Exploring and Detecting Self-disclosure in Multi-modal posts on C hinese Social Media (2025)

Abstract: Self-disclosure can provide psychological comfort and social support, but it also carries the risk of unintentionally revealing sensitive information, leading to serious privacy concerns. Research on self-disclosure in Chinese multimodal contexts remains limited, lacking high-quality corpora, analysis, and methods for detection. This work focuses on self-disclosure behaviors on Chinese multimodal social media platforms and constructs a high-quality text-image corpus to address this critical data gap. We systematically analyze the distribution of self-disclosure types, modality preferences, and their relationship with user intent, uncovering expressive patterns unique to the Chinese multimodal context. We also fine-tune five multimodal large language models to enhance self-disclosure detection in multimodal scenarios. Among these models, the Qwen2.5-omni-7B achieved a strong performance, with a partial span F1 score of 88.2%. This study provides a novel research perspective on multimodal self-disclosure in the Chinese context.

## introduction an increasing number of users engage in selfdisclosure within online social platforms, openly sharing information about themselves @xcite . the types of disclosed information are diverse, ranging from sensitive attributes such as health status @xcite , gender @xcite , and sexual orientation to less sensitive content like emotional expressions, life experiences, and personal preferences. online self-disclosure is often driven by multiple motivations, including the desire for belonging, emotional support, social connection, and adherence to community norms and interaction conventions @xcite . while self-disclosure offers cer-* corresponding author. tain psychological and social benefits, it also carries significant privacy risks @xcite . in particular, under unintentional circumstances, users may inadvertently reveal sensitive personal information, which, once identified or misused by others, can lead to adverse real-world consequences such as harassment, discrimination, or limited employment opportunities. moreover, as multimodal contents within online communities continue to proliferate, the dimensions of potential privacy violations become increasingly complex, making identifying and mitigating these emerging risks crucial. existing research on self-disclosure has primarily concentrated on single-modal textual content within english-language contexts @xcite @xcite . there is a notable lack of studies regarding self-disclosure in other language environments, particularly in the chinese context. as multimodal content becomes increasingly prevalent on social media platforms, users often rely on a combination of images, text, emojis, and other media to express themselves, which raises higher demands for self-disclosure detection models. traditional approaches primarily rely on single-text modality models such as bert and roberta, which exhibit limited capability in modeling cross-modal semantic correlations @xcite , leading to suboptimal performance in multimodal data processing tasks. to more accurately characterize self-disclosure expressions in chinese multimodal social media and identify potential privacy risks associated with them, there is an urgent need for more in-depth and systematic research. in our work, we design a crowdsourcing task to systematically collect user-generated multimodal (text and image) posts related to personal information from the rednote platform, thereby constructing a multimodal corpus. based on this corpus, we leverage the existing self-disclosure category framework @xcite and conduct an indepth analysis of the distribution patterns and expressive forms of various types of self-disclosure. finally, we fine-tune multimodal large language models (mllms) to develop an automated selfdisclosure detection model to alert users to potential privacy risks. specifically, we create a high-quality dataset with human annotations on 4,870 rednote multimodal posts foot_0 . we systematically analyse their overall distribution patterns, modality-specific posting preferences, inter-category correlations, and distributional variations under user intent scenarios. with this corpus, we fine-tune five mllms to identify the self-disclosures in the given post, achieving over 80% partial span f1 and select the bestperforming model as an automatic tool for identifying self-disclosures, aiming to assist users in detecting potential privacy risks. our key contributions include: online self-disclosure types previous studies on self-disclosure have predominantly focused on a single entity category within specific communities, such as healthcare or mental health @xcite @xcite @xcite . although some research qwen2.5 inference rseult booking an appointment with a young doctor? don't panic. if they can't handle it, they can call for backup. #dentistdaily [hashtag] #oraldoctor [hashtag] llava-1.5 image description llm-based pre-annotation annotation protocol development expert annotation with custom tooling quality assurance through cross-validation has begun to explore multi-category self-disclosure @xcite , much of it remains tied to specific event-driven contexts, such as online discussions during the covid-19 pandemic. only a limited number of studies have attempted to systematically expand the scope to cover a broader range of personal information @xcite , encompassing up to 19 distinct categories. these studies primarily focus on text-only modalities in english-language contexts, with limited systematic exploration of self-disclosure in chinese multimodal settings. we construct a chinese multimodal corpus aimed at advancing the identification and analysis of selfdisclosure behaviors within chinese multimodal contexts. ## chinese