Gorrell, G.M. and Bontcheva, K.L. (2014) Classifying Twitter Favorites: Like, Bookmark, or Thanks? Journal of the Association for Information Science and Technology. ISSN 2330-1635
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 selfpromotion). 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.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2014. Genevieve Gorrell and Kalina Bontcheva . This is an Open Access article distributed under the terms of the Creative Commons Attribution Licence (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
Keywords: | information overload ;machine learning; Internet |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 03 Jul 2015 11:48 |
Last Modified: | 03 Jul 2015 11:48 |
Published Version: | http://dx.doi.org/10.1002/asi.23352 |
Status: | Published |
Publisher: | Association for Information Science and Technology (ASIS&T): JASIS&T |
Refereed: | Yes |
Identification Number: | 10.1002/asi.23352 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:87448 |