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  • Publication
    Topy: Real-time Story Tracking via Social Tags
    The Topy system automates real-time story tracking by utilizing crowd- sourced tagging on social media platforms. Topy employs a state-of-the-art Twitter hashtag recommender to continuously annotate news articles with hashtags, a rich meta-data source that allows connecting articles under drastically different timelines than typical keyword based story tracking systems. Employing social tags for story tracking has the following advantages: (1) social annotation of news enables the detection of emerging concepts and topic drift in a story; (2) hashtags go beyond topics by grouping articles based on connected themes (e.g., #rip, #blacklivesmatter, #icantbreath); (3) hashtags link articles that focus on subplots of the same story (e.g., #palmyra, #isis, #refugeecrisis).
      629Scopus© Citations 3
  • Publication
    Hashtagger+: Efficient High-Coverage Social Tagging of Streaming News
    News and social media now play a synergistic role and neither domain can be grasped in isolation. On one hand, platformssuch as Twitter have taken a central role in the dissemination and consumption of news. On the other hand, news editors rely on socialmedia for following their audiences attention and for crowd-sourcing news stories. Twitter hashtags function as a key connectionbetween Twitter crowds and the news media, by naturally naming and contextualizing stories, grouping the discussion of news andmarking topic trends. In this work we propose Hashtagger+, an efficient learning-to-rank framework for merging news and socialstreams in real-time, by recommending Twitter hashtags to news articles. We provide an extensive study of different approaches forstreaming hashtag recommendation, and show that pointwise learning-to-rank is more effective than multi-class classification as wellas more complex learning-to-rank approaches. We improve the efficiency and coverage of a state-of-the-art hashtag recommendationmodel by proposing new techniques for data collection and feature computation. In our comprehensive evaluation on real-data weshow that we drastically outperform the accuracy and efficiency of prior methods. Our prototype system delivers recommendations inunder 1 minute, with a Precision@1 of 94% and article coverage of 80%. This is an order of magnitude faster than prior approaches,and brings improvements of 5% in precision and 20% in coverage. By effectively linking the news stream to the social stream via therecommended hashtags, we open the door to solving many challenging problems related to story detection and tracking. To showcasethis potential, we present an application of our recommendations to automated news story tracking via social tags. Ourrecommendation framework is implemented in a real-time Web system available from insight4news.ucd.ie.
      907Scopus© Citations 23