multimodal self-disclosure dataset we have constructed a small-scale chinese selfdisclosure multimodal dataset, which includes 4,870 user-generated posts combining text and images, shared on the rednote social media platform. crowdsourcing data collection we selected rednote foot_1 as the platform for social media data collection because its content is lifestyle-oriented and emphasizes sharing personal experiences. unlike platforms that primarily feature commercial promotions or entertainment content, rednote encourages users to actively document and discuss various aspects of daily life, such as interpersonal relationships, health conditions, educational experiences, category attribution social post identified value personal identity name the picture is a medical record, including information name: wang yuanyuan gender: female age: 29 years old clinic number: 2023019616 wang yuanyuan age the picture is a medical record, including information name: wang yuanyuan gender: female age: 29 years old clinic number: 2023019616 29 gender i started dating with my male roommate. i met this good man #daily love of two boys male location i love suzhou and xiangcheng. i just moved here for half a month and i successfully made an appointment. suzhou sexual orientation i started dating with my male roommate. i met this good man #daily love of two boys homosexual family and social relationship marital status don't worry, my future boyfriend, i'm still reading literature in the library during national day. unmarried husband/boyfriend don't worry, my future boyfriend, i'm still reading literature in the library during national day. none wife/girlfriend family, we had a two-hour video call tonight. family, family, i am almost hooked by my wife. she has asked me to call her sister many times before, but i was so shy that i couldn't open my mouth. today, in order to video chat with me, she actually called me "sister". the point is, she hopes that we will end our long-distance relationship sooner! girlfriend family status since the beginning of my pregnancy, i have been thinking about how i can be a good mother after my two babies are born. two children pet the first time i went to the flower, bird and fish market, i brought back a golden sun. i didn't know anything, so i started by feeding it, watching videos while making milk powder. let's give the parrot a name first. parrot health physical health i was diagnosed with advanced cancer at the age of 35 and i am now 41. it is fate that i met you all in this vast crowd of people. thank you all for your support and encouragement. come on to yourself, don't give up! cancer mental health i was diagnosed with moderate depression and anxiety at the end of september 2021. moderate depression and anxiety occupation and education occupation i have finished sewing the grapefruit peel, but i feel like my hands are still not satisfied. what should i sew next time? give me a new challenge. #surgeon# surgeon education information after i finish my master's degree, i will work hard to get a doctorate. master student economic and financial status financial status state-owned and private enterprises. monthly income of 40,000 yuan, taking advantage of the opportunity is saving money∼ monthly income: 40,000 yuan and consumption habits. therefore, the platform is well-suited for collecting authentic, life-related social media data. we designed a crowdsourcing task and recruited a team of 20 university students with rich experience using rednote from college communities. the task's goal was to manually collect social media posts with life-related attributes from rednote, excluding content containing advertising links and brand sponsorship statements. participants were advised to prioritize posts from users sharing their personal lives. they were required to collect information, including post titles, main content, and related images. a payment of $2 was paid for each successful post. we successfully collected and curated 4,870 high-quality multimodal (text-image) posts. further details about the crowdsourcing task can be found in the appendix a. self-disclosure classification we drew on the self-disclosure attribute classification proposed in previous research @xcite , removed specific inapplicable attributes, and reorganized the remaining ones to construct a new classification framework consisting of five core categories and fifteen key personal information attributes. ## llm-based self-disclosure detection model we frame the self-disclosure detection task as a question-answering task (specific examples are detailed in appendix d.1) and fine-tune five multimodal large language models using an annotated corpus of constructed self-disclosure. considering the limitations of resources, we selected models with a parameter size below 11 b. we chose those that performed relatively well on other tasks for fine-tuning: gemm3-4b-it foot_4 @xcite , internvl3-8b foot_5 @xcite , llama-3.2-11b-vision foot_6 , qwen2.5-vl-7b-instruct and qwen2.5-omni-7b foot_7 @xcite . additionally, we further fine-tuned the roberta model( cui ## conclusion we systematically constructed a novel chinese multimodal self-disclosure corpus based on social media data. we conducted an in-depth analysis of the distribution patterns and expressive forms of various types of self-disclosure. it provides a solid data foundation and theoretical reference for subsequent self-disclosure recognition and protection efforts. building on this corpus, we fine-tuned several multimodal large language models and evaluated their performance using partial span-level f1 scores. experimental results show that all fine-tuned llms achieved a partial span f1 score exceeding 80%. we ultimately best fine-tuned llm as the automatic recognition model to assist users in detecting potential privacy leakage risks.