Designing a Credit-Based Content Management System: Strategies, Risk Assessment, and AI Techniques

When the security field first emerged, its logic resembled today’s content domain: discovering bad cases based on expert rules, but as PC software proliferated and expert resources became scarce, a credit system was proposed to evaluate companies and allocate resources accordingly.

Content platforms typically involve three types of content: UGC (user‑generated content), PGC (professionally generated content), and OGC (occupationally generated content), with examples ranging from review sites to public accounts and short‑video platforms.

The strong social, random, and operational nature of open content platforms creates conflicts among producers, platforms, and consumers, requiring the platform to act as police, judge, and auditor to balance ad revenue, content quality, and user experience.

A credit system architecture is introduced: after a content producer submits material, a credit audit determines trustworthiness, followed by upload audit, AB testing, tiered release, and, if necessary, a recall strategy based on risk assessment.

Credit rating applies to merchants (certified and individual) and users (VIP and regular), as well as to the platform’s own managers; examples illustrate how dead merchants selling fake goods exploit the system and how operational teams mirror judicial structures (Police, Court, Procuratorate) to handle disputes.

Three core questions are addressed: how to detect non‑compliant content, how cheat and anti‑cheat strategies operate, and how to handle low‑quality content that drives traffic.

For non‑compliant content detection, data flows through keyword filtering, followed by credit grading into levels (e.g., low, high, or tiered levels 1‑5), with monitoring of user trust signals such as view duration and reporting mechanisms.

Cheat and anti‑cheat examples include limiting a buyer’s monthly comments to six, flagging IP mismatches when over 20% of traffic is filtered, and defining credit degradation thresholds at 50% and 70% violations, using behavior records and similarity clustering to identify bad actors.

Low‑quality content traffic is modeled as a graph where users and content are nodes; when the average degree c = (n‑1)p exceeds 1, the platform becomes self‑operating, requiring threshold‑based controls aligned with the platform’s positioning.

Key interest areas for platform development include text classification, account credit management based on content similarity, and user credit management with tiered publishing; a Facebook‑based Word2vec algorithm and fastText are compared for text classification, noting the need for effective Chinese tokenizers such as Jcseg.

Account credit management leverages content similarity vectors (e.g., using intersection‑over‑union ratios) to cluster suspicious accounts, acknowledging commercial motivations that may prevent full remediation.

User credit management employs point‑based tiers (e.g., 20‑day premium, 60‑day normal, 90‑day low‑activity accounts) and AB testing to automate reviews, reducing manual audits while maintaining quality.

Future directions involve hotspot identification, negative sentiment monitoring across eight dimensions, and the TBT strategy for predicting event impact and stock price movements, though domestic implementation faces additional challenges.