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Founder's Pitch
"A novel framework for detecting covert multimodal toxicity using graph-based metrics."
Commercial Viability Breakdown
0-10 scaleHigh Potential
2/4 signals
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3/4 signals
Series A Potential
3/4 signals
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arXiv Paper
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Why It Matters
Multimodal content, including text and images, is becoming increasingly prevalent, and the ability to detect covert toxic messages within these formats addresses a growing need for platforms requiring sophisticated content moderation.
Product Angle
Develop a content moderation tool for social media and online platforms that integrates with existing systems to provide deeper analysis of hidden toxic content through an intuitive dashboard.
Disruption
This could replace or augment current multimodal content detection systems that often fail to capture context-dependent harm in image-text combinations.
Product Opportunity
The social media moderation market is growing rapidly; platforms will pay for advanced technology that can filter more sophisticated forms of toxicity. Clients include Facebook, YouTube, and Twitter, which need to comply with increasing regulations.
Use Case Idea
An API for social media platforms that detects covert toxic content using TAGs to prevent content with hidden malicious intent from spreading.
Science
The research uses Toxicity Association Graphs (TAGs) to model semantic associations in multimodal data, introducing a metric called Multimodal Toxicity Covertness (MTC). By mapping semantic links between text and images, the system can detect both overt and covert toxicity, utilizing a newly constructed dataset for validation.
Method & Eval
Used the newly built Covert Toxic Dataset to benchmark the TAG-based approach, showing it outperforms existing models in detecting both overt and covert toxicity, offering interpretability and transparency in its outputs.
Caveats
The system's reliance on predefined toxic pairs from knowledge bases could lead to missed new forms of toxicity if not regularly updated. The cultural specificity of toxicity interpretations could limit use across different regions.
Author Intelligence
Guanzong Wu
The Chinese University of Hong Kong, Shenzhen
guanzongwu@link.cuhk.edu.cn
Zihao Zhu
The Chinese University of Hong Kong, Shenzhen
zihaozhu@link.cuhk.edu.cn
Siwei Lyu
State University of New York at Buffalo
siweilyu@buffalo.edu
Baoyuan Wu
The Chinese University of Hong Kong, Shenzhen
wubaoyuan@cuhk.edu.cn