Telegram robots are becoming more and more like real people, and data traces are becoming a breakthrough

One of the biggest changes in Telegram in recent years is that bot accounts are becoming increasingly difficult to identify. In the past, many abnormal accounts could be seen at a glance. They had blank avatars, simple information, and mechanical behavior. They could be distinguished with a little observation. But now, a large number of bots and batch accounts have begun to actively imitate the behavior of real people.

Many accounts will:

lUse a real person avatar

lAdd nickname and introduction

lSimulate normal chat rhythm

lFaking long-term online status

lMix in real social interaction

This has also led to more and more teams discovering that simple active detection is no longer enough. Especially in the scenarios of encryption, community recruitment, overseas promotion and private domain operations, robot traffic is becoming more and more like real people.

WhyTelegram bots are getting harder to identify

Telegram itself is a highly open platform.

Compared with many traditional social platforms,Telegram:

lLow registration threshold

lRich automation tools

lGroups and channels spread quickly

lLarge-scale community operation

Therefore, a large number of automated accounts will naturally gather inIn the Telegram ecosystem.

As robot technology becomes more and more mature, many accounts are now more than just"Automated messaging" is actively simulating a real person.

For example:

lGo online on time

lSimulate normal reply interval

lUse photorealistic avatars

lJoin multiple public groups

lMaintain basic interactive behaviors for a long time

This makes traditional identification logic more and more ineffective.

Why simply looking at activity is no longer enough

In the past, many teams would focus on:

lIs online

lIs it active?

lWhether to join the community

lIs there any interaction record?

But now the problem is:

Robots can also"Fake active".

Many abnormal accounts will remain online for a long time and even actively participate in some interactions. If you only look at activity, it is easy to misjudge low-quality robot traffic as real people.

Therefore, more and more teams are now turning their focus to data traces.

Why data traces are becoming a new breakthrough point

Compared with short-term behavior, data traces are more difficult to forge in the long term.

Because real-person accounts that have been used for a long time usually leave a lot of continuous usage characteristics.

For example:

lAvatar long-term stability

lNickname changes are logical and natural

lThe introduction and information are more complete

lThe path to join the community is more realistic

lLong-term interactive behavior is more continuous

Even if many robot accounts can imitate some behaviors, it is difficult to maintain complete traces of use for a long time.

Therefore, more and more teams now include:

Data completeness

avatar long-term

interactive continuity

long-term social behavior

Incorporate screening logic together.

Which industries are most vulnerable to bot traffic?

Not all industries will be seriously affected by robot traffic, but the following scenarios will be particularly obvious.

crypto industry

The proportion of robots and wool accounts is very high.

Telegram community attracts new users

A large number of fake users will dilute the quality of the real community.

Overseas promotion

Abnormal traffic will make data analysis increasingly distorted.

Long-term private domain operation

Low-quality accounts will slow down customer service and conversion efficiency.

These industries have a common characteristic: they all rely heavily on real long-term users.

Why are more and more teams now paying attention to real-person trace recognition?

In the past, many teams paid more attention to:

lNumber of people in the group

lNumber of messages

lActivity rate

Now more and more teams are paying attention to:

llife proportions

llong-term interaction capabilities

lProportion of high-quality users

Because the number of people in the community is no longer equal to the real value.

Especially in encryption and private domain scenarios, if a large number of robots mix in:

lThe social atmosphere will become increasingly fake

lCustomer service communication efficiency will decrease

lSubsequent conversions will get worse and worse

Therefore, more and more teams are beginning to“Real person identification” is put into the front-end screening stage.

existBefore Telegram screening, it is more stable to complete the basic test first.

If the account itself exists:

lAbnormal state

lInvalid for a long time

lBatch behavior traces

Then subsequent community operations will become more and more difficult.

In actual operation, you can first use Digital Planet for screening detection, and then combine data traces and long-term active behaviors for further identification. Digital Planet supports free trial screening test.

In this way, obviously low-quality accounts can be filtered out in advance.

Why will future account identification rely more and more on combined tags?

In the past, many teams preferred single-dimensional judgments, such as:

lonline status

lactive frequency

lHave you sent a message?

But now these dimensions are increasingly easy to fake.

Therefore, the future will increasingly rely on combination judgment:

lactive behavior

lData completeness

lTraces of long-term use

lcommunity relationship structure

lAvatar stability

lDevice and region labels

Because it has become increasingly difficult to accurately identify real people with a single label.

WhyTelegram communities increasingly rely on high-quality real users

Many teams will later discover:

What really determines the quality of a community is not the number of people, but the proportion of real people.

If there are too many bots and low-quality accounts:

lGroup interactions will become more and more fake

lRetention will get worse and worse

lUser trust will decline

lConversion paths will become increasingly unstable

Although the number of high-quality real users is not that exaggerated, their long-term value will be significantly higher.

Robots are becoming more and more like real people, but long-term traces are still difficult to fake

Short-term behavior can be simulated, but long-term usage traces are difficult to fully replicate.

Accounts that are truly used for a long time will usually form naturally:

lstable data structure

llong-term interaction rhythm

lMore authentic social behavior

lContinuous usage trajectory

Even if a robot can imitate some behaviors, it will be difficult to maintain this natural state for a long time.

Therefore, the futureThe focus of Telegram account screening is no longer just "whether it is a robot", but also:

Which users are really worthy of long-term operation?

Telegram operations are moving from "traffic competition" to "real person competition"

In the past, many teams compared:

Which group has more people.

Who recruits people faster?

Now more and more teams are starting to compare:

Which one has a higher proportion of real users?

Who has more long-term active users?

Whose community is more stable?

Because what is really important in the future is no longer simply expanding traffic, but having long-term high-quality real users.

The reason why data traces are becoming more and more important is essentially because it is becoming the most stable breakthrough point to distinguish between real people and pseudo-real people.

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