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Unknowingly, it has been almost two years since I entered the 1v1 social field, and this experience has given me a deeper insight and systematic summary of this vertical track.
Previously, a fan left a message under my article asking: “From social product minions to management positions, in which directions should we focus our efforts?” ”
At that time, I shared that there are three core areas of operation work – operation strategy, risk control review, and market launch.
I even boldly believe that if you can integrate the underlying logic of these three modules and be familiar with the linkage link between them, if you have certain resources, then you basically have the potential to be competent for the position of operation director.
Recently, I happened to be reading Mr. Huang Renyu’s “Fifteen Years of Wanli” and had some new insights into the historical cycle and organizational operation.
He also talked about the key module of “risk control audit” that he was rotated to be responsible for before.
Therefore, I decided to combine my recent work experience with my past experience, starting with the challenging and valuable field of “risk control audit”, and conduct a series of sharing records and summaries.
1. Risk control review: the invisible guardian of social platforms
In the daily operation of social platforms, “risk control audit” plays a crucial role.
It is the core link to ensure the healthy and stable operation of the platform and maintain a good atmosphere in the community.
Specifically, the main task of risk control audit is to effectively review and process user behavior and generated content (such as text, pictures, audio and video, etc.) on the platform to reduce platform risks.
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A basic understanding is that as long as there is a user-generated content (UGC) module in the social application, the intervention of the risk control review mechanism will inevitably be required, and access to third-party machine review + human review can escort the entire platform.
The scope of review covers from basic information (such as avatar, nickname, personality signature, voice signature, etc.) when users register to various content posted and uploaded by users on the platform (such as community posts, articles, live room interactions, etc.).
We often say, “where there are people (traffic), there are rivers and lakes”, and similarly, places with high traffic will inevitably gather risky users who seek profit.#流量
For example, black and gray industry practitioners (malicious marketing, fraud, drainage, etc.), speculative groups that try their best to “gather wool”, and the use of robots for automated chats and even fraud.
Therefore, risk control audits can be simply understood as the continuous “offensive and defensive roles” between the platform and potential risks. When the volume of the platform gradually increases and the user traffic surges, the risk control audit is like a firewall, escorting the good atmosphere of the entire community.
2. My role: the middleman, connecting the upper and lower levels
So, in this “offensive and defensive war” without gunpowder, what is my specific job?
In fact, the core is to connect front-line insights and strategy formulation and promote implementation.
Closely connect with the frontline review students, and systematically sort out, summarize and dig deep the reports, feedback, verification and other information collected by them in their daily work.
Based on these data and phenomena, existing risk control rules will be adjusted, or new risk control rules will be formulated according to the law, and then collaborated with the development team to effectively deploy these rules and policies into the product. There are parameters in the rule that can be customized by back-end students. It is convenient to adjust and switch at any time.
After the rules are launched, the data should be read in time (to prevent mistakes, missed seals, etc.), determine the hit rate of the rules, and adjust the strategy in time. According to the actual operation situation and the changing risk situation, the rules are periodically adjusted, optimized and improved until the best balance is reached.
This is a closed-loop process of information collection – > analysis mining – > rule formulation – > technology implementation – > online verification – > continuous iteration.
3. The dynamic evolution of audit standards
Product Phase 0-1 (Foundation Framework Construction Period): At this stage, the first task is to quickly build and launch a basic set of audit standards.
The core goal of these standards may be relatively “extensive” in the early stages, and their core goal is to help review students quickly and broadly filter and process those notes that clearly violate laws and regulations, especially high-risk information such as “pornography, gambling, and drugs”, so as to provide a relatively clean environment for the cold start of the product and the accumulation of early users. However, it must be emphasized that the content ecology is dynamically changing, so the review standard must not be static, and it needs to be flexibly adjusted and revised immediately according to the actual situation.
Product 1-100 stage (refined operation and risk confrontation period): When the product passes the initial stage, the user volume and platform traffic reach a certain scale, and the review standards also enter the stage of refinement and systematization.
