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 these challenges, yet they struggle to accurately interpret human emotions and complex content such as 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 opensource 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. Our code and data are available at https: //github.com/claws-lab/MMSoc.git.

heading: 1 Introduction
text: 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 (Ferrara, 2020; Vosoughi et al., 2018). The inherent multimodality of social media content requires users to not only interpret individual
Image Desc Context
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 (Ramos et al., 2023; Liu et al., 2023c,b). Despite these advancements, the complexity of tasks such as understanding hu-
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man emotions, memes, and verifying misinformation presents significant evaluation challenges to MLLMs (Chen and Shu, 2023a,b). 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 (Ruch, 2010; Jacobi, 2014). 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 (Choi et al., 2023), 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 MMSOC 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.

heading: 2 The MM-SOC Benchmark
text: 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 Table 5. Tagging. In digital content management, the ability to accurately predict appropriate tags for online content is particularly significant given their diverse and multimodal nature, which includes textual narratives, visual features, and cultural contexts. Effective tagging enhances content discoverability, facilitates content moderation, and significantly improves the user experience. To this end, we introduce YouTube2M, a novel dataset comprising 2 million YouTube videos shared on Reddit, specifically curated to assess models’ proficiency in predicting tags from a predefined set in Table 7 based on video titles, descriptions, and visual content. We provide a comprehensive analysis of YouTube2M in Appendix B.
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 (Statista, 2024) 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 1 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 (Yang et al., 2022, 2023; Ma et al., 2022; Jin et al., 2022; He et al., 2023). Here, we formulate misinformation detection as a binary classification problem and utilize the PolitiFact and GossipCop datasets (Shu et al., 2020). The task aims at evaluating a model’s ability to accurately differentiate between true news and misinformation, leveraging both the textual content
1https://developers.google.com/youtube/v3
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 (Mondal et al., 2017; He et al., 2021). To support research targeted at curbing the spread of harmful content and abusive language, we incorporate the Hateful Memes (Kiela et al., 2020) 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 (Sharma et al., 2020) 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 (Zannettou et al., 2018). 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 (Savelka et al., 2023; Gilardi et al., 2023; Zhu et al., 2023a). Thus, for each example in the dataset, we employed GPT4V 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.

heading: 2.1 Model Selection
text: We consider 10 prominent open-source models spanning four different distinct architectures: LLaVA-v1.5 (Liu et al., 2023b), BLIP2 (Li et al., 2023b), InstructBLIP (Dai et al., 2023), and LLaMA-Adapter-v2 (Zhang et al., 2023b). Details on model parameter volumes are in Table 12. The models are selected to cover diverse model sizes. We apply our prompts (Table 6) to test the performances of MLLMs in a zero-shot setting. For tasks in which ground-truth texts are available as inputs, we compare MLLMs’ performances with five unimodal discriminative models in a full fine-tuning setting, including BERT (Kenton and Toutanova, 2019), RoBERTa-Base/Large (Liu et al., 2019), DeBERTa (He et al., 2020), and MiniLM (Wang et al., 2020). These text-only models have shown competitive performances in text classification. Implementation details can be found in Appendix D.1.

