Hate Speech in Pixels: Detection of Offensive Memes towards Automatic Moderation
By Benet Oriol Sabat, Cristian Canton-Ferrer, Xavier Giró-i-Nieto. 2019
96 citations recorded by the source corpus.
This work addresses the challenge of hate speech detection in Internet memes, and attempts using visual information to automatically detect hate speech, unlike any previous work of our knowledge. Memes are pixel-based multimedia documents that contain photos or illustrations together with phrases which, when combined, usually adopt a funny meaning. However, hate memes are also used to spread hate through social networks, so their automatic detection would help reduce their harmful societal impact. Our results indicate that the model can learn to detect some of the memes, but that the task is far from being solved with this simple architecture. While previous work focuses on linguistic hate speech, our experiments indicate how the visual modality can be much more informative for hate speech detection than the linguistic one in memes. In our experiments, we built a dataset of 5,020 memes to train and evaluate a multi-layer perceptron over the visual and language representations, whether independently or fused. The source code and mode and models are available this https URL .
Subfields: Artificial Intelligence, Natural Language Processing, Signal Processing
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Douwe Kiela
- Valerio Basile
- Viviana Patti
- Vedanuj Goswami
- Amanpreet Singh
- Pratik Ringshia
- Mohit Bansal
- Davide Testuggine
In Natural Language Processing
- Valerio Basile
- Viviana Patti
- Douwe Kiela
- Amanpreet Singh
- Pratik Ringshia
- Vedanuj Goswami
- Davide Testuggine
- Aravind Mohan
In Signal Processing
Citations in the corpus
- The Hateful Memes Challenge: Detecting Hate Speech in Multimodal Memes (2020, background): “[62] recently found in a preliminary study that the visual modality can be more informative for detecting hate speech in memes than the text.”
- Resources and benchmark corpora for hate speech detection: a systematic review (2020, methodology): “Sabat et al. (2019) (SCG)—5020 memes, containing images and words (in English), collected from Google Images and from Reddit.”