Finding and analyzing principal features for measuring user influence on Twitter

Yan Mei, Youliang Zhong, Jian Yang

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contribution

18 Citations (Scopus)

Abstract

The study of social influence in online social networks has attracted great interests in recent years for its applications in information propagation and marketing. While many existing studies focus on the measurement of social influence on various platforms, there is a lack of comprehensive analysis regarding the effectiveness of the principal features for measuring user influence. In this paper, we employ Entropy method and Rank Correlation Analysis to identify the key manifest features for measuring user influence. We also utilize Principal Component Analysis and Stepwise Multiple Linear Regression to analyze the most important hidden social attributes for identifying influential users on Twitter. Our study reveals a number of novel findings as follows: (i) Firstly, besides mention and rewet actions that have been widely used to measure user influence in literature, we find that number of public lists, new tweets, follower to friends ratio are also fairly effective indicators for user influence, (ii) We further discover that popularity, engagement and authority are the three most important social attributes to drive user influence in Twitter environment, (iii) Finally, we compare four popular influence scoring services, and find that new mentions and number of public lists are the two most effective manifest features for their influence ranking, and popularity is commonly considered as the first key social attribute of the influencers on Twitter.

Original languageEnglish
Title of host publicationProceedings - 2015 IEEE 1st International Conference on Big Data Computing Service and Applications, BigDataService 2015
Place of PublicationPiscataway, NJ
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages478-486
Number of pages9
ISBN (Electronic)9781479981281
ISBN (Print)9781479981298
DOIs
Publication statusPublished - 10 Aug 2015
Event1st IEEE International Conference on Big Data Computing Service and Applications, BigDataService 2015 - San Francisco, United States
Duration: 30 Mar 20153 Apr 2015

Other

Other1st IEEE International Conference on Big Data Computing Service and Applications, BigDataService 2015
CountryUnited States
CitySan Francisco
Period30/03/153/04/15

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