Co-following on twitter

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

6 Scopus citations

Abstract

We present an in-depth study of co-following on Twitter based on the observation that two Twitter users whose followers have similar friends are also similar, even though they might not share any direct links or a single mutual follower. We show how this observation contributes to (i) a better understanding of language-agnostic user classification on Twitter, (ii) eliciting opportunities for Computational Social Science, and (iii) improving online marketing by identifying cross-selling opportunities. We start with a machine learning problem of predicting a user's preference among two alternative choices of Twitter friends. We show that co-following information provides strong signals for diverse classification tasks and that these signals persist even when the most discriminative features are removed. Going beyond mere classification performance optimization, we present applications of our methodology to Computational Social Science. Here we confirm stereotypes such as that the country singer Kenny Chesney (@kennychesney) is more popular among @GOP followers, whereas Lady Gaga (@ladygaga) enjoys more support from @TheDemocrats followers. In the domain of marketing we give evidence that celebrity endorsement is reflected in co-following and we demonstrate how our methodology can be used to reveal the audience similarities between not so obvious entites such as Apple and Puma.

Original languageEnglish (US)
Title of host publicationHT 2014 - Proceedings of the 25th ACM Conference on Hypertext and Social Media
PublisherAssociation for Computing Machinery
Pages249-254
Number of pages6
ISBN (Print)9781450329545
DOIs
StatePublished - 2014
Externally publishedYes
Event25th ACM Conference on Hypertext and Social Media, HT 2014 - Santiago, Chile
Duration: Sep 1 2014Sep 4 2014

Publication series

NameHT 2014 - Proceedings of the 25th ACM Conference on Hypertext and Social Media

Conference

Conference25th ACM Conference on Hypertext and Social Media, HT 2014
Country/TerritoryChile
CitySantiago
Period9/1/149/4/14

All Science Journal Classification (ASJC) codes

  • Computer Graphics and Computer-Aided Design
  • Human-Computer Interaction
  • Software

Keywords

  • co-following
  • twitter
  • user classification
  • user similarity

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