Co-Following on Twitter
Venkata Rama Kiran Garimella, Ingmar Weber

TL;DR
This paper investigates co-following patterns on Twitter, demonstrating their usefulness for user classification, social science insights, and marketing strategies by analyzing followers' shared interests and preferences.
Contribution
It introduces a machine learning approach leveraging co-following data for diverse classification tasks and social science applications on Twitter.
Findings
Co-following signals are strong predictors for user preferences.
Co-following patterns reveal stereotypes and audience similarities.
Larger, less diverse audiences correlate with higher popularity.
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 (i) the most discriminative features are removed and (ii) only relatively "sparse" users with fewer than 152 but more than 43…
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Taxonomy
TopicsComplex Network Analysis Techniques · Sentiment Analysis and Opinion Mining · Authorship Attribution and Profiling
