Have there been more abnormal accounts in Telegram recently? Which tags to look at first during batch detection

existIn Telegram-related data usage scenarios, the topic of abnormal accounts has been discussed more and more recently. Not only those who operate Telegram community are paying attention, but those who do Telegram traffic drainage, Telegram precise user screening, Telegram batch screening, and Telegram number detection API have also begun to put the identification of abnormal accounts in a higher priority.

The reason is very practical. As the amount of data increases, the proportion of abnormal accounts mixed in is also increasing. Many people used to do itThe key point of Telegram screening is to determine whether it is registered, whether it can be imported, and whether it is activated. But now, if you still stay at this level, you will often find that the amount of data is not small, but there are not many users who can actually interact and convert.

This is whyTelegram's abnormal account identification is changing from an additional action to a basic action in Telegram's batch detection process.

WhyTelegram abnormal accounts have been mentioned frequently recently

The increase in abnormal accounts is not essentially a single point problem, but the result of changes in the data environment.

For one thing, the quality of data from many sources varies. No matter what you doTelegram user screening, community recruitment, or batch number detection, the raw data is often mixed with low-quality accounts, abnormal accounts, and even robot-like data. This type of account may have no problem in terms of registration status, but its actual value varies greatly.

on the other hand,Telegram operations increasingly focus on active users, rather than simply focusing on the number of users. In the past, there were 5,000 people in a group, which seemed to be a good size. Now, more attention is paid to how many highly active users there are, how many real accounts there are, and how many abnormal accounts need to be filtered in advance.

Therefore, abnormal account detection is mentioned frequently, not because it is suddenly important, but because it was ignored before and can no longer be ignored.

When detecting batches, which tags are usually looked at first for abnormal accounts?

DoWhen Telegram screens accounts in batches, if it wants to identify abnormal accounts first, it usually looks at a few tags first.

The first is the active status label.

Is an accountBeing active for 3 days, active for 7 days, or even online on the same day can help judge the quality of the account. The problem with many abnormal accounts is not that the account does not exist, but that there is no effective behavior for a long time. If there is no activity support at all, the value of such accounts is often low.

The second one is the abnormal behavior label.

This type of label is mainly used to identify accounts with abnormal behavior, such as unstable behavior patterns, abnormal usage status, or data with obvious risk characteristics. This step isTelegram's abnormal account screening is becoming more and more important.

The third is the account integrity label.

Including avatars, data integrity, and basic information structure. Although such labels are not absolute criteria for judgment, they are helpful in identifying low-quality accounts.

The fourth is the risk account label.

This type of label is more focused on risk identification, and manyPeople who use the Telegram number detection API will use it as a separate filtering condition.

From the perspective of batch detection efficiency, these four types of tags are often a combination with higher priority.

Why batch inspection order is critical

Many people focus on which tags to screen, but ignore the detection order.

In fact, the order will directly affect the screening efficiency.

If active users are screened from the beginning, but abnormal accounts are not filtered in advance, the active results themselves may be interfered with by low-quality data.

A more reasonable approach is often to identify abnormal accounts first and then screen active users.

First remove the obviously abnormal data and then make a judgment3-day active users and 7-day active users will get cleaner results.

This point isIt is very important in Telegram batch screening, Telegram user data cleaning, and Telegram accurate user identification.

Because the sieve number is not simply to add a few more labels, but to make the order of the labels reasonable.

more practicalHow to set up Telegram abnormal account screening process

From a practical perspective, a more stable process is usually divided into three layers.

The first level is basic account detection.

First determine the account activation status and basic availability, and filter obviously invalid data first.

The second layer is filtering abnormal accounts.

Use abnormal behavior labels, risk account labels, and account integrity labels to remove obviously problematic data.

The third level is to filter active users.

For example3-day active and 7-day active stratification to screen out highly active users individually.

The core of this logic is to clean the data first and then filter high-quality users.

At this stage, many teams will use Digital Planet toTelegram abnormal account identification, Telegram active user screening and Telegram data cleaning, layering processing of abnormal accounts, risk accounts, and highly active users, and then deciding whether to conduct subsequent community operations or precise contact.

This method is much more stable than simply looking at the registration status.

What results will be affected if abnormal accounts are not cleared in advance?

This impact is often greater than imagined.

First, response rates will drop.

Because abnormal accounts themselves do not generate effective feedback, and the proportion of such accounts in the data pool is high, the overall quality of access will naturally decrease.

Second, data judgment will be distorted.

For example, if you think it is a content problem, it may actually be because there are too many abnormal accounts in the user pool.

Third, the quality of the community will be lowered.

DoWhen operating a Telegram community, if a large number of abnormal accounts and low-quality accounts are mixed into the imported users, the group may have a good number of people, but it will be difficult to interact.

Therefore, abnormal account filtering is not essentially to delete data, but to improve the quality of the remaining data.

Why are active user screening and abnormal account identification increasingly being done together?

This is an obvious trend recently.

Many people used to thinkTelegram active user screening and abnormal account detection should be done separately, but now there is an increasing tendency to do them together.

The reason is simple. Active tags can help identify abnormal accounts, and identifying abnormal accounts can also improve the accuracy of active screening.

Combining the two is more stable than using them alone.

For example, first filter abnormal accounts through Digital Planet, and thenTelegram’s 3-day active user filtering and 7-day active user filtering, the quality of the filtered data is usually higher than direct active user filtering.

This is why there are so many nowTelegram’s batch screening process began to merge anomaly identification and activity detection into the same layer.

Why Telegram abnormal account detection is becoming a pre-action

In the past, many people would do the identification of abnormal accounts later.

Now more and more teams are putting it front and center.

The reason is not that the process becomes more complicated, but that pre-processing is more efficient.

Clean up the abnormal accounts first, and then do whateverTelegram’s precise user screening, Telegram’s traffic drainage, or community accumulation will all be smoother.

And from the perspective of resource utilization, it is also more economical.

Because all subsequent actions are based on a cleaner data pool.

From registration screen numbers to anomaly identification,Telegram screening logic is being upgraded

Telegram’s screening logic is indeed changing.

In the past, the focus was on whether the account was registered, but now the focus is on whether the account quality is high; in the past, more attention was paid to quantity, and now more attention is paid to the proportion of high active users; in the past, abnormal account identification was regarded as an additional item, but now it is increasingly regarded as a basic item.

Behind this is actually the same change——Telegram user screening is moving from basic number detection to more refined data screening.

In this process, abnormal account identification is no longer a question of whether to do it, but a question of which step to take first.

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