heading: 3 Benchmark Results
text: Table 1 shows the overall performances across 10 tasks. Here, we use a unified score for each task to facilitate a high-level performance comparison across diverse tasks. For text classification and extraction tasks, we use the macro-F1 score as the aggregated measure. For text generation tasks including image description (ID) and social context description (SCD), we use ROUGE-L (Lin, 2004). The results for misinformation detection are averaged across PolitiFact and GossipCop, and the results for OCR are averaged across Memotion and Hateful Memes. The complete evaluation results can be found in Appendix D.2. Zero-shot MLLMs are on par with random
guesses. Despite their large model sizes and extensive training corpus, all MLLMs demonstrate underwhelming performances in zero-shot settings, often paralleling and sometimes falling short of the random baseline. This trend is especially evident on the offensiveness detection task, where none of the 10 models surpass the random baseline, with an average macro F1 score of 0.402 compared to the baseline of 0.493. A similar pattern emerges in humor detection, with eight models underperforming the baseline. The tasks in our benchmark which simulate real-life interactions in social media are indeed challenging for most MLLMs. Zero-shot MLLMs underperform fully finetuned models in most settings. We next focus on the misinformation detection task, which takes a binary classification form and can thus be evaluated using encoder-only LLMs such as BERT. Table 2 reveals a consistent underperformance of MLLMs compared to fully fine-tuned LLMs which only use textual information. To our surprise, DeBERTa emerges as the top-performing model with only 98 million parameters, whereas zero-shot MLLMs achieve significantly inferior performances.
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) (Dai et al., 2023; Liu et al., 2023c), 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
scores of 0.402 / 0.368, a 18.9% / 8.9% improvement over InstructBLIP Vicuna 13B. The performance gap is most significant on the text generation tasks, including ID and SCD as shown in Table 1, where LLaVA-v1.5-13B reaches a performance improvement of 76.9% and 55.7% compared with the other models. This advantage could result from both having a wider range of training data and pretraining objectives — multiturn conversation, detailed description, and complex reasoning. For example, the complex reasoning objective typically requires a step-by-step reasoning process by following rigorous logic. Figure 2 shows the performances of the strongest models under each model architecture. The scores are normalized in the 0- 1 range. Interestingly, we found that no single model achieves the best performance across all tasks. LLaVA-v1.5-13B performs the best on text
generation such as ID or SCD as well as tasks that require social reasoning like misinformation detection, but its ability in tagging is relatively poor. BLIP2 is best on OCR and discriminative tasks like sarcasm and hate speech detection, whereas its generative abilities are relatively poor. Larger models exhibit better instructionfollowing abilities. To quantify an LLM’s adherence to predefined content constraints, we leverage a success rate metric, defined as the percentage of responses from a model that aligns with the requested formats. We see a compelling positive correlation between the parameter size of the text encoder and its ability to follow instructions and precisely classify news content. Table 2 shows that the macro F1-score on PolitiFact for InstructBLIP increases from 0.376 to 0.434 when the text encoder changes from Vicuna-7B to Vicuna-13B,

heading: Normalized PerformancesOffensive MisinformationHumor Hate Speech Sarcasm Sentiment
text: llama-adapter-v2 0.93564356 0.7664042 1 0.71100917 0.75210084 0.55333333
and improves from 0.418 to 0.519 when changing from FlanT5-XL to FlanT5-XXL. This correlation indicates that models with larger parameter sizes are equipped with more complex reasoning abilities and a sophisticated understanding of social knowledge, which are essential components for accurately evaluating the veracity of news articles.
Online content ranges from concise and engaging social media posts and microblogs to detailed and extensive narratives found in news articles and in-depth blog posts. This diversity in content length poses a significant challenge for MLLMs, as it requires the models to maintain their generative capabilities over varying context sizes and a wide
range of information densities (Peng et al., 2023; Peysakhovich and Lerer, 2023). To address these concerns, we vary the number of tokens used as input to detect misinformation on the PolitiFact dataset from 16 to 512 tokens. The results, as depicted in Figure 3, provide compelling evidence of the MLLMs’ stable instruction-following abilities. Notably, we observed an increase in the macro-F1 score as the input length expanded, suggesting that MLLMs are able to leverage evidence from longer contexts for enhanced reasoning and performances.

heading: 4 Illustrative Uses of MM-SOC
text: 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.

heading: 4.1 Can MLLMs Self-improve Its Answers?
text: The ability of MLLMs to self-improve – enhancing their answers iteratively without external supervision – can help generate increasingly consistent and robust answers, reducing the need for human oversight. Using our benchmark, we investigate the
self-improvement capabilities of MLLMs. The initial phase involves the model generating an answer for each question. Subsequent iterations, starting from the second round, require the model to produce new answers conditioned on the multimodal inputs and its prior responses. The iterative process is performed for six rounds. To quantitatively assess the evolution of answers across these iterations, we employed three established similarity metrics: BERTScore (Zhang et al., 2019), sentence embeddings similarity (Reimers and Gurevych, 2019), and bigram similarity (Kondrak, 2005). These metrics enabled us to measure the consistency of answers from one round to the next, as well as their fidelity to the ground truth.
Figure 4 displays a notable trend towards convergence in the model’s answers with each iteration. For instance, the average BERTScore between answers from consecutive rounds (first to second, and second to third) exhibited a significant increase, from 0.699 to 0.910. Meanwhile, over 55% of all answer pairs between the second and third rounds achieved a sentence embedding similarity score exceeding 0.99. Despite improvements in internal consistency, our analysis revealed a gradual divergence from the ground truth over successive iterations. This was evidenced by a decrease in sentence embedding similarity between MLLM-generated answers and the ground-truth (0.887 → 0.854), signaling a potential limitation in the model’s ability to maintain factual accuracy in iterative generation.

