# Unified and Multilingual Author Profiling for Detecting Haters

arXiv: https://arxiv.org/abs/2109.09233 · HTML: https://mlsanity.com/paper/2109.09233/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 29 references that are in the map.
- First posted on arXiv 19 Sep 2021.

## Builds on (most cited first, 8 of 8)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://mlsanity.com/paper/1910.01108/) (2019)
- [Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation](https://mlsanity.com/paper/2004.09813/) (2020)
- [AdapterHub: A Framework for Adapting Transformers](https://mlsanity.com/paper/2007.07779/) (2020)
- [Spread of hate speech in online social media](https://mlsanity.com/paper/1812.01693/) (2018)
- [Fake News Spreader Detection on Twitter using Character N-Grams. Notebook for PAN at CLEF 2020](https://mlsanity.com/paper/2009.13859/) (2020)

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

- [PART: Pre-trained Authorship Representation Transformer](https://mlsanity.com/paper/2209.15373/) (2022)
- [Understanding writing style in social media with a supervised contrastively pre-trained transformer](https://mlsanity.com/paper/2310.11081/) (2023)
- [GerAV: Towards New Heights in German Authorship Verification using Fine-Tuned LLMs on a New Benchmark](https://mlsanity.com/paper/2601.13711/) (2026)
- [AI-UPV at EXIST 2023 -- Sexism Characterization Using Large Language Models Under The Learning with Disagreements Regime](https://mlsanity.com/paper/2307.03385/) (2023)

## Abstract (arXiv)

This paper presents a unified user profiling framework to identify hate speech spreaders by processing their tweets regardless of the language. The framework encodes the tweets with sentence transformers and applies an attention mechanism to select important tweets for learning user profiles. Furthermore, the attention layer helps to explain why a user is a hate speech spreader by producing attention weights at both token and post level. Our proposed model outperformed the state-of-the-art multilingual transformer models.