MUTE : A Multimodal Dataset for Detecting Hateful Memes (2022)

Abstract: The exponential surge of social media has enabled information propagation at an unprecedented rate. However, it also led to the generation of a vast amount of malign content, such as hateful memes. To eradicate the detrimental impact of this content, over the last few years hateful memes detection problem has grabbed the attention of researchers. However, most past studies were conducted primarily for English memes, while memes on resource constraint languages (i.e., Bengali) are under-studied. Moreover, current research considers memes with a caption written in monolingual (either English or Bengali) form. However, memes might have code-mixed captions (English+Bangla), and the existing models can not provide accurate inference in such cases. Therefore, to facilitate research in this arena, this paper introduces a multimodal hate speech dataset (named MUTE) consisting of 4158 memes having Bengali and code-mixed captions. A detailed annotation guideline is provided to aid the dataset creation in other resource constraint languages. Additionally, extensive experiments have been carried out on MUTE, considering the only visual, only textual, and both modalities. The result demonstrates that joint evaluation of visual and textual features significantly improves (≈ 3%) the hateful memes classification compared to the unimodal evaluation.

## introduction with the advent of the internet, social media platforms (i.e., facebook, twitter, instagram) significantly impact people's day-to-day life. as a result, many users communicate by posting various content in these mediums. this content includes promulgating hate speech, misinformation, aggressive and offensive views. while some contents are beneficial and enrich our knowledge, they can warning: this paper contains meme examples and words that are offensive in nature. also trigger human emotions that can be considered harmful. among them, the propagation of hateful content can directly or indirectly attack social harmony based on race, gender, religion, nationality, political support, immigration status, and personal beliefs. in recent years, memes have become a popular form of circulating hate speech @xcite . these memes on social media have a pernicious impact on societal polarization as they can instigate hateful crimes. therefore, to restrain the interaction through hateful memes, an automated system is required to quickly flag this content and lessen the inflicted harm to the readers. several works @xcite have accomplished hateful memes detection, most of which were for the english language. unfortunately, no significant studies have been conducted on memes regarding low-resource languages, especially bengali. in recent years an increasing trend has been observed among the people to use bengali memes. as a result, it becomes monumental to identify the bengali hateful memes to mitigate the spread of negativity. however, memes analysis is complicated as it requires a holistic understanding of visual and textual content to infer @xcite . the visual content of the meme alone may not be harmful (figure 1 (a) ). however, it becomes hateful with the incorporation of textual content as it directly attacks religious beliefs. a meme's caption can be written in a mixed language (written in both english and bengali as in figure 1 (b)), which can evade the surveillance engine in those cases. developing a hateful meme detection system for such a scenario is complicated as no standard dataset is available. moreover, developing an intelligent multimodal memes analysis system for bengali is challenging due to the unavailability of benchmark corpus, lack of reliable nlp tools (such as ocr), and the complex morphological structure of the bengali language. therefore, this work aims to develop a multimodal dataset for bangla hate speech detection and investigate various models for the task. the critical contributions of the work are summarized as follows: this section discusses the past studies on hate speech detection based on unimodal (i.e., image or text) and multimodal data. unimodal based hate speech detection: hate speech detection is a prominent research issue among the researchers of different languages (ross et al., 2016; lekea and karampelas, 2018). most hate speech detection works were accomplished based on the text data. for example, both davidson et al. (2017) and waseem and hovy (2016) developed hate speech datasets considering the twitter posts. similarly, de gibert et al. (2018) constructs a dataset that considers the hate speech posted in a white supremacy forum. some works were also accomplished concerning the low resource languages. for instance, fortuna et al. (2019); ousidhoum et al. (2019) introduced hate speech datasets for portuguese and arabic. a few works have also been done on bengali hate speech detection (romim et al., 2021; mathew et al., 2021; ishmam and sharmin, 2019). several architectures have been employed over the last few years to classify hateful texts. earlier researchers widely used recurrent neural network (gröndahl et al., 2018), long short term memory (lstm) network (badjatiya et al., 2017), and the combination of rnn and convolutional neural network (cnn) (zhang