paper/2110.05133/) (2021)
- [LG4AV: Combining Language Models and Graph Neural Networks for Author Verification](https://mlsanity.com/paper/2109.01479/) (2021)
- [An Automated Knowledge Mining and Document Classification System with Multi-model Transfer Learning](https://mlsanity.com/paper/2106.12744/) (2021)
- [Sentiment Analysis in SemEval: A Review of Sentiment Identification Approaches](https://mlsanity.com/paper/2503.10457/) (2025)

## Abstract (arXiv)

This paper describes the UM-IU@LING's system for the SemEval 2019 Task 6: OffensEval. We take a mixed approach to identify and categorize hate speech in social media. In subtask A, we fine-tuned a BERT based classifier to detect abusive content in tweets, achieving a macro F1 score of 0.8136 on the test data, thus reaching the 3rd rank out of 103 submissions. In subtasks B and C, we used a linear SVM with selected character n-gram features. For subtask C, our system could identify the target of abuse with a macro F1 score of 0.5243, ranking it 27th out of 65 submissions.