Influence-based Twitter browsing with NavigTweet

作者：

Highlights：

• We propose novel visual framework to analyse, explore and interact with Twitter ‘Who Follows Who’ relationship.

• We developed NavigTweet to identify key influencers in Twitter network based upon the actual influence of shared content.

• A power-law based modified force-directed technique is provided to draw multi-clustered, and multi-layered graphs.

• A qualitative user-study is reported to gather feedback via survey.

• Reported pre-launch pilot test execution found to be positive along with post-release user-feedback survey.

摘要

Highlights•We propose novel visual framework to analyse, explore and interact with Twitter ‘Who Follows Who’ relationship.•We developed NavigTweet to identify key influencers in Twitter network based upon the actual influence of shared content.•A power-law based modified force-directed technique is provided to draw multi-clustered, and multi-layered graphs.•A qualitative user-study is reported to gather feedback via survey.•Reported pre-launch pilot test execution found to be positive along with post-release user-feedback survey.

论文关键词：Social Media Influencers,Social Media Influence,Twitter analytics,Graph visualization,Power-law graphs

论文评审过程：Received 5 July 2016, Accepted 13 July 2016, Available online 8 October 2016, Version of Record 10 November 2016.

论文官网地址：https://doi.org/10.1016/j.is.2016.07.012