Abstract
In this paper we provided the definition of the Audience overlap network, as well as proposed a simple algorithm to compute overlap between two users on social media based on public data about their followers. There was proposed an alternative approach for computing overlaps based only on public data about users. This approach allows to include content overlap and activity patterns signals to be incorporated into more general statistical models featuring other covariates such as influencers’ direct engagement in shared conversations; relative influencer sizes and histories and links to similar third-party content to recover otherwise censored network structures and properties. For validate results there was designed a calibration process which utilizes Evolution Strategies algorithm to find a set of conditions which will make Audience overlap network built using similarity measures structurally equivalent to the Audience overlap network build on full information about followers.
Highlights
With growing number of users in recent years, social media platforms have become the prime place for public discourse, and a main source of news for many people
In this paper we provided the definition of the Audience overlap network, as well as proposed a simple algorithm to compute overlap between two users on social media based on public data about their followers
To map social media influencers to their audiences we explore a general approach for building Audience overlap networks (AONs)
Summary
With growing number of users in recent years, social media platforms have become the prime place for public discourse, and a main source of news for many people This massive development increased a need of their analysis on structural and content levels. While platforms like Twitter or Reddit grant unrestricted access to lists of followers or members of influential channels and profiles, others like YouTube or Facebook do not grant similar access or otherwise restrict its granularity. This puts additional pressure for general approach of building AONs but at the same time opens opportunity for exploration of deeper connections between influencers and their audiences
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