Classifying Twitter favorites: Like, bookmark, or Thanks?

Genevieve Gorrell and Kalina Bontcheva

Journal of the Association for Information Science & Technology, 2016, vol. 67, issue 1, 17-25

Abstract: type="main">

Since its foundation in 2006, Twitter has enjoyed a meteoric rise in popularity, currently boasting over 500 million users. Its short text nature means that the service is open to a variety of different usage patterns, which have evolved rapidly in terms of user base and utilization. Prior work has categorized Twitter users, as well as studied the use of lists and re-tweets and how these can be used to infer user profiles and interests. The focus of this article is on studying why and how Twitter users mark tweets as “favorites”—a functionality with currently poorly understood usage, but strong relevance for personalization and information access applications. Firstly, manual analysis and classification are carried out on a randomly chosen set of favorited tweets, which reveal different approaches to using this functionality (i.e., bookmarks, thanks, like, conversational, and self-promotion). Secondly, an automatic favorites classification approach is proposed, based on the categories established in the previous step. Our machine learning experiments demonstrate a high degree of success in matching human judgments in classifying favorites according to usage type. In conclusion, we discuss the purposes to which these data could be put, in the context of identifying users' patterns of interests.

Date: 2016
References: Add references at CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://hdl.handle.net/10.1002/asi.23352 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:bla:jinfst:v:67:y:2016:i:1:p:17-25

Ordering information: This journal article can be ordered from
http://www.blackwell ... bs.asp?ref=2330-1635

Access Statistics for this article

More articles in Journal of the Association for Information Science & Technology from Association for Information Science & Technology
Bibliographic data for series maintained by Wiley Content Delivery ().