MM - SOC : Benchmarking Multimodal Large Language Models in Social Media Platforms (2024)

Abstract: Social media platforms are hubs for multimodal information exchange, encompassing text, images, and videos, making it challenging for machines to comprehend the information or emotions associated with interactions in online spaces. Multimodal Large Language Models (MLLMs) have emerged as a promising solution to address these challenges, yet struggle with accurately interpreting human emotions and complex contents like misinformation. This paper introduces MM-Soc, a comprehensive benchmark designed to evaluate MLLMs’ understanding of multimodal social media content. MM-Soc compiles prominent multimodal datasets and incorporates a novel large-scale YouTube tagging dataset, targeting a range of tasks from misinformation detection, hate speech detection, and social context generation. Through our exhaustive evaluation on ten size-variants of four open-source MLLMs, we have identified significant performance disparities, highlighting the need for advancements in models’ social understanding capabilities. Our analysis reveals that, in a zero-shot setting, various types of MLLMs generally exhibit difficulties in handling social media tasks. However, MLLMs demonstrate performance improvements post fine-tuning, suggesting potential pathways for improvement.

## introduction social media platforms have become the epicenter of multimodal information exchange, blending various formats of content such as text, images, and videos. these platforms serve not only as channels for sharing news and personal experiences but also for spreading rumors and shaping public opinion @xcite . the inherent multimodality of social media content requires users to not only interpret individual modalities such as text or images but also to understand the interplay between them, pushing the boundaries of how machines comprehend human communication in online spaces. multimodal large language models (mllms) have recently emerged as powerful tools for bridging the understanding of natural language and visual cues, showcasing their potential in a range of tasks ranging from image captioning to complex question answering @xcite . despite these advancements, the complexity of tasks such as understanding hu-man emotions, memes, and verifying misinformation presents significant evaluation challenges to mllms @xcite . these tasks require not only combining signals extracted from both textual and visual domains, but also considering various social contexts upon making a decision regarding contextual appropriateness or correctness, which often require knowledge of cultural contexts and subjective interpretations @xcite . for instance, the task of explaining visual memes requires not only proficiency in image recognition and language generation, but also capability of understanding the underlying situation of the image on why it should be considered humorous. given that large language models struggle at solving tasks requiring social knowledge @xcite , we anticipate multimodal social tasks to prove an even harder challenge. the complexity of multimodal tasks from social media demands a benchmark that can evaluate mllms on their understanding of the different data domains as well as the social context. such a benchmark would not only highlight the current limitations of mllms, but also lead to future innovations aimed at bridging the gap between human and machine understanding of multimodal content. this work. this paper introduces mm-soc, a novel multimodal benchmark to rigorously assess the capabilities of mllms across diverse tasks typical of social media environments. along with existing prominent multimodal datasets, we add a large-scale, newly collected youtube tagging dataset, resulting in ten tasks across five datasets. our analysis primarily targets opensource mllms, recognizing their advantages in terms of rapid deployment, reduced operational costs, and superior capacity for maintaining data integrity compared to centralized proprietary models. through mm-soc, we conduct a thorough and systematic examination of mllms, exploring and validating new methodologies to augment mllm efficacy in handling multimodal tasks. finally, we provide a detailed discussion on the performances, shedding light on the implications of our findings for future mllm development and deployment. contributions. our contributions are summarized as follows. first, we introduce mm-soc, a novel benchmark to holistically evaluate mllms' capability in tackling multimodal tasks derived from online social networks. second, we perform a comprehensive evaluation and benchmark 10 representative open-source mllms on mm-soc, compar-ing their performances with fine-tuned llm baselines. third, we conduct two case studies on mm-soc for testing the effectiveness of two methods: self-improvement and explanation-augmented finetuning. we find that, while zero-shot mllms often fall short in achieving comparable performances compared to fine-tuned models, their performances can be improved via specific fine-tuning strategies. ## the mm-soc benchmark overview. the deployment of multimodal large language models (mllms) as general-purpose assistants across social networks marks a significant shift from traditional, specialized models designed for singular tasks. this transition necessitates a comprehensive skill set enabling these models to navigate the multifaceted challenges presented by user-generated content. motivated by this, we design mm-soc, which spans both natural language understanding and generation tasks. these tasks are designed to test the models' abilities to interact with user-generated content encountered online. the selection includes binary classification, multi-class classification, text extraction, and text generation tasks, aiming to cover a wide range of interactions mllms might encounter with online content. the detailed task selection process is in appendix a. to ensure a comprehensive evaluation, we employ a variety of 10 tasks that mirror the complexity of real-world scenarios, ranging from understanding online video content to identifying misinformation and detecting hate speech in memes. the statistics of the dataset are in youtube2m distinguishes itself with two features: 1) relevance to online user groups. the youtube2m dataset features videos shared on red-dit. the selection of youtube as the primary source is based on its expansive user base, with more than 2.5 billion monthly users @xcite and the rich variety of its multimodal content. reddit is among the top most popular social media and is characterized by its unique community structures called "subreddits". unlike general video collections on youtube, youtube2m reflects the choices of individuals within specific subreddit communities, aligning with their interests, humor, or preferences. this targeted selection process ensures our dataset is particularly relevant to distinct user groups. 