MDC ^3 : A Novel Multimodal Dataset for Commercial Content Classification in B engali (2025)

Abstract: Identifying commercial posts in resource-constrained languages among diverse and unstructured content remains a significant challenge for automatic text classification tasks. To address this, this work introduces a novel dataset named MDC ^3 (Multimodal Dataset for Commercial Content Classification), comprising 5,007 annotated Bengali social media posts classified as commercial and noncommercial. A comprehensive annotation guideline accompanying the dataset is included to aid future dataset creation in resource-constrained languages. Furthermore, we performed extensive experiments on MDC ^3 considering both unimodal and multimodal domains. Specifically, the late fusion of textual (mBERT) and visual (ViT) models (i.e., ViT+mBERT) achieves the highest F1 score of 90.91, significantly surpassing other baselines.

## introduction social media platforms are crucial for communication, enabling individuals and businesses to share diverse content. commercial posts influence consumer behavior and brand perception, making their identification essential for transparency, consumer protection, and regulatory compliance @xcite @xcite . however, native advertising and influencer marketing blur the lines between ads and personal content, complicating detection @xcite . detecting commercial posts is vital for targeted advertising, brand monitoring, and consumer behavior analysis. while most research focuses on english, bengali social media lacks annotated datasets and faces challenges from multimodal content and cultural nuances. to address this, we introduce mdc 3 , a dataset for classifying bengali social media posts as commercial or non-commercial. various unimodal and multimodal baselines are explored based on mdc 3 . ## developement of mdc 3 as per our investigation's outcome, no benchmark dataset is explicitly available for detecting influencer commercial content in bengali. therefore, this work presents a benchmark dataset, mdc 3 (multimodal commercial content classification dataset), from bengali social media posts comprising facebook and instagram posts categorized into two classes, commercial and non-commercial. the definitions of each class within the dataset are provided below, as described by @xcite . ## experimental setup this section describes the summary of the experimental setup while training and evaluating our model on the dataset. the simulation was run on a personal computer with an nvidia geforce gtx 2060 gpu and an intel core i7-9700 cpu running at 3.00 ghz. additionally, a kaggle notebook with a p100 gpu was uti- ## error analysis in our study on the classification of multimodal commercial content from social media, we performed an extensive error analysis to identify the strengths and weaknesses of our proposed model. the analysis was conducted quantitatively and qualitatively to understand the model's performance comprehensively. quantitative analysis: during the evaluation, we conducted a detailed quantitative analysis using confusion matrices to assess the model's performance. the confusion matrices are shown in figure 4 states that the proposed multimodal model vit+mbert, which integrates both text and visual data, achieved an accuracy of 90.92%. this represents a significant improvement over unimodal models that rely solely on visual or textual data, demonstrating the model's effectiveness in accurately classifying commercial content on social media platforms. the analysis revealed specific challenges, particularly with subtle commercial content. posts that casually mentioned products without explicit promotional intent were occasionally misclassified as non-commercial despite the presence of brand-related keywords. for instance, these errors occurred in 7.68% of the cases. conversely, noncommercial posts that included neutral or critical discussions of products were sometimes wrongly categorized as commercial, likely due to an overreliance on product-related keywords. this misclassification was observed in 10.95% of the cases. moreover, the complexity of multimodal data led to further misclassification issues. specifically, posts with ambiguous text and neutral visuals were prone to errors, which occurred 15.87% of the time. this underscores the need for more sophisticated textual and visual data integration to improve classification accuracy and reduce errors. ## conclusion this paper proposed a multimodal framework for detecting commercial content in bengali social media posts, evaluated on the newly developed mdc 3 dataset with 5,007 posts labeled as commercial and non-commercial. the study utilized models such as mbert for textual features and vit for visual features. results show that multimodal approaches significantly outperform unimodal methods, with vit+mbert achieving the best performance. error analysis identified challenges in detecting subtle advertising styles. future work will expand the dataset, incorporate diverse domains, and explore advanced fusion techniques to improve model robustness and performance. moreover, explainability analysis will also be included to improve the model's clarity.