Aggregating Content and Network Information to Curate Twitter User Lists

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Title: Aggregating Content and Network Information to Curate Twitter User Lists
Authors: Greene, Derek
Sheridan, Gavin
Smyth, Barry
Cunningham, Pádraig
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Date: 25-Jun-2012
Abstract: Twitter introduced user lists in late 2009, allowing users to be grouped according to meaningful topics or themes. Lists have since been adopted by media outlets as a means of organising content around news stories. Thus the curation of these lists is important - they should contain the key information gatekeepers and present a balanced perspective on a story. Here we address this list curation process from a recommender systems perspective. We propose a variety of criteria for generating user list recommendations, based on content analysis, network analysis, and the "crowdsourcing" of existing user lists. We demonstrate that these types of criteria are often only successful for datasets with certain characteristics. To resolve this issue, we propose the aggregation of these different "views" of a news story on Twitter to produce more accurate user recommendations to support the curation process.
Funding Details: Science Foundation Ireland
Type of material: Conference Publication
Publisher: ACM
Copyright (published version): 2012 ACM
Keywords: Social network analysisRecommender systemsSocial media analysisTwitter
Subject LCSH: Online social networks
Recommender systems (Information filtering)
Social media
DOI: 10.1145/2365934.2365941
Language: en
Status of Item: Peer reviewed
Is part of: RSWeb'12 : Proceedings of the 4th ACM RecSys workshop on Recommender systems and the social web
Conference Details: ACM RecSys 2012 Workshop on Recommender Systems & The Social Web, 9 September, 2012, Dublin
ISBN: 978-1-4503-1638-5
Appears in Collections:Clique Research Collection
CASL Research Collection

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