The reflection of offline activities on users’ online social behavior: An observational study
作者：
Highlights：
• We show how content from two social networks can provide the means to study users'offline and online behavior in tandem in the context of observational studies.
• We introduce two metrics, namely social alignment and social convergence to model users’ online behavior.
• We study the causal impact of users’ offline activities on their online behavior as it relates to their topical interests and sentiments towards active topics.
• We train machine learning-based predictive models solely based on users’ offline activities in order to accurately predict the users’ online topical interests and sentiments.
摘要
•We show how content from two social networks can provide the means to study users'offline and online behavior in tandem in the context of observational studies.•We introduce two metrics, namely social alignment and social convergence to model users’ online behavior.•We study the causal impact of users’ offline activities on their online behavior as it relates to their topical interests and sentiments towards active topics.•We train machine learning-based predictive models solely based on users’ offline activities in order to accurately predict the users’ online topical interests and sentiments.
论文关键词：
论文评审过程：Received 15 November 2018, Revised 23 April 2019, Accepted 1 July 2019, Available online 15 July 2019, Version of Record 15 July 2019.
论文官网地址：https://doi.org/10.1016/j.ipm.2019.102070
