Detecting Hate Speech Spreaders on Twitter using LSTM and BERT in English and Spanish

Moshe Uzan, Yaakov HaCohen-Kerner

Research output: Contribution to journalConference articlepeer-review

6 Scopus citations


In this paper, we describe our submissions for PAN at CLEF 2021 contest. We tackled the subtask “Profiling Hate Speech Spreaders on Twitter”. We developed different models for English and Spanish languages, using classic machine learning methods like Support Vector Classifier, Multi-Layer Perceptron, Logistic Regression, Random Forest, Ada-Boost Classifier and K-Neighbors Classifier to more recent deep learning methods like BERT and Bidirectional LSTM.

Original languageEnglish
Pages (from-to)2178-2185
Number of pages8
JournalCEUR Workshop Proceedings
StatePublished - 2021
Event2021 Working Notes of CLEF - Conference and Labs of the Evaluation Forum, CLEF-WN 2021 - Virtual, Bucharest, Romania
Duration: 21 Sep 202124 Sep 2021

Bibliographical note

Publisher Copyright:
© 2021 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).


  • Ada-boost classifier
  • Author profiling
  • BERT
  • English
  • Hate speech
  • LSTM
  • Logistic regression
  • MLP
  • Random forest
  • SVM
  • Spanish
  • Twitter


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