# CHASM: Unveiling Covert Advertisements on Chinese Social Media

arXiv: https://arxiv.org/abs/2604.20511 · HTML: https://mlsanity.com/paper/2604.20511/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 53 references that are in the map.
- First posted on arXiv 22 Apr 2026.

## Builds on (most cited first, 8 of 8)

- [GPT-4o System Card](https://mlsanity.com/paper/2410.21276/) (2024)
- [InternVL: Scaling up Vision Foundation Models and Aligning for Generic Visual-Linguistic Tasks](https://mlsanity.com/paper/2312.14238/) (2023)
- [ChatGLM: A Family of Large Language Models from GLM-130B to GLM-4 All Tools](https://mlsanity.com/paper/2406.12793/) (2024)
- [Automatic Understanding of Image and Video Advertisements](https://mlsanity.com/paper/1707.03067/) (2017)
- [Zoom Out and Observe: News Environment Perception for Fake News Detection](https://mlsanity.com/paper/2203.10885/) (2022)
- [Customized large language models can outperform Community Notes in correcting misinformation](https://mlsanity.com/paper/2403.11169/) (2024)
- [TH-Bench: Evaluating Evading Attacks via Humanizing AI Text on Machine-Generated Text Detectors](https://mlsanity.com/paper/2503.08708/) (2025)
- [LLM generated responses to mitigate the impact of hate speech](https://mlsanity.com/paper/2311.16905/) (2023)

## Built on by (most cited first, 2 of 2)

- [What Your Posts Reveal: A Benchmark and Agentic Framework for User-Level Privacy Leakage on Social Media](https://mlsanity.com/paper/2606.06784/) (2026)
- [TransMeme: A Multi-Agent Framework for Cross-Cultural Meme Transcreation](https://mlsanity.com/paper/2608.27127/) (2026)

## Abstract (arXiv)

Current benchmarks for evaluating large language models (LLMs) in social media moderation completely overlook a serious threat: covert advertisements, which disguise themselves as regular posts to deceive and mislead consumers into making purchases, leading to significant ethical and legal concerns. In this paper, we present the CHASM, a first-of-its-kind dataset designed to evaluate the capability of Multimodal Large Language Models (MLLMs) in detecting covert advertisements on social media. CHASM is a high-quality, anonymized, manually curated dataset consisting of 4,992 instances, based on real-world scenarios from the Chinese social media platform Rednote. The dataset was collected and annotated under strict privacy protection and quality control protocols. It includes many product experience sharing posts that closely resemble covert advertisements, making the dataset particularly challenging.The results show that under both zero-shot and in-context learning settings, none of the current MLLMs are sufficiently reliable for detecting covert advertisements.Our further experiments revealed that fine-tuning open-source MLLMs on our dataset yielded noticeable performance gains. However, significant challenges persist, such as detecting subtle cues in comments and differences in visual and textual structures.We provide in-depth error analysis and outline future research directions. We hope our study can serve as a call for the research community and platform moderators to develop more precise defenses against this emerging threat.