Bias Detection Using Textual Representation of Multimedia Contents (2023)

Abstract: The presence of biased and prejudicial content in social media has become a pressing concern, given its potential to inflict severe societal damage. Detecting and addressing such bias is imperative, as the rapid dissemination of skewed content has the capacity to disrupt social harmony. Advanced deep learning models are now paving the way for the automatic detection of bias in multimedia content with human-like accuracy. This paper focuses on identifying social bias in social media images. Toward this, we curated a Social Bias Image Dataset (SBID), consisting of 300 bias/no-bias images. The images contain both textual and visual information. We scientifically annotated the dataset for four different categories of bias. Our methodology involves generating a textual representation of the image content leveraging state-of-the-art models of optical character recognition (OCR), image captioning, and character attribute extraction. Initially, we performed fine-tuning on a Bidirectional Encoder Representations from Transformers (BERT) network to classify bias and no-bias, as well as on a Bidirectional AutoRegressive Transformer (BART) network for bias categorization, utilizing an extensive textual corpus. Further, these networks were finetuned on the image dataset built by us SBID. The experimental findings presented herein underscore the effectiveness of these models in identifying various forms of bias in social media images. We will also demonstrate their capacity to discern both explicit and implicit bias.

## introduction approximately 14 billion images are uploaded every day across social media platforms like facebook. instagram, whatsapp, snapchat 1 . some of them contain harmful content that introduces or reinforces racial, gender, or cultural biases targeting certain groups of people. this can perpetuate 1 https://photutorial.com/photos-statistics/ stereotypes and normalize discrimination. additionally, they cause a decline in the user experience and encourage fair-minded users to use less of or leave the particular social media platform. in recent times, memes have become popular across the internet in various forms, including images, videos, text, and other media. the analysis of memes has surged in popularity due to their emergence as a significant mode of online communication and cultural expression. image-based memes frequently feature superimposed text and are characterized by humor, satire, or the conveyance of distinct cultural and social messages. memes often follow a common format or template that can be readily adapted to diverse contexts, giving rise to a multitude of variations on a particular theme. diverse aspects of meme analysis, including the identification of hate @xcite , emotion detection @xcite , sarcasm @xcite , and misogyny @xcite have been well studied. several datasets are available as a part of semeval -semantic evaluation workshop foot_0 . extensive research papers have been published, encompassing both unimodal and multimodal approaches. cutting-edge visual transformer models, such as visualbert, vilbert, mmbt have been employed foot_1 . these visual models performed well on visual linguistic tasks such as image captioning, visual question answering (vqa), etc., but failed when applied to multiple tasks on meme analysis @xcite . this might be because meme analysis involves associating text and image together for the prediction task. moreover, memes are context-dependent, and thus focusing on text and image content alone is not sufficient. secondly, unlike other tasks such as image captioning and vqa, text and image contents are most often uncorrelated. this study primarily aims to detect bias in images, notably memes. several recent research studies have targeted the detection of bias within textual data in the context of hateful speech, social media posts, and the likes @xcite . detection of bias in movie scripts has been studied by @xcite where the authors introduced a novel dataset of movie scripts annotated for bias and the type of bias. their annotation mechanism considered the context of the dialogue and also the previous utterance. madaan et al. analyzed gender bias in bollywood movies based on movie plots and movie posters @xcite . the authors performed a detailed plot analysis by extracting male and female character mentions and images from wikipedia movie pages. jha et al. also analyzed bollywood movie posters for gender bias from poster images @xcite . in some cases, gender stereotypes in a sentence are spotted by considering the presence of gender-specific stereotypical words @xcite , @xcite , however, they do not address contextuality or the implicit connotations for identifying potential prejudices. while there has been substantial attention on analyzing memes and detecting bias in textual data, prior to this, the exploration of bias detection in multimedia contents has been overlooked. we aim to ascertain bias by combining the textual information with the visual elements in the image by generating a contextual text representation. a few sample images from the dataset indicating bias labels are given in figure 1 . memes in figure 1 require contextual knowledge to comprehend bias labels. our focus in this work is on bias prediction and not on hate, where hate is considered a direct attack on a person or a group based on their characteristics. while memes demonstrating hate display the target community and opinions very clearly in a brutal manner as shown in figure 2 , biased memes are often difficult to interpret directly. with the above motivation in mind, we aim to explore the detection of bias and the category of bias in multimodal memes. in particular, we make the following contributions. the rest of the paper is structured as follows: section 2 describes details about the curated dataset at length in terms of the annotation process and challenges faced. we also present details of textual datasets we used for fine-tuning in the same section. section 3 presents our methodology to identify bias and the category of bias. section 4 presents experimental results with discussion. we