Telegram fake users are more like real people recently, and traditional robot filtering logic is

existIn Telegram user screening, one change is becoming more and more obvious: fake users are no longer as easy to identify as before. In the past, many teams relied on simple robot filtering logic, such as checking whether there is an avatar, whether the information is complete, and whether the behavior is abnormal. These methods were indeed effective for a period of time. But now more and more fake users are beginning to simulate real behaviors. Their profiles look complete, their avatars are normal, and in some cases they can even behave like active users. This makes traditional filtering logic gradually lose its effectiveness.

If the screening stage still stays in the old method, it will be easy to regard such fake users as real users and put them into the data pool.Telegram traffic drainage or community operation will have the problem of large data volume but unstable conversion. This is why Telegram’s fake user identification has recently begun to move from single judgment to multi-dimensional combination judgment.

WhyTelegram fake users are harder to identify these days

Essentially a fake user"Evolved". In the past, many fake accounts had simple structures, incomplete information, and single behaviors, which were easily filtered out through basic rules. But now many fake users have begun to simulate the structure of real users, such as adding avatars, filling in information, and maintaining basic active status. These behaviors make them superficially closer to real users.

The direct result of this change is that the hit rate of traditional screening logic decreases. In the past, a set of rules could filter out most abnormal accounts, but now under the same rules, more fake users will slip through the net.

So the problem is not with the screening tool, but with the fact that the screening logic has not changed.

What are the main failures of traditional robot filtering logic?

The first failure point is over-reliance on a single label. For example, only looking at avatars or only looking at completeness of information. Such labels are now easily forged.

The second problem is ignoring the behavioral layer. A lot of old logic only looks at static information, but not user behavior, and now fake users are very close to real users in terms of static information.

The third problem is that there is no multi-dimensional combination. In the past, it could be judged at a single point, but now it is difficult to distinguish between true and false without combining active status, abnormal behavior, and account structure.

So it’s not that the original methods are completely useless, but just using these methods is no longer enough.

Fake users look like real people, which data scenarios are most likely to get mixed into them?

The most likely problem is the batch import data scenario. For example, doWhen Telegram is screening numbers in batches, recruiting new members from communities, and expanding its user pool, if it only performs basic number detection, it will be easy to import fake users together.

Secondly, when screening highly active users, some fake users will enter the active user range through simulated behavior, which will directly affect the subsequent reach effect.

Another is the community operation scenario. The number of group members seems normal, but the interaction is not possible. This is often because the proportion of fake users is high.

What these scenarios have in common is that the amount of data is large and the filtering is not detailed enough.

do it nowTelegram fake user filtering, what judgment dimensions should be added?

The first is the active status, but you can’t just look at whether it is active, but also combine it with the time window, such as3-day active and 7-day active are judged separately.

Next is the abnormal behavior label. This layer is becoming more and more important and is used to identify accounts that do not conform to normal usage logic.

Another one is the data structure, but it cannot be used alone, but as an auxiliary judgment.

There is also risk account identification, which can further filter potentially problematic data.

The combination of these dimensions can approach real user identification instead of relying on single-point labels.

How to cooperate with active detection, abnormal behavior and data integrity

A more stable way is to do basic detection first, then do anomaly filtering, then screen active users, and finally use data completeness to make auxiliary judgments.

If the order is reversed, such as looking at the information first and then the activity, it is easy to be misled by the fake user structure.

This is why the screening process now emphasizes order more than simply increasing the number of tags.

Because the order is wrong, no matter how many tags you add, they may be invalid.

How Digital Planet can help with more detailed identification of real and fake users

At this stage, many teams will use Digital Planet toTelegram account detection, active user screening, abnormal account filtering and multi-dimensional combination judgment. Digital Planet supports free trial screen number detection, which can combine the dimensions of active status, abnormal behavior, and data structure to filter, which is more suitable for the current fake user environment than single label judgment.

The value of this method does not lie in one more step of screening, but in separating the real and fake users so that subsequent traffic diversion and contact can be based on cleaner data.

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