mlsanityData to 7 Oct 2026

Unified and Multilingual Author Profiling for Detecting Haters

arXiv 2109.09233 ↗First posted 19 Sep 2021cs.CL cs.AI cs.LGcited by 4

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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.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 8 of its 29 references that are in the map. First posted on arXiv 19 Sep 2021.

Builds on

8 of 29 references.

References · 8

  1. 2017Attention Is All You Needcited by 59,932
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  3. 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,049
  4. 2019DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lightercited by 4,243
  5. 2020Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillationcited by 591
  6. 2020AdapterHub: A Framework for Adapting Transformerscited by 441
  7. 2018Spread of hate speech in online social mediacited by 59
  8. 2020Fake News Spreader Detection on Twitter using Character N-Grams. Notebook for PAN at CLEF 2020cited by 2

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 1; 2023: 2; 2024: 0; 2025: 0; 2026: 1.

4 papers.