present a detailed analysis with example images in section 5 and finally, we conclude in section 6. ## dataset and annotation bias, distinct from hatred or misogyny, possesses a subtle nature. in this context, we define bias as follows: a multimodal entity consisting of an image, optionally accompanied by embedded text, demonstrating partiality or prejudice towards or against an individual or group, often perceived as unfair. an annotated image dataset identifying bias is hard to get and harder to create because of the sociocultural nuances associated with labeling multimodal content. it is relatively less difficult to label a textual dataset for bias. various text datasets were curated in the past under different contexts, while an image-based dataset is not yet available to the best of our knowledge. consequently, as part of our research, we have meticulously assembled a compact dataset comprising 300 images, known as the social bias image dataset (sbid). in this article, we present detailed annotations for sbid. we utilized a textually annotated dataset for training and fine-tuning classifier models. ## methodology identification of bias is complicated since bias can cause harm even without using any explicit hateful content. bias can be emotionally neutral and can inadvertently get introduced in the content for reasons those are personal and inoffensive. often the contents posted on social media are sarcastic, and not all sarcasm necessarily are biased or stereotyped. it is also possible that the image posted is seemingly innocuous, but a tagline on the image or some other comments added to the image may make it offensive and harmful. the methodology we propose considers these specific tenets of bias and works on the principle of generating as holistic a description as possible of the content. we aim to create a contextual text representation of the image content by extracting text inscribed on the image, generating an image caption, and extracting character attributes of people in the image. we then use this representation in a two-stage framework; first a bias identification model (bim)) and then a topic classification model (tcm) to determine the category of bias. the functional flow of our approach is shown in figure 3 . both bim and tcm are trained following a sequential adaptation strategy where the models are first fine-tuned on a large domain dataset before fine-tuning on sbid. for tcm to identify the topic of the textual representations, we trained a multi-class classification model that can identify gender, race, occupational, and other (age, religion, body shaming, lgbtq, personality) bias. the individual functional blocks are explained below. ## experimental results and discussion an experimental evaluation of the proposed approach was performed on sbid test data. as mentioned previously, bim and tcm were trained starting with hibd. the approach presented an opportunity to understand the effectiveness of different sets of training data on model performance. we have listed the different experimental setups in ## analysis the caption generated by our ofa model is not the best fit for the current task. in most of the cases, the captions are general. character attributes play a major role in associating the character with the image content. to get a deeper insight into the contextual text representation, we examine the classification results on the following images. the caption produced for an illustrative image depicted in figure 6 is "a man standing in the grass holding a frisbee". the caption along with ocr "fat can't hide" is an example of a biased image while the bim model predicted as "no-bias". the annotators originally labeled the image as a biased image belonging to the body-shaming (other bias) category. ofa model failed to capture the attributes or characteristics of the person correctly. while the model made an accurate prediction with the caption obtained by the language model -"an obese man wearing a tank top and a fanny pack is standing in a field holding a frisbee". when there is no text present in the image, the contextual representation relies solely on the image caption and character attributes for prediction. in the case of the image example depicted in figure 1c , if the text on the image is removed, the image is unbiased. however, with the presence of text on the image, the image's meaning undergoes a complete transformation, rendering it a biased image, which our model is correctly capturing. ## conclusion & future work detection of bias is an inherently challenging problem since bias is often implicit and is associated with contextual and socio-cultural nuances. seemingly innocuous visuals can portray a very different meaning when connected with a textual statement and vice versa. towards this end, we attempted a transformer model-based approach that creates a fully textual representation of images with a description of contexts and then tries to classify the image. further, a topic is also associated with the textual narration of the image to identify the category of bias. this approach had to be adopted as it was hard to obtain an acceptable bias-labeled image dataset. we also handcrafted a social bias image dataset (sbid) with 300 annotated images to finetune the transformer models which were essentially trained on textual data. our results demonstrated that the models fine-tuned on the image dataset performed better as compared to using pre-trained text-based models in detecting bias in social media images. we expect the models to get stronger with sbid growing in size. the model performance today heavily depends on the effectiveness of the ocr system and the ability to generate succinct image captions. in future work, it is interesting to include images with multiple people and images with objects that resemble human form in the dataset. images with multiple people in it will demand establishing additional relationships between them and further research can aim to address this issue. the ocr model output can improve with the incorporation of modern language models. a larger suitably annotated image dataset will also enable direct leveraging of multi-modal transformer models.