Meme Classification

The Hateful Memes data set is a multimodal dataset for hateful meme detection (image + text) that contains 10,000+ new multimodal examples created by Facebook AI. Images were licensed from Getty Images so that researchers can use the data set to support their work.

Harm-C is a dataset for detecting harmful memes related to Covid-19.

HarMeme is a benchmark dataset for hateful meme classification containing 3, 544 memes related to COVID-19 collected from the Internet

Harm-P contains around 3,000 memes related to US politics.

Existing hate speech datasets contain only textual data. We create a new manually annotated multimodal hate speech dataset formed by 150,000 tweets, each one of them containing text and an image. We call the dataset MMHS150K.

Social media are interactive platforms that facilitate the creation or sharing of information, ideas or other forms of expression among people. This exchange is not free from offensive, trolling or malicious contents targeting users or communities. One way of trolling is by making memes, which in most cases combines an image with a concept or catchphrase. The challenge of dealing with memes is that they are region-specific and their meaning is often obscured in humour or sarcasm. To facilitate the computational modelling of trolling in the memes for Indian languages, we created a meme dataset for Tamil (TamilMemes). We annotated and released the dataset containing suspected trolls and not-troll memes. In this paper, we use the a image classification to address the difficulties involved in the classification of troll memes with the existing methods. We found that the identification of a troll meme with such an image classifier is not feasible which has been corroborated with precision, recall and F1-score.

PrideMM comprising 5,063 text-embedded images associated with the LGBTQ+ Pride movement

Introudced from Multimodal Meme Dataset (MultiOFF) for Identifying Offensive Content in Image and Text