Predicting Reposting Latency of News Content in Social Media : A Focus on Issue Attention, Temporal Usage Pattern, and Information Redundancy

Research output: Conference Papers (RGC: 31A, 31B, 32, 33)33_Other conference paperNot applicablepeer-review

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Detail(s)

Original languageEnglish
Publication statusPresented - 25 May 2019

Conference

Title69th Annual International Communication Association Conference (ICA19)
LocationWashington Hilton Hotel
PlaceUnited States
CityWashington
Period24 - 28 May 2019

Abstract

Social media platforms are becoming the most popular spaces for users to share, discuss, and further contribute to news dissemination. While an increasing number of studies have investigated the factors that influence user’s news content sharing behavior, few have paid attention to the reposting latency of news content online. Since timeliness is an essential factor for news value and news spreading, late reposting of news content may hinder its diffusion speed in the local network structure and further determine whether the news story will arouse a societal-wide burst of public attention. Reposting activity on social media is also an important type of user feedback behavior to the message received. The speed of the response could also reflect the user’s processing efficiency and capacity. This study aims to examine the possible factors that may influence users’ reposting latency of news content on social media. In doing so, we employed a multilevel regression analysis to examine the impacts of issue attention, temporal fragmentation, and information redundancy. Our findings show an inverted U-shape pattern for the relationship between the audience’s issue attention and reposting speed. We also found that a distributed temporal usage pattern can help shorten reposting time, while information redundancy and information overload increase the reposting latency of news on social media. The findings of this study can contribute to the advanced understanding of news consumption behavior on social media. The conclusions have the potential to help explain and further predict the success of news diffusion.

Citation Format(s)

Predicting Reposting Latency of News Content in Social Media : A Focus on Issue Attention, Temporal Usage Pattern, and Information Redundancy. / Guan, Lu; Liang, Hai; Zhu, Jonathan Jian Hua.

2019. 69th Annual International Communication Association Conference (ICA19), Washington, United States.

Research output: Conference Papers (RGC: 31A, 31B, 32, 33)33_Other conference paperNot applicablepeer-review