Classifying Twitter favorites: Like, bookmark, or Thanks?. (22nd December 2014)

• Record Type: Journal Article
• Authors: Gorrell, Genevieve; Bontcheva, Kalina
• Is Part Of: Journal of the Association for Information Science and Technology. Volume 67:Number 1(2016:Jan.)
• Page Start: 17
• Page End: 25
• Publication Date: 2014-12-22
• Subjects: information overload -- machine learning -- Internet; Information science -- Periodicals; Information technology -- Periodicals; 020.5
• DOI: 10.1002/asi.23352
• Languages: English
• ISSNs: 2330-1635

Abstract

Abstract : 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.