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LinkedIn aiming at viral spam content detection

With More than 600 million professional profiles on LinkedIn, It provides an almost limitless number of networking opportunities and employment possibilities.

Using LinkedIn is an essential part of becoming a fully-fledged professional in any business these days, from looking for a new job to maintaining your personal brand.

People share their views, careers high-lows, and valuable discussions on this platform.

Content violating LinkedIn-Policy can ruin the essence and purpose of the Application. Considering the credibility, LinkedIn announced to create a trusted and safe environment by implementing various spam and policy-violating content detection measures.”

The platform ensures to keep users’ feeds as relevant as possible for a safe experience. It also mentioned how LinkedIn is not for “Virality,” but some posts that get likes, comments, and shares in large numbers go viral and appear on random feeds.

Identification of Viral spam content

LinkedIn launched several AI models based on Viral spam and “violative” content on the platform.

The two primary kinds of AI models used to identify objectionable content are as follows: two types of defenses: proactive and reactive.

In the proactive approach, the identification occurs as soon as the information appears on the LinkedIn feed, but in the reactive model, the platform watches the activity surrounding the uploaded content and then attempts to forecast its likelihood of being extensively shared.

The blog by the platform talks about various features differentiated on the basis of the ways through which content goes viral.

Features influencing Virality

There are several aspects that affect the engagement of a post.

These aspects include the type of material (text, picture, video, article, etc.), user engagement, and content circulation throughout the network of members.

These features are divided into two categories: member features and post features.

Post Feature

This includes the type of content, its action, and its spamminess. Analyzing these factors helps the platform to understand how the content got viral.

Member Feature

This feature focuses on the users who interacted with the post in any manner. Analyzing the features of this user might help in detecting early potential viral spam content.

LinkedIn is all set to keep the boundaries and fulfill the real agenda of the app. The platform is making constant efforts to keep it clean and safe for users.

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