What really matters?: characterising and predicting user engagement of news postings using multiple platforms, sentiments and topics.

Autor:	Aldous, Kholoud Khalil1 kaldous@mail.hbku.edu.qa, An, Jisun2, Jansen, Bernard J.3
Předmět:	*Associations institutions etc. *Sentiment analysis *Publishing *Research *Social media *Metadata *Content analysis Patient participation Press Consumer attitudes Language & languages Descriptive statistics Emotions Prediction models Public opinion
Zdroj:	Behaviour & Information Technology. Apr2023, Vol. 42 Issue 5, p545-568. 24p. 1 Diagram, 8 Charts, 3 Graphs.
Abstrakt:	This research characterises user engagement of approximately 3,000,000 news postings of 53 news outlets and 50,000,000 associated user comments during 8 months on 5 social media platforms (i.e. Facebook, Instagram, Twitter, YouTube, and Reddit). We investigate the effect of sentiments and topics on user engagement across four levels of user engagement expressions (i.e. views, likes, comments, cross-platform posting). We find that sentiments and topics differ by both news outlets and social media platforms, and both sentiments and topics by the four levels of user engagement expression. Finally, we predict a volume of four user engagement levels for given news content, with an 83% maximum average F1-score for the external posting of news articles from one platform to another using language and metadata features. Implications are that news outlets can benefit by developing a platform, sentiment and topic, and strategies to best achieve user engagement objectives. [ABSTRACT FROM AUTHOR]
Databáze:	Library, Information Science & Technology Abstracts
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