et al., 2018b) based methods. recently, bidirectional encoder representations for transformers or bert-based models (pamungkas and patti, 2019; fortuna et al., 2021) are applied and achieved superior performance compared to the deep learningbased methods. multimodal hate speech detection: in contrast to the text-based analysis, in recent years, few pieces of work considered multimodal information (i.e., image + text) for hate speech detection. for example, kiela et al. (2020) introduced a multimodal memes dataset for detecting hate speech. gomez et al. (2020) developed a large scale multimodal dataset (mmhs150k) for detecting hateful memes. in another work, rana and jha (2022) introduced a multimodal hate speech dataset concerning three modalities (i.e., image, text, and audio). however, few works have been accomplished on multimodal hate speech detection for resource constraint languages. perifanos and goutsos (2021) introduced a multimodal dataset for detecting hate speech in greek social media. likewise, karim et al. (2022) developed a dataset for multimodal hate speech detection from bengali memes. several approaches were employed for detecting hate speech using multimodal learning. some researchers exploited the different fusion (sai et al., 2022; perifanos and goutsos, 2021) techniques (i.e., early and late fusion) to evaluate the image and textual features jointly. others have employed bi-linear pooling @xcite and transformer-based methods @xcite ) such as mmbt, vilbert, and visual-bert. despite having the state of the art multimodal transformer architectures, these models have only applied for high resource language (i.e., english). differences with existing researches: though a considerable amount of work has been accomplished on multimodal hate speech detection, only a few works studied low-resource languages (i.e., bengali). in our exploration, we found a work @xcite that detects hate speech from multimodal memes for the bengali language. however, they did not curate the social media memes for analysis; instead artificially created a memes dataset for bengali by conjoining the hateful texts into various images. moreover, the current works overlooked the memes containing captions written cross-lingually. considering these drawbacks, the proposed research differs from the existing studies in three ways: (i) develops a multimodal hate speech dataset (i.e., mute) for bengali considering the internet memes, (ii) provides a detailed annotation guideline that can be followed for resource creation in other low resource languages, and (iii) consider the memes that contain code-mixed (english + bangla) and code-switched (written bengali dialects in english alphabets) caption. ## mute: a new benchmark dataset this work developed mute: a novel multimodal dataset for bengali hateful memes detection. the mute considered the memes with code-mixed and cod-switched captions. for developing the dataset, we follow the guidelines provided by @xcite . this section briefly describes the dataset development process with detailed statistics. ## methodology several computational models have been explored to identify hateful memes by considering the single modality (i.e., image, text) and the combination of both modalities (image and text). this section briefly discusses the methods and parameters utilized to construct the models. ## mute: benchmark evaluation the training set is used to train the models, whereas the validation set is for tweaking the hyperparameters. we have empirically tried several hyperparameters to obtain a better model's performance and reported the best one. the final evaluation of the models is done on the test set. this work selects the weighted f1-score (wf) as the primary metric for the evaluation due to the class imbalance nature of the dataset. apart from this, we used the class weighting technique @xcite to give equal priority to the minority class (hate) during the model training. ## conclusion this paper presented a multimodal framework for hateful memes classification and investigated its performance on a newly developed multimodal dataset (mute) having bengali and code-mixed (bangla + english) captions. for benchmarking the framework, this work exploited several computational models for detecting hateful content. the key finding of the experiment is that the joint evaluation of multimodal features is more effective than the memes' only visual or textual information. moreover, the cross-lingual embeddings (xlm-r) did not provide the expected performance compared to the monolingual embeddings (bangla-bert) when jointly evaluated with the visual features. the error analysis reveals that the model's performance gets biased to a particular class due to the class imbalance. in future, we aim to alleviate this problem by extending the dataset to a large scale and framing it as a multi-class classification problem. secondly, for robust inference, advanced fusion techniques (i.e., co-attention) and multitask learning approaches will be explored. finally, future research will explore the impact of dataset sampling and do some ablation study (i.e., experimenting with only english, only bangla, code-mixed, and code-switched text) to convey valuable insights about the models' performance.