What may have been only 10 basic audit rules at the beginning often evolves into a detailed audit specification document containing hundreds (e.g., 300+) detailed rules and scenario definitions after a long period of case accumulation, data analysis, and market changes. This dynamic “code” is constantly updated as user behavior changes, new risks emerge, and regulatory policy adjustments. The vitality of audit standards lies in their continuous precipitation, iterative validation, and agile revisions. Here, I would like to share an unconventional but effective tip that I have summarized in practice:
When encountering some content with blurred boundaries and difficult scales, and internal discussions cannot reach an agreement, you can try to publish similar content anonymously on other mainstream and mature social platforms to observe their moderation results and scale. This often provides us with a valuable reference to help us make more rational judgments.
Entering the 1-100 stage, the product has a considerable basic traffic, and the user sources have become more complex and diverse.
At this time, all kinds of black industry organizations and professional “wool” gangs will flock to them like sharks smelling blood, waiting for opportunities to make profits. If it cannot be identified and dealt with in a timely and effective manner, it can seriously affect the experience of normal users, damage the reputation of the platform, and may lead to loss of control of content or even catastrophic consequences such as being forced to remove products from the shelves.
In order to respond to these organized and large-scale risk behaviors more professionally and efficiently, we usually choose to access mature third-party risk control products and services (such as the well-known “Yidun” and “Shumei” in the industry). These professional tools can help us more effectively identify and combat malicious behaviors, such as batch registration, fake accounts, malicious content dissemination, etc.
In daily risk control work, some basic and commonly used risk control mechanisms include:
1. Sensitive word blocking/interception:
The platform maintains a sensitive thesaurus that is dynamically updated. For example, some platforms include the names or keywords of related competitors in order to prevent users from maliciously draining traffic to competitors. When these sensitive words are triggered by content posted by users, the system may automatically block or appear as a successful delivery but cannot be viewed by the other party. Therefore, review and operation students need to continue to pay attention to market dynamics and user feedback, and constantly supplement and optimize this sensitive thesaurus.
2. Related ban:
Once an account is confirmed to have serious violations (whether automatically identified by the system or manually reviewed) and banned it, the system can trace and block other linked accounts suspected of being controlled by the same gang based on the associated characteristics of the account (such as registered IP, device ID, behavior pattern, etc.). This approach can effectively combat the black industry chain and significantly save the cost of manual review.
3. Whitelist/Grey List Strategy:
Whitelists can be set up for specific types of users (e.g., high-value users, official partner accounts, etc.), and their content may enjoy a higher moderation priority or a slightly looser scale under the premise of compliance. On the contrary, some accounts with potential risks but have not yet reached the level of direct banning can be included in the gray list for key monitoring or behavioral restrictions. In addition, it can also be combined with user profile and behavioral data to optimize user experience and traffic distribution efficiency by not displaying or pushing certain content/users that may cause disgust or discomfort to specific users.
4. Login and behavior anomaly detection:
Real-time monitoring and pattern analysis of users’ login behavior (such as frequent login to different places in a short period of time, logging in with non-use devices, etc.) and a series of operation behaviors after login (such as an abnormally large number of followers, likes, comments, etc. in a short period of time) are carried out, and abnormalities are detected and warned in a timely manner.
4. The “hardest hit area” of 1v1 social risk control: “suspected robots” and “professional wool picking”
In the mature 1-100 stage of 1v1 social products, the core focus of risk control is in addition to the above-mentioned basic violations (such as malicious drainage, abusive harassment, etc.), there are two “hardest hit areas” that are particularly worthy of our focus and severe crackdown, that is, “suspected robot” behavior and “professional wool gathering” behavior.
For 1v1 social products, their core value propositions are often “real-time interaction with real people” and “emotional companionship”. This model itself may wander in a certain “edge” zone to some extent.
If such a platform is filled with a large number of “robot” programs impersonating real users for chat interaction, or even using this to deceive users into paying or obtaining platform revenue, then its risk factor will increase sharply, which will not only seriously damage the experience and trust of real users, but also may touch the regulatory red line and bring disaster to the platform.
(There is no shortage of lessons learned from the past in the industry, I remember that in the past few years, some social products were reported by a large number of users and exposed by the media that there was a proliferation of bots on their platforms – for example, users would receive a large number of formatted and templated greetings from the “opposite sex” as soon as they registered and went online, and were named for problems such as well-designed “beauty chat” traps).
