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Classification of Twitter Follow Links Based on the Followers' Intention

by Hikaru Takemura, Atsushi Tanaka, Keishi Tajima

Abstract

In Twitter, a user follows other users for various purposes, e.g., for information gathering, for personal communication, and for reading a chat by celebrities. As a result, the intention of followers behind follow links is different from case to case. We classify follow links in Twitter based on the followers' intention along with three classification axes: user-orientation, content-orientation, and mutuality. The combination of these three axes covers most major types of followers' intention found in Twitter. We collected 1760 Twitter follow links through a questionnaire and we found that (1) user-orientation and content-orientation have weak positive correlation, and user-orientation also has weak positive correlation with mutuality, but content-orientation has no correlation with mutuality, (2) content-oriented follows are more frequent than user-oriented follows even among communication-oriented users, and (3) the users have no clear intention for more than 20% of their follow links. We then constructed classifiers with various features of the followee, the follower, and their relationship. We also developed a method of classifying “lists” in Twitter into information lists and community lists, and used the types of lists including the followee. Our experiments show that (1) no single property is a prominent discriminator, and (2) classification accuracy for follow links by information-gathering users are higher than that for communication-oriented users.

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BibTex entry

Keywords

micro-blogging, social network, link classification, Twitter lists
Published in Proc. of ACM SAC, pp.1174-1180, Salamanca, Spain, 2015


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