Racist or Sexist Meme? Classifying Memes beyond Hateful (2021)

Abstract: Memes are the combinations of text and images that are often humorous in nature. But, that may not always be the case, and certain combinations of texts and images may depict hate, referred to as hateful memes. This work presents a multimodal pipeline that takes both visual and textual features from memes into account to (1) identify the protected category (e.g. race, sex etc.) that has been attacked; and (2) detect the type of attack (e.g. contempt, slurs etc.). Our pipeline uses state-of-the-art pre-trained visual and textual representations, followed by a simple logistic regression classifier. We employ our pipeline on the Hateful Memes Challenge dataset with additional newly created fine-grained labels for protected category and type of attack. Our best model achieves an AUROC of 0.96 for identifying the protected category, and 0.97 for detecting the type of attack. We release our code at https://github.com/harisbinzia/HatefulMemes

## introduction an internet meme (or simply "meme" for the remainder of this paper) is a virally transmitted image embellished with text. it usually shares pointed commentary on cultural symbols, social ideas, or current events (gil, 2020). in the past few years there has been a surge in the popularity of memes on social media platforms. instagram, which is a popular photo and video sharing social networking service recently revealed that over 1 million posts mentioning "meme" are shared on instagram each day. 1 we warn the reader that this paper contains content that is racist, sexist and offensive in several ways. although memes are often funny and used mostly for humorous purposes, recent research suggests that they can also be used to disseminate hate @xcite and can therefore emerge as a multimodal expression of online hate speech. hateful memes target certain groups or individuals based on their race @xcite and gender @xcite , among many other protected categories, thus causing harm at both an individual and societal level. an example hateful meme is shown in figure 1 . at the scale of the internet, it is impossible to manually inspect every meme. hence, we posit that it is important to develop (semi-)automated systems that can detect hateful memes. however, detecting hate in multimodal forms (such as memes) is extremely challenging and requires a holistic understanding of the visual and textual material. in order to accelerate research in this area and develop systems capable of detecting hateful memes, facebook recently launched the hateful memes challenge @xcite . the challenge introduced a new annotated dataset of around 10k memes tagged for hatefulness (i.e. hateful vs. nothateful). the baseline results show a substantial dif-ference in the performance of unimodal and multimodal systems, where the latter still perform poorly compared to human performance, illustrating the difficulty of the problem. more recently, a shared task on hateful memes was organized at the workshop on online abuse and harms foot_1 (woah), where the hateful memes dataset @xcite was presented with additional newly created fine-grained labels foot_2 for the protected category that has been attacked (e.g. race, sex, etc.), as well as the type of attack (e.g. contempt, slurs, etc.). this paper presents our multimodal pipeline based on pre-trained visual and textual representations for the shared task on hateful memes at woah. we make our code publicly available to facilitate further research. foot_3 ## problem statement there are two tasks with details as follows: ## dataset the dataset consists of 9,540 fine-grained annotated memes and is imbalanced, with large number of non-hateful memes and relatively small number of hateful ones. the details of different splits foot_4 are given in the for the evaluation, we use the standard auroc metric. ## model & results this section describes our model, the visual & textual embeddings, as well as the results. ## conclusion & future work this paper has presented our pipeline for the multilabel hateful memes classification shared task organized at woah. we show that our multimodal classifiers outperform unimodal classifiers. our best multimodal classifier achieves an auroc of 0.96 for identifying the protected category, and 0.97 for detecting the attack type. although we trained our classifier on language agnostic representations, it was only tested on a dataset of english memes. as a future direction, we plan to extend our work auroc type embedding task a task b unimodal ctxt 0.56 0.67 laser 0.88 0.91 labse 0.89 0.92 cimg 0.93 0.94 multimodal cimg + ctxt 0.95 0.96 cimg + laser 0.94 0.95 cimg + labse 0.94 0.95 cimg + ctxt + laser + labse 0.96 0.97