heading: 4.2 Does finetuning MLLMs Improve Overall Performance?
text: We examine whether MLLMs can improve on MMSOC via additional fine-tuning steps. Instead of fine-tuning models on separate tasks, we use the data across all different tasks at once for training and examine whether this setting still can contribute towards improvements for each task.
We employed two distinct strategies for finetuning. The first approach directly fine-tunes the model using the default input and output data, analogous to a standard fine-tuning setting. In the second approach, we leverage GPT-4V as a strong teacher to generate explanations after each ground truth answer for each sample. Along with the original input data, the GPT-generated explanations are augmented as additional training data.
Figure 5 shows the performances of fine-tuned LLaVA-7B and 13B models along with baselines. With standard fine-tuning, we observe notable
gains in detecting misinformation, offensiveness, and sentiment, but also drops in hate, humor, and sarcasm detection. Meanwhile, fine-tuning with explanations improved performance across a broader spectrum of tasks, e.g., increases of 18.2% in hate speech detection and 12.7% in sentiment analysis. Notably, text generation tasks such as image description and social context demonstrated greater gains. Table 3 further reinforces the positive effects of finetuning with explanations for text generation tasks. Compared to the zero-shot baseline, both the 7B & 13B LLaVA models achieve higher ROUGE2 scores on image description (40.5% for 7B and 30.5% for 13B). Similarly, for social context description, we observe improvements of 20.9% and 11.6% respectively. These improvements are accompanied by a reduction in response verbosity, highlighting the importance of explanations and rationales for improving multimodal text generation tasks. Interestingly, finetuning without explanations performs worse than the baseline, indicating that the standard finetuning approach may not be sufficient to learn the tasks in MM-SOC and signaling the need for refined finetuning strategies.

heading: 5 Related Works
text: Multimodal Large Language Models: Multimodal Large Language Models (MLLMs) have demonstrated exceptional natural language understanding and generation abilities by integrating visual information with textual inputs (Awadalla et al., 2023; Yu et al., 2023; Liu et al., 2023a; Verma et al., 2023; Li et al., 2023c; Zeng et al., 2023; Xie et al., 2024). Models such as LLaVA (Liu et al., 2023b,c), BLIP2 (Li et al., 2023b), InstructBLIP (Dai et al., 2023), and LLaMAAdapter (Zhang et al., 2023b; Gao et al., 2023) have showcased their superior performance in a range of applications. The success of MLLMs suggests their potential for widespread use in scenarios requiring not only factual analysis and comprehension but also subjective judgment and decision-making based on a nuanced understanding of social contexts and human perceptions. Our study reveals that current MLLMs still fall short of fully grasping and responding to complex social scenarios with the required depth of understanding and sensitivity.
Benchmarking Large Language Models: The evaluation of LLMs is crucial for uncovering their capabilities and identifying potential risks associated with their deployment in sensitive do-
mains (Wang et al., 2024a,b, 2023; Liu et al., 2020; Zhang et al., 2023a; Zhao et al., 2023; Zong et al., 2023; Xiao et al., 2023; Chan et al., 2023). Benchmarking efforts across various domains such as legal (Deroy et al., 2023), healthcare (Jin et al., 2023), finance (Zhou et al., 2023), psychology (Li et al., 2023a) have provided valuable insights into LLMs such as their reliability (Shu et al., 2023), robustness (Zhu et al., 2023b), and ethical implications (Sun et al., 2023). Despite these efforts, there remains a notable gap in the development of comprehensive multimodal benchmarks for social domains. In this work, we create a holistic multimodal benchmark that captures the broad spectrum of social language and interactions.

heading: 6 Conclusion
text: 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.

heading: 7 Limitations
text: We describe limitations of the current study settings and discuss potential directions for future works.

