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Related Experiment Video

Updated: Sep 18, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

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Multimodal hate speech detection: a novel deep learning framework for multilingual text and images.

Furqan Khan Saddozai1, Sahar K Badri2, Daniyal Alghazzawi2

  • 1Gomal Research Institute of Computing, Faculty of Computing, Gomal University, D.I.Khan, KP, Pakistan.

Peerj. Computer Science
|June 26, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a deep learning framework for detecting multimodal hate speech in Urdu and English tweets. The model effectively classifies hate speech using text and images, outperforming other methods.

Keywords:
BiLSTMDeep learningEfficientNetB1Hate speechImageMultilingualMultimodalUrdu-English

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Area of Science:

  • Natural Language Processing
  • Computer Vision
  • Social Media Analysis

Background:

  • Social media enables widespread opinion sharing but also facilitates hate speech.
  • Detecting multimodal hate speech in low-resource languages presents significant challenges.

Purpose of the Study:

  • To develop and evaluate a deep learning framework for classifying multimodal hate speech in Urdu-English tweets.
  • To introduce a new dataset, MMHS11K, for multimodal multilingual hate speech detection.

Main Methods:

  • A deep learning framework integrating Bidirectional Long Short-Term Memory (BiLSTM) and EfficientNetB1 was employed.
  • An early fusion strategy combined text and image features for classification.
  • A manually annotated dataset of 11,000 multimodal tweets (MMHS11K) was utilized.

Main Results:

  • The BiLSTM+EfficientNetB1 model achieved an F1-score of 81.2% for Urdu tweets and 75.5% for English tweets.
  • The proposed multimodal approach outperformed unimodal and baseline multimodal methods.

Conclusions:

  • The developed framework effectively addresses challenges in multilingual and multimodal hate speech detection.
  • This research provides a strong foundation for future advancements in combating online hate speech.