B angla A buse M eme: A Dataset for B engali Abusive Meme Classification (2023)

Abstract: The dramatic increase in the use of social media platforms for information sharing has also fueled a steep growth in online abuse. A simple yet effective way of abusing individuals or communities is by creating memes, which often integrate an image with a short piece of text layered on top of it. Such harmful elements are in rampant use and are a threat to online safety. Hence it is necessary to develop efficient models to detect and flag abusive memes. The problem becomes more challenging in a low-resource setting (e.g., Bengali memes, i.e., images with Bengali text embedded on it) because of the absence of benchmark datasets on which AI models could be trained. In this paper we bridge this gap by building a Bengali meme dataset. To setup an effective benchmark we implement several baseline models for classifying abusive memes using this dataset. We observe that multimodal models that use both textual and visual information outperform unimodal models. Our best-performing model achieves a macro F1 score of 70.51. Finally, we perform a qualitative error analysis of the misclassified memes of the best-performing text-based, image-based and multimodal models.

## introduction in recent times, internet memes have become commonplace across social media platforms. a meme is an idea usually composed of an image and a short piece of text on top of it, entrenched as part of the image @xcite . memes are typically jokes, but in the current internet culture they can be far beyond jokes. people make memes as they are free of cost but can impress others and help to accrue social capital. bad actors however use memes to threaten and abuse individuals or specific target communities. such memes are collectively known as abusive memes on social media. owing to their naturally viral nature, such abusive memes can ignite social tensions, tarnish the reputation of the platforms that host them @xcite , and may severely affect the victims psychologically @xcite . therefore, controlling the spread of such abusive memes is necessary and the first step toward this is to efficiently detect them. in the past few years a number of studies have tried to take initiative to detect and control the effect of abusive memes on different social media platforms. however most of these are concentrated around memes that have english as the text component @xcite @xcite . further, several multimodal vision language models have been explored, but again they are limited to english. efforts in other languages is low and this is particularly true for resource-impoverished languages like bengali (aka bangla). in this paper we consider the problem of detecting bangla abusive memes (see figure 1 for examples) across social media platforms. the motivation comes from the fact that bangla is the seventh most spoken language (ber) in the world having over 210 million speakers, with around 100 million bengali speakers in bangladesh and about 85 million speakers in india. it is the official language of bangladesh and one of the officially recognized languages in the constitution of india. besides bangladesh and india, bengali is spoken in many other countries, including the united kingdom, the united states, and several countries in the middle east @xcite . the other important reason for considering bangla is linked to the several smearing incidents in bangladesh and india, such as slandering moves against famous political leaders, celebrities, and social media personalities, online anti-religious propaganda, and cyber harassment @xcite that have inflicted the online world. our key contributions in this paper are as follows. abusive meme datasets: in an effort to develop resources for multimodal abusive meme detection, several datasets have been constructed. sabat et al. (2019) created a dataset of 5,020 memes for hate meme detection by crawling google images. gomez et al. (2020) contributed another multimodal dataset (mmhs150k) for hate speech detection which has 150k posts collected from twitter. similarly, chandra et al. (2021) developed a dataset for detecting antisemitism in multimodal memes by crawling datasets from both twitter and gab. suryawanshi et al. (2020) created another dataset of 743 memes annotated as offensive or not-offensive. the dataset has been built by leveraging the memes related to the 2016 us presidential election. pramanick et al. (2021a) also built another dataset to detect harmful memes containing around 3.5k memes related to covid-19. in addition, to boost the research around multimodal abusive memes, several shared tasks have been organized. facebook ai (kiela et al., 2020) introduced another dataset of 10k+ posts comprising labeled hateful and non-hateful as part of the hateful memes challenge. there are two previous works on bengali meme detection. the first work, conducted by karim et al. (2022), involved extending the bengali hate speech dataset (karim et al., 2020) by labeling 4,500 memes. hossain et al. (2022) developed a dataset of 4,158 memes with bengali and code-mixed captions. in the former work the authors have not made the data public (although they put a github link in the paper albeit without the link to the dataset). further the data collection and annotation process is not discussed in detail, the target communities are not labeled and the interannotator agreement is also not reported. the latter work does not make the data public. here again the target labels are missing making it difficult to ascertain how diverse the data is. in our work, we aimed to overcome the drawbacks of these previous studies by not only labeling memes as abusive or non-abusive but also further annotating them with labels such as vulgar, sarcasm, sentiment, and the targeted community. this richness of our data provides a more holistic understanding of the dynamics of bengali memes. plethora of models, including bert, vgg16, and mlp. @xcite explored several models such as fcm, inception-v3, lstm, etc. with the advancement of the vision language models, several approaches, such as visualbert (li et al., 2020), vilbert @xcite , mmbt @xcite , uniter @xcite , and others @xcite @xcite have been explored. recent studies have also attempted several data augmentation techniques @xcite to improve classification performance. while previous methods were proposed for abusive speech detection for the english language, we focus on detecting bengali abusive memes. ## dataset creation here we discuss the data collection strategy, annotation guidelines, and the statistics of the annotated dataset. data collection and sampling: in order to construct the abusive meme dataset, we first build a lexicon of 69 offensive bengali terms. this lexicon comprises words that target individuals or different protected communities. in addition, we include the targeted community's name in the lexicon. the choice is made to extract random hateful/offensive memes about a community that do not contain abusive words. then, using these lexicons, similar to pramanick et al. (2021a) we perform a keywordbased web search on various platforms such as google image, bing, etc. we also scrape various pages and groups from facebook and instagram. to download the images, we used an extension 2 of google chrome. unlike the hateful memes 2 https://download-all-images.mobilefirst.me/ challenge @xcite , which contributed synthetically generated memes, our dataset consists of memes curated from the real-world. once the images are downloaded, we apply the following data sampling techniques before moving forward with the actual annotation. we remove the memes having no text or having text in different languages other than bengali. we also remove memes having very low resolution where the meme text is unreadable. ## methodology this section presents a suite of models we have used for bengali abusive meme detection ranging from text-based, image-based, and, finally, fusionbased multimodal models. the techniques are dis-cussed below. text-based classification: here, the idea is to classify the memes solely based on the embedded text presented as part of the image. we use easy-ocr foot_3 to extract the texts of a meme and apply standard pre-processing techniques on the post to remove urls, mentions, and special characters. we experiment with the following transformer-based models for the classification, m-bert @xcite , muril @xcite , xlm-roberta @xcite and banglabert @xcite . image-based classification: here we pose the problem as an image classification task and experiment with the following pre-trained models: vgg16 (simonyan and zisserman, 2014), resnet-152 @xcite , vision transformer(vit) @xcite and visual attention network(van) @xcite . ## discussion the abusive meme detection model can be very effective in content moderation, especially when there is a surge in multimedia content over social media platforms. the auto-discard technique has become increasingly valuable, where hateful content is automatically discarded from posting once flagged by an abuse detection model. other approaches include alerting the user about a flagged post or lowering the visibility of the flagged post. finally, moderators can be automatically informed to counter the flagged post so that it dilutes or nullifies the harmful effect of the post. in order to have such an effective flagging, some challenges include the continuous availability of training data, computational costs, and the imperative of upholding the model's sustained accuracy over time. ## conclusion this paper presents a new benchmark dataset for bengali abusive meme detection. our dataset comprises more than 4k memes collected using keyword-based web search on various platforms. we further evaluated the performance of different classifiers ranging from text-based, image-based, and multimodal models. our experiments show that using both textual and visual modalities together helps to enhance the performance by learning better representation of the memes. this is further demonstrated by our in-depth error analysis.