Predicting audience engagement across social media platforms in the news domain
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 me...
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sg-smu-ink.sis_research-72972023-08-03T05:23:33Z Predicting audience engagement across social media platforms in the news domain ALDOUS, Kholoud Khalil AN, Jisun JANSEN, Bernard J. 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. 2019-11-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/6294 info:doi/10.1007/978-3-030-34971-4_12 https://ink.library.smu.edu.sg/context/sis_research/article/7297/viewcontent/Predicting_Audience_Engagement_Across_Social_Media_Platforms_in_the_News_Domain.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University audience engagement news outlets social media Artificial Intelligence and Robotics Numerical Analysis and Scientific Computing Social Media |
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audience engagement news outlets social media Artificial Intelligence and Robotics Numerical Analysis and Scientific Computing Social Media ALDOUS, Kholoud Khalil AN, Jisun JANSEN, Bernard J. Predicting audience engagement across social media platforms in the news domain |
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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. |
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text |
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ALDOUS, Kholoud Khalil AN, Jisun JANSEN, Bernard J. |
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ALDOUS, Kholoud Khalil AN, Jisun JANSEN, Bernard J. |
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ALDOUS, Kholoud Khalil |
title |
Predicting audience engagement across social media platforms in the news domain |
title_short |
Predicting audience engagement across social media platforms in the news domain |
title_full |
Predicting audience engagement across social media platforms in the news domain |
title_fullStr |
Predicting audience engagement across social media platforms in the news domain |
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Predicting audience engagement across social media platforms in the news domain |
title_sort |
predicting audience engagement across social media platforms in the news domain |
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Institutional Knowledge at Singapore Management University |
publishDate |
2019 |
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https://ink.library.smu.edu.sg/sis_research/6294 https://ink.library.smu.edu.sg/context/sis_research/article/7297/viewcontent/Predicting_Audience_Engagement_Across_Social_Media_Platforms_in_the_News_Domain.pdf |
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