Now showing 1 - 2 of 2
  • Publication
    Real time event monitoring with trident
    Building a scalable, fault-tolerant stream mining system that deals with realistic data volumes presents unique challenges. Considerable work is being done to make the development of such systems simpler, creating high level abstractions on top of existing systems. Many of the technical barriers can be eliminated by adopting a state-of-the-art interface, such as the Trident API for Storm. We describe a stream mining tool, based on Trident, for monitoring breaking news events on Twitter, which can be extended quickly and scaled easily.
  • Publication
    Aggregating Content and Network Information to Curate Twitter User Lists
    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.
    Scopus© Citations 12  586