heading: 7.1 Exclusion of Proprietary Models
text: This study does not focus on proprietary models like GPT-4V and Gemini for specific reasons. First, this research aims to spotlight the constraints of open-source MLLMs in tackling multimodal tasks derived from social media contexts. This emphasis on open-source models is driven by our com-
mitment to enhancing privacy protection. Unlike proprietary models that aggregate data of multiple platforms onto a central server, posing significant privacy risks and operational costs, open-source models are able to process data in a decentralized way (Fan et al., 2023; Zhang et al., 2023c). This distinction not only ensures better privacy safeguards but also resonates with our objective to spotlight and scrutinize the limitations inherent within open-source frameworks when deployed in complex, real-world scenarios like social media. By doing so, we hope that the research community can dedicate resources towards the development of more sophisticated open-source models that address these gaps, promoting the ethos of open science. Second, proprietary models like Gemini reject images containing people and prompts associated with misinformation and hate speech. These restrictions present significant barriers to a comprehensive analysis of MLLMs’ performance in handling the diverse and often complex content found on social media platforms.

heading: 7.2 Scope of Datasets Included in Benchmark
text: Online platforms facilitate several well-being discussions and provide support to potentially vulnerable members of the community (Alghowinem et al., 2016; Sindoni, 2020). While our current datasets consider applications of MLLMs for some safety-critical tasks like misinformation and hate detection, extensions of MM-SOC should include datasets and tasks that cover applications that promote inclusivity and support-offering on online platforms. The current version of the benchmark is not “open-world, universal, and neutral,” the likes of which have been contested to exist (Raji et al., 2021), but an evolving-effort to contextualize the progress in MLLMs with respect to widely-used social media tasks.

heading: 8 Ethical Considerations and Broader Impacts
text: MLLMs are recognized for exhibiting decisionmaking biases, a direct consequence of biases present within their training datasets. These include but are not limited to, biases in core sociodemographic categories such as gender, race, and religion (Janghorbani and De Melo, 2023; Ruggeri and Nozza, 2023). This can cause severe issues during downstream applications of MLLMs, particularly in contexts where decisions can significantly
affect individual choices. A significant portion of the biases in MLLMs may be attributed to the data it is trained on. The annotation of subjective tasks in NLP benchmarks also requires consideration, as highlighted in various studies (Aroyo and Welty, 2015; Waseem, 2016; Al Kuwatly et al., 2020). The interpretation of humor or offensive content can significantly vary across different cultural and societal backgrounds, and thus benchmarks should incorporate a broader spectrum of human viewpoints. This is also applicable to certain tasks within our benchmark, where the labels of our questions are reflective of the viewpoints of a hypothetical "average Twitter user." We recognize the importance of this diversity and inclusivity. Our hope is for subsequent research leveraging our benchmark to hopefully develop and include datasets that are more representative of social diversity and inclusiveness, thereby addressing these disparities.
One consistent theme throughout our empirical investigations is that the current performances of MLLMs in general are suboptimal. Notably, certain zero-shot MLLMs exhibit lower accuracy compared to both LLMs fine-tuned exclusively on textual data and even random scores. This underperformance is likely attributable to the insufficient training of MLLMs on tasks requiring subjective judgment and comprehension of social context. For MLLMs to achieve broader and more reliable applicability, future versions should be trained on more tasks that cover ethical, social, and cultural dimensions, thereby ensuring a more comprehensive understanding and interaction capability in diverse contexts.

heading: Acknowledgements
text: This research/material is based upon work supported in part by NSF grants CNS-2154118, ITE2137724, ITE-2230692, CNS2239879, Defense Advanced Research Projects Agency (DARPA) under Agreement No. HR00112290102 (subcontract No. PO70745), CDC, and funding from Microsoft. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the position or policy of DARPA, DoD, SRI International, CDC, NSF, and no official endorsement should be inferred. We thank members of the CLAWS Lab for their helpful feedback.

heading: A Task Selection
text: The selection of tasks and datasets in MM-SOC centers around three key criteria:
• Tasks that require multimodal understanding of both textual and image domains;
• Tasks directly related to the dynamics of social media platforms;
• Tasks that have undergone rigorous evaluation in subsequent research, which affirms their validity as a benchmark.
The task selection process started with a comprehensive literature review through NLP conferences (ACL, EMNLP, NAACL, SemEval), Machine Learning conferences (NeurIPS and ICML)
and Data Mining conferences (KDD and SIGIR) since 2019. Papers satisfying these criteria were retained. Our final list of tasks, while collectively categorized under multimodal engagements in social media contexts, each distinctly require a variety of cognitive capabilities. Some of these capabilities intersect across different tasks, while others are unique to specific challenges. Every task demands that models not only comprehend textual instructions but also accurately interpret relevant visual information to solve the task.
B The YouTube2M dataset

heading: B.1 Distribution of Languages
text: Table 8 shows the language distribution of YouTube videos in YouTube2M. There are 138 unique languages in YouTube2M. 323,007 videos have explicitly specified their default languages, representing 16.45% of the total 1,963,697 videos. We provide a detailed breakdown of the languages, showcasing the distribution of the top 10 most and least popular languages within the dataset. Our findings reveal a long-tail distribution in language popularity. Notably, English (including en, en-GB, en-US) dominates the dataset with 275,408 videos, accounting for 85.3% of videos with a specified language. In contrast, the ten least common languages each only appear once.

