<b>Beyond the Disclosure Trap: An ANT-enhanced CVAE-LLM-BERT Framework for Algorithmic Tra

25.7 MB of code and documents implement the ACLB framework for automated transparency assessment. Authored by Hui Xia and released under CC-BY-4.0, the dataset includes Python code, documents, and spreadsheets related to an empirical study of five major Chinese social media platforms. It was last updated on May 16, 2026.

Use Cases

• Benchmarking algorithmic transparency assessment frameworks based on the described ACLB methodology.
• Analyzing compliance patterns in social media platforms based on the empirical study of five Chinese platforms.
• Training NLP models for legislative text categorization based on the described CVAE and BERT components.
• Studying deceptive compliance mechanisms in AI systems based on the framework's findings.

Strengths

• Includes source code (PY files) and documentation (DOCX, TXT) for a complete research framework implementation.
• Empirical evaluation is based on a study of five major Chinese social media platforms.
• Released under the permissive CC-BY-4.0 license for broad reuse.

Limitations

• Row count and sample data are unavailable, limiting suitability assessment for direct data analysis.
• Column-level documentation is absent; field semantics must be inferred after download.
• Data may reflect geographic bias inherent to its focus on Chinese social media platforms.

Provenance

Source

Hui Xia via figshare.

Collection Method

Likely contains research outputs including code, analysis, and documentation for the ACLB framework.

Freshness

Last updated 2026-05-16 01:57:16; freshness should be verified.

Geography

Empirical study focuses on Chinese social media platforms.