@inproceedings{jigar-etal-2024-detecting,
    title = "Detecting Hate Speech in {A}mharic Using Multimodal Analysis of Social Media Memes",
    author = "Jigar, Melese Ayichlie  and
      Ayele, Abinew Ali  and
      Yimam, Seid Muhie  and
      Biemann, Chris",
    editor = "Kumar, Ritesh  and
      Ojha, Atul Kr.  and
      Malmasi, Shervin  and
      Chakravarthi, Bharathi Raja  and
      Lahiri, Bornini  and
      Singh, Siddharth  and
      Ratan, Shyam",
    booktitle = "Proceedings of the Fourth Workshop on Threat, Aggression {\&} Cyberbullying @ LREC-COLING-2024",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.trac-1.10",
    pages = "85--95",
    abstract = "In contemporary society, the proliferation of hate speech is increasingly prevalent across various social media platforms, with a notable trend of incorporating memes to amplify its visual impact and reach. The conventional text-based detection approaches frequently fail to address the complexities introduced by memes, thereby aggravating the challenges, particularly in low-resource languages such as Amharic. We develop Amharic meme hate speech detection models using 2,000 memes collected from Facebook, Twitter, and Telegram over four months. We employ native Amharic speakers to annotate each meme using a web-based tool, yielding a Fleiss{'} kappa score of 0.50. We utilize different feature extraction techniques, namely VGG16 for images and word2Vec for textual content, and build unimodal and multimodal models such as LSTM, BiLSTM, and CNN. The BiLSTM model shows the best performance, achieving 63{\%} accuracy for text and 75{\%} for multimodal features. In image-only experiments, the CNN model achieves 69{\%} in accuracy. Multimodal models demonstrate superior performance in detecting Amharic hate speech in memes, showcasing their potential to address the unique challenges posed by meme-based hate speech on social media.",
}

In contemporary society, the proliferation of hate speech is increasingly prevalent across various social media platforms, with a notable trend of incorporating memes to amplify its visual impact and reach. The conventional text-based detection approaches frequently fail to address the complexities introduced by memes, thereby aggravating the challenges, particularly in low-resource languages such as Amharic. We develop Amharic meme hate speech detection models using 2,000 memes collected from Facebook, Twitter, and Telegram over four months. We employ native Amharic speakers to annotate each meme using a web-based tool, yielding a Fleiss{'} kappa score of 0.50. We utilize different feature extraction techniques, namely VGG16 for images and word2Vec for textual content, and build unimodal and multimodal models such as LSTM, BiLSTM, and CNN. The BiLSTM model shows the best performance, achieving 63{\%} accuracy for text and 75{\%} for multimodal features. In image-only experiments, the CNN model achieves 69{\%} in accuracy. Multimodal models demonstrate superior performance in detecting Amharic hate speech in memes, showcasing their potential to address the unique challenges posed by meme-based hate speech on social media.
Jigar, Melese Ayichlie Ayele, Abinew Ali Yimam, Seid Muhie Biemann, Chris
Detecting Hate Speech in Amharic Using Multimodal Analysis of Social Media Memes