heading: B.2 Distribution of Tags
text: The YouTube2M dataset encompasses a rich variety of 62 unique tags, with 1,389,219 videos bearing the top 5 tags, as shown in Table 9. Note that a video can have multiple tags. This accounts for 70.7% of the entire dataset. We observe a strong inclination towards gaming and music content.

heading: B.3 Channel Information
text: There are 604,340 unique channels associated with the videos in the dataset. The most popular 10 channels and their associated videos are shown in Table 9. As observed from the statistics, a large portion of videos propagated in online social networks are centered around news and sports, signifying the popularity of these topics among online discourse.

heading: C Details about Datasets

heading: C.1 Tagging
text: The tagging task focuses on predicting appropriate “topic categories” for YouTube videos, chosen from a predefined set as listed in Table 7. These topics
make it easier for users to find videos that match their interests but also enhance the overall content management strategy. This dataset exemplifies the
necessity of multimodal understanding in categorizing online video content. The dataset is licensed under the Apache 2.0 License.
Given the substantial volume of the YouTube2M dataset, evaluation and fine-tuning on the entire dataset presents challenges such as runtime costs and catastrophic forgetting (Huang et al., 2024; Zhai et al., 2024), where LLMs severely forget previously acquired information upon being trained on new data. To address potential biases and the predominance of YouTube data in tagging tasks, we strategically curated a subset of 2,000 examples from YouTube2M, aiming to mitigate any disproportionate influence of tagging tasks on the fine-tuning process. We partitioned the sampled dataset into training and test sets with an 80:20 ratio.

heading: C.2 Misinformation datasets
text: We consider two datasets under the misinformation detection theme: PolitiFact and GossipCop. Both datasets were curated by Shu et al. (2020), distributed under the CC-BY-SA License, and are publicly available for download at https://github.
com/KaiDMML/FakeNewsNet/.

heading: C.2.1 PolitiFact
text: This dataset contains news content from the factchecking website PolitiFact2, which focuses on political discourse, and contains the title, body, images, and user metadata from news articles. The dataset contains 485 news articles. Each article is annotated into one of the two categories: ‘fake’ and ‘real.’

heading: C.2.2 GossipCop
text: This dataset contains news content from GossipCop, which targets the realm of entertainment news, and includes the title, body, images, from the news articles. The article contains 12,840 new articles, each of which is categorized into one of the two categories: ‘fake’ and ‘real.’

heading: C.3 Hateful Memes
text: The Hateful Memes dataset contains 12,840 memes that were designed to include meme-like visuals with text laid over them. Since a unimodal classifier (i.e., text-only or image-only) would struggle to make an inference about the hateful nature of the memes without considering both the modalities, they present a unique opportunity to test the multimodal reasoning capabilities of MLLMs. The designed memes were manually annotated to be in one of the two categories: ‘hateful’ or ‘benign.’ The dataset is distributed under the MIT License.

heading: C.4 Memotion
text: The Memotion dataset comprises 12,143 memes, each meticulously annotated with labels that cate-
2https://www.politifact.com/
Channel Description #Videos
euronews European television news network, headquartered in Brussels, Belgium 4737 Al Jazeera English 24-hour English news channel headquartered in Doha, Qatar 4655 IGN American video game media featuring videos about the latest
gaming & entertainment news and events 4041
Fox News 24-hour all-encompassing news service dedicated to delivering breaking news and political & business news 3504 WWE World Wrestling Entertainment (WWE) original shows 2847 The Young Turks American progressive news commentary show 2345 ESPN Multimedia sports entertainment channel 2026 Movieclips Online movie clips collection 1883 CNN Multinational news channel and website 1808 The Majority Report w/ Sam Seder daily political talk show 1655
gorize the memes according to their sentiment (positive, negative, neutral), the type of emotion they convey (sarcastic, funny, offensive, motivational), and the intensity of the expressed emotion. The emotion class and the overall sentiment were manually labeled by Amazon Mechanical Turk (AMT) workers. The dataset is distributed under the Community Free Resource License3.

