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Detecting Hateful Memes on Social Media

Overview

This project aims to develop a model that can accurately detect hate speech in memes shared on social media platforms. It utilizes a dataset published by Meta, consisting of 10,000 labeled samples of hate speech and non-hate speech memes. The goal is to leverage both text and image analysis to identify hateful memes, based on the assumption that relying solely on either modality would be insufficient.

Data

  • The dataset contains 10,000 labeled samples of memes
  • 64.1% are non-hateful and 35.9% are hateful
  • Includes both the text and image of each meme

Methodology

  • Text features extracted using FastText word embeddings
  • Image features extracted from a ResNet-18 CNN model pre-trained on ImageNet
  • Unimodal models developed using text-only and image-only features
  • Multimodal model combining text and image features
  • Models optimized using Keras Tuner and evaluated on a test set

Results

  • Multimodal model underperformed likely due to challenges in integrating diverse features
  • Visual cues prove more useful than text for hate speech detection in memes

Future Work

  • Collect more diverse data from additional platforms
  • Improve integration of text and image features
  • Apply dimensionality reduction techniques
  • Explore other word embeddings like BERT
  • Enhance context modeling between text and images

Reference