Tweet the debates: understanding community annotation of uncollected sources
WSM '09 Proceedings of the first SIGMM workshop on Social media
TwitterRank: finding topic-sensitive influential twitterers
Proceedings of the third ACM international conference on Web search and data mining
What is Twitter, a social network or a news media?
Proceedings of the 19th international conference on World wide web
Recommending twitter users to follow using content and collaborative filtering approaches
Proceedings of the fourth ACM conference on Recommender systems
Merging multiple criteria to identify suspicious reviews
Proceedings of the fourth ACM conference on Recommender systems
The network effects of recommending social connections
Proceedings of the fourth ACM conference on Recommender systems
Producing a unified graph representation from multiple social network views
Proceedings of the 5th Annual ACM Web Science Conference
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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.