Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

11-2019

Abstract

We analyze cross-platform factors for posts on both single and multiple social media platforms for numerous news outlets to better predict audience engagement, precisely the number of likes and comments. We collect 676,779 social media posts from 53 news outlets during eight months on four social media platforms (Facebook, Instagram, Twitter, and YouTube), along with the associated comments (more than 31 million) and the number of likes (more than 840 million). We develop a framework for predicting the audience engagement based on both linguistic features of the post and social media platform factors. Among other findings, results show that content with high engagement on one platform does not guarantee high engagement on another platform, even when news outlets use similar cross-platform posts; however, for some content, cross-sharing posts on a platform will increase overall audience engagement on another platform. As one of the few multiple social media platform studies, the findings have implications for the news domain, as well as other fields that distribute online content via social media.

Keywords

audience engagement, news outlets, social media

Discipline

Artificial Intelligence and Robotics | Numerical Analysis and Scientific Computing | Social Media

Research Areas

Data Science and Engineering

Publication

SociInfo 2019: Proceedings of 11th International Conference on Social Informatics, Doha, Qatar, November 18-21

Volume

11864

First Page

173

Last Page

187

ISBN

9783030349714

Identifier

10.1007/978-3-030-34971-4_12

Publisher

Springer

City or Country

Cham

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1007/978-3-030-34971-4_12

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