Intelligent Twitter filtering system analysis to accurately identify high-quality Twitter user groups

Twitter’s user ecosystem is becoming increasingly “stratified.” On one side are highly active content creators, crypto community users, and brand followers, while on the other side are a large number of accounts with low interaction or even long-term silence. For teams doing overseas growth and social marketing, the real challenge is not “whether there are users”, but “which users are worth operating”.

The role of the intelligent Twitter filtering system is to make this fuzzy user structure clear.

Twitter user structure is changing

pastTwitter is more like an open information flow platform, and interactions between users are relatively random. But now with the entry of the encryption industry, Web3 projects and brand marketing, the user structure has obviously been "content-based".

Among the same group of users, some frequently tweet and participate in discussions, some only browse but do not interact, and some accounts have been inactive for a long time.

This structural change makes it impossible to judge user value simply by relying on the number of fans.

Why you need an intelligent screening system

If you only do basic attention relationship analysis, it is easy to ignore real active users.

For example, if an account has many fans but very low interaction, such users have limited marketing value; on the contrary, an account with a low number of fans but frequent interactions often has greater communication capabilities.

The core of the intelligent screening system is to“Interaction quality” is included in the judgment criteria.

high qualitySeveral key characteristics of Twitter users

In actual screening, high-quality users usually have several obvious characteristics.

Content is updated more frequently;

Participate in comments and retweets more;

Concentrated areas of focus, such as crypto, technology, finance, or branded content;

The account has a long history and stable behavior;

Interactive objects have a certain concentration of circles.

The combination of these characteristics constitutes a truly valuable user group.

Why just looking at the number of followers is no longer enough

The number of fans used to be the most intuitive indicator, but in the current environment, this indicator has been significantly distorted.

A large number of zombie accounts, marketing accounts and low-activity accounts are mixed in, making fan size no longer equal to influence.

What really affects the communication effect is the quality of interaction, not superficial data.

Intelligent filtering is changing social marketing

done in the pastTwitter marketing relies more on the "cast a wide net" approach.

Now more and more teams are beginning to conduct refined screening to only reach highly active user groups.

Although the user scale becomes smaller in this way, the conversion efficiency is higher and the content dissemination path is clearer.

The core of data screening lies in behavioral analysis

The key to the intelligent screening system is not the existence of an account, but user behavior.

Including interaction frequency, content type, attention structure and activity cycle.

Through these behavioral data, we can judge whether a user is worthy of long-term operation.

Social platforms are moving from"Traffic platform" becomes "relationship network"

The nature of Twitter is changing, from an information flow platform to a relationship network.

The connections, interactions and content influence between users are becoming more important than the number of fans.

This also further enhances the value of the screening system.

Application of Digital Planet in Intelligent Twitter Screening

In the actual data processing process, Digital Planet is usually used for batch filtering and tag addition of Twitter users.

After importing user data through the system, you canTwitter accounts perform basic status identification and combine behavioral tags for stratification, such as active users, low active users, and potentially influential users.

In some cross-border marketing andIn the process of the Web3 growth team, they will first use Digital Planet to complete the initial screening of Twitter users, and then overlay data from multiple platforms such as Telegram and WhatsApp for cross-validation to build a more complete user profile.

For example, a batch of user data from community activities can be screened out firstActive Twitter accounts are then screened to see if they are also active in the Telegram community, and finally, whether they have online consumption or transaction behavior is identified.

This multi-dimensional combination allows user screening to be expanded from a single platform to cross-platform identification.

from“Account quantity” to “influence quality”

The significance of the intelligent Twitter filtering system is not just to filter data, but to redefine user value.

When the screening dimension shifts from quantity to behavioral quality, marketing strategies will also change.

Accurately reaching a small number of high-value users is often more effective than reaching a large number of low-quality users.

The core of social marketing is also shifting from traffic acquisition to influence identification.

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