Impact of information timeliness and richness on public engagement on social media during COVID-19 pandemic: An empirical investigation based on NLP and machine learning

作者：

Highlights：

• Impact of information timeliness and richness on public engagement on social media.

• Dataset from China's largest social media platform during the COVID-19 pandemic.

• An empirical investigation based on natural language processing (NLP) and machine learning.

• Information retrospectiveness had a negative effect on public engagement breadth but a positive effect on depth.

• Information immediateness and prospectiveness improved the breadth and depth of public engagement.

摘要

•Impact of information timeliness and richness on public engagement on social media.•Dataset from China's largest social media platform during the COVID-19 pandemic.•An empirical investigation based on natural language processing (NLP) and machine learning.•Information retrospectiveness had a negative effect on public engagement breadth but a positive effect on depth.•Information immediateness and prospectiveness improved the breadth and depth of public engagement.

论文关键词：Natural language processing (NLP) for societal benefit,Information timeliness,Information richness,Public engagement,Social media,Health emergencies

论文评审过程：Received 30 June 2021, Revised 15 November 2021, Accepted 4 February 2022, Available online 12 February 2022, Version of Record 20 September 2022.

论文官网地址：https://doi.org/10.1016/j.dss.2022.113752