Therefore, for any responsible 1v1 social platform, severely cracking down on users’ use of plug-in chatbots or the platform’s own use of bots to deceive users is a lifeline to maintain the platform’s reputation, user trust, and business continuity.
To effectively manage and identify these high-risk behaviors, the general admin backend typically designs and relies on two key feature pages:
Role of the risk list: This list is used to focus on users with abnormal behavior that are automatically flagged by preset risk control rules (e.g., newly registered accounts have followed 50 users of the opposite sex in a row within 5 minutes, high-frequency matching requests in a short period of time but the connection rate is extremely low, and the content of the speech is highly repetitive or irrelevant to the context, etc.).
Processing process: Operators can customize whether these flagged users need to enter the manual review queue based on the risk level and policy. If it is set to require manual review, the moderating student association will intervene and conduct detailed verification of the specific behavior logs and content records of these users to determine whether there are real violations, and take corresponding measures (such as warnings, bans, restricted functions, bans, etc.).
The role of the ban list: This list is mainly used to record all user accounts and related information that have been manually or systematically confirmed to have clear violations in the risk list and have been blocked.
Core value: This list is not just a simple banning record book, but also an extremely valuable data goldmine. Through in-depth analysis and summary of user behavior patterns, types of violations, source channels and other information in the ban list, we can discover new trends in black and gray industries, identify blind spots in risk control rules, and refine more accurate user behavior portraits, so as to feed back and optimize our overall risk control strategy and rule system. So, how to judge the behavior of “suspected robot” and “suspected wool gathering”?
This does not depend on a single, perfectly universal rule, because the means of black and gray are constantly learning and evolving. We need to make comprehensive and cross-judgments from multiple dimensions of data and behavioral characteristics, and continuously find and summarize their common characteristics.
Common dimensions for determining “suspected robot” behavior:
- Reported data: Reported by multiple real users as “non-real”, “harassing” or “fraudulent” in a short period of time.
- Audio and video call behavior: The call duration is generally very short (such as hanging in seconds), or the audio and video call request is never initiated/answered.
- Device and IP environment: whether to log in using the emulator; Whether the IP address is concentrated in a specific geographical area, IDC room, or a known proxy IP.
- Interaction behavior pattern: Abnormal active hours: Often maintain high frequency activity during non-mainstream users’ online hours such as late night or early morning.
- Reply logic is confusing: After reviewing the screenshot chat message report provided by the user, after receiving the voice message sent by the other party, immediately reply to some preset text content without listening or for a very short listening time.
- Templated replies: Frequently and heavily reply with highly similar or identical shortcuts and template phrases, lacking personalization and emotional expression. The interaction mode is single and lacks contextual understanding.
- Withdrawal behavior (if the platform involves revenue withdrawal): Whether the withdrawal behavior such as the source, frequency, and amount of chat revenue conforms to normal user logic.
Common characteristics of “suspected wool picking” behavior (especially in products with cash incentives, subsidies, or virtual currency giveaways):
- IP address concentration and anomalies: A large number of registered or active IP addresses of accounts are concentrated in specific overseas regions (for example, some Southeast Asian countries have historically been high incidence areas for wool harvesting), or a large number of proxy IPs are used.
- Device association and bulk registration: A large number of new accounts are continuously registered for the same or a small number of device IDs in a short period of time (e.g., bulk registration of male accounts to take advantage of the initial virtual currency or experience privileges that the platform may offer to new male users).
- Benefit transfer behavior: Quickly and targetedly transfer platform benefits obtained through wool harvesting (such as virtual currency, experience cards, etc.) to a few “large” or “small” (for example, using batch registered male accounts to give a large number of virtual gifts to female anchor accounts controlled by oneself to cash out).
5. Conclusion
The above is my recent work experience, it is not static, with a deeper understanding, there will be more different experiences, and then update version 2.0.
The core of risk control audits is to stay close to the front line of business and deeply analyze user behavior patterns. The means of black and gray production are constantly evolving, and the risk control rules must keep pace with the times. Only through data precipitation, continuous optimization and flexible adjustment can we maintain our advantages in offensive and defensive confrontations and escort the platform.