2) viral potential. reddit is renowned as a catalyst for virality. videos shared on reddit can rapidly gain significant attention and engage communities deeply through more discussions, comments, and votes within their respective subreddits. notably, the presence of toxic, biased, or unverified content in online videos raises concerns over the propagation of misinformation, fostering distrust and hate speech online. consequently, the accurate categorization and tagging of these videos become critical for content moderation. dataset construction. we retrieved the urls of all youtube videos shared on reddit over 12 years spanning from 2011 to 2022. subsequently, we used youtube data api foot_0 to collect comprehensive metadata of the youtube videos, including their titles, descriptions, channels, publish timestamps, restrictions, default languages, topic categories, and embeddability status. additionally, we compiled extensive statistics for each video, covering aspects such as duration, and the number of comments, likes, and views they garnered. to ensure the quality and relevance of the dataset, we filtered the dataset and retained only videos with valid tags and thumbnail images, resulting in a dataset with 1,963,697 videos. misinformation detection. misinformation detection represents a critical challenge as the proliferation of multimodal misinformation across online platforms can undermine trust in digital ecosystems and lead to real-world harm @xcite @xcite @xcite . here, we formulate misinformation detection as a binary classification problem and utilize the politifact and gossipcop datasets @xcite . the task aims at evaluating a model's ability to accurately differentiate between true news and misinformation, leveraging both the textual content and the associated images of news articles. hate speech detection. the prevalence of hate speech in online platforms has several detrimental effects, both on the individual user-level and on the platform as a whole @xcite . to support research targeted at curbing the spread of harmful content and abusive language, we incorporate the hateful memes @xcite dataset. this dataset evaluates the ability to recognize messages that attack or demean a group based on attributes such as race, religion, ethnic origin, sexual orientation, disability, or gender. such ability is essential for creating inclusive online environments, protecting users from harm, and complying with legal standards. emotion analysis. the interactions among users in online social media platforms often contain rich and diverse exchanges of emotions. these emotions include not only sentiment but also humor, sarcasm, and offensiveness. coupled with multimodal means of expressions such as memes, it can be challenging for mllms to accurately capture the true emotion conveyed through the message. therefore, we include the memotion @xcite dataset which focuses on sentiment and emotion analysis within online memes, presenting a multifaceted challenge that spans sentiment analysis and the detection of humor, sarcasm, and offensive contents. ocr. optical character recognition (ocr) refers to the task of extracting text within images into machine-encoded text. a model's ocr proficiency is directly related to its ability to access and interpret online information such as infographics, memes, and screenshots of textual conversations, which are prevalent forms of communication and information dissemination online @xcite . we use the hateful memes and memotion datasets to evaluate ocr capabilities. image & social context description. image description assesses a model's ability to generate accurate, contextually relevant, and coherent natural language descriptions of images. the capability to accurately describe an image in natural language aids in the understanding of the visual content, which both provides an intermediary step in reasoning about the multimodal inputs and also aids human users in understanding their decisions in an interpretable way. previous studies have demonstrated that commercial models such as gpt-4/3.5 possess extensive domain knowledge in various fields, including social sciences, and have shown promising results in data annotation, surpassing the performance of human annotators @xcite @xcite . thus, for each example in the dataset, we employed gpt-4v as a strong teacher to generate descriptions of images and their associated social contexts. for each example within the dataset, we instructed the model to provide a comprehensive description of the image, encompassing its foreground, background, major subjects, colors, and textures, as well as the social context for each example, such as cultural backgrounds, possible interpretations within various societal groups, and the potential target demographics. these examples served as references for evaluating mllms' capabilities to understand both the image contents and social knowledge. ## benchmark results the low performances of zero-shot mllms can be attributed primarily to two reasons: 1) the divergence in training objectives. unlike discriminative models, which are explicitly fine-tuned to predict correct labels, mllms are oriented towards maximizing cross-modal alignment and instructionfollowing abilities. their training regimes are designed to enhance text generation capabilities based on input images. such an alignment does not cater to misinformation detection, which demands not only multimodal reasoning but also the ability to evaluate the reliability of sources and incorporate extensive external knowledge. 2) disparity in the training corpus content. mllms are predominantly trained for tasks such as object detection, image captioning and visual question answering (vqa) @xcite , which rarely encompass tasks in social knowledge reasoning. the lack of tasks requiring subjective reasoning may inherently limit the mllms' performance regarding these tasks, and is further supported by the fact that performing task-specific fine-tuning on even much smaller models that use only limited information significantly outperforms mllms. llava achieves highest performance among all mllms in most tasks. among the tested mllms, llava-v1.5-13b/7b achieve the best and second best overall performances with average ## illustrative uses of mm-soc the mm-soc benchmark can be used to experiment with new methods for enhancing mllms in solving multimodal reasoning and generation tasks. we conduct two case studies, proposing new directions for strengthening mllm capabilities. ## conclusion our study presents a comprehensive evaluation of 4 leading mllms on 10 carefully constructed multimodal social media tasks from diverse domains such as misinformation, hate speech, memes, and a novel youtube dataset, which comprises our proposed mm-soc benchmark. our evaluation of the current capabilities presents the following insights: (i) zero-shot capabilities of certain mllms are near-random and underperform drastically in comparison to smaller fully fine-tuned models, (ii) llava-v1.5 is currently the most competitive open-source mllm, and (iii) instruction following capabilities of mllms improve with their size. mm-soc also enables quantitative case studies, two of which were illustrated in this work and revealed (a) the limitations of mllms in selfimproving their accuracy and (b) the effectiveness of fine-tuning mllms with labeled data. as benchmarks highlight current limitations and guide future research, we intend to expand mm-soc's coverage to more models and social media tasks to encourage reliable applicability of mllms in online spheres.