heading: D Details about Experiments
text: D.1 Implementation Details
Benchmark Evaluation For inference, we use Nucleus Sampling (Holtzman et al., 2019) with a probability threshold of 0.9, a temperature of 1.0, and a maximum output length of 256 tokens. To account for the randomness in the generation process, we run each experiment with 3 random seeds and report the average results. All experiments were conducted on a server with 5 A100 80GB GPUs. The models are implemented using the Transformers library (Wolf et al., 2020). We use the NLTK package (Loper and Bird, 2002) to calculate BLEU scores, the rouge4 package to calculate ROUGE scores and the sentence-bert5 package to calculate sentence embedding similarities, respectively. Model Finetuning. We finetuned the models for 1 epoch using a batch size of 16, a warmup ratio of 0.03, a learning rate of 2e-4 and a cosine annealing learning rate scheduler.
3https://www.figma.com/legal/ community-free-resource-license/
4https://github.com/pltrdy/rouge 5https://github.com/UKPLab/
sentence-transformers

heading: D.2 Evaluation Metrics
text: Classification. For classification tasks, we employ metrics including macro precision, macro recall, macro F1-score, accuracy (Acc), and Area Under the Curve (AUC), reflecting the comprehensive assessment of the models’ tagging proficiency. Tagging. For the tagging task, we additionally leverage Hamming Loss and Jaccard index. Hamming loss (LHamming) is used to measure the fraction of labels that are incorrectly predicted:
LHamming = 1
N
N∑
i=1
1
|L|
|L|∑
j=1
XOR(yij , ŷij) (1)
where yij ∈ {0, 1} is a binary variable that indicates whether example i has label j. ŷij ∈ {0, 1} is the predicted binary variable. N is the number of examples in the dataset, and L is the set of labels. Jaccard index is defined as the size of the
intersection between the predicted labels and the ground-truth divided by the size of their union:
Jaccard = 1
N
N∑
i=1
|Yi ∩ Ŷi| |Yi ∪ Ŷi|
(2)
where N is the total number of examples. Ŷi and Yi are the set of predicted and ground-truth labels for example i. OCR. We use word error rate (WER), character error rate (CER), and BLEU scores (Papineni et al., 2002). The word error rate (WER) and character error rate (CER) are derived from the Levenshtein distance (Levenshtein et al., 1966), defined as:
WER = |WS |+ |WD|+ |WI |
|W | (3)
CER = |CS |+ |CD|+ |CI |
|C| (4)
where |W | and |C| are the number of words and characters in the ground-truth. |WS |, |WD|, and |WI | are the number of substitutions, deletions, and insertions at the word, and |CS |, |CD|, and |CI | are at the character level. Text Generation. We use n-gram-based metrics including BLEU (Papineni et al., 2002) ROUGE (Lin, 2004), METEOR (Lavie et al., 2004), and n-gram similarity (Kondrak, 2005). These metrics evaluate the MLLMs by measuring the lexical overlap between the generated text and the reference text. Meanwhile, we use two established similarity metrics based on pretrained language models, including BERTScore (Zhang et al., 2019) and sentence embedding similarity (Reimers and Gurevych, 2019), to measure the high-level semantic overlap between two answers. Specifically, BERTScore leverages contextualized word embeddings to capture a token’s usage in a sentence and encode sequence information. Sentence embedding similarity simsent is defined as the cosine similarity between the sentence embeddings of two answers:
simsent (si, sj) = si · sj
∥si∥∥sj∥ , (5)
where si is the embedding of the i-th response. Additionally, we calculate the length of response, defined as the number of words in a model-generate response.

heading: D.3 Details on Models
text: Table 12 contains the names and number of parameters of the language encoder and vision encoder for each of the models used in our study. Table 13 contains the accuracy scores of every classification task in our benchmark, examined across all of the zero-shot MLLMs.

heading: Future Work
text: For MLLMs to achieve broader and more reliable applicability, future versions should be trained on more tasks that cover ethical, social, and cultural dimensions, thereby ensuring a more comprehensive understanding and interaction capability in diverse contexts