Now showing 1 - 2 of 2
  • 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
  • Publication
    Event Detection in Twitter using Aggressive Filtering and Hierarchical Tweet Clustering
    Twitter has become as much of a news media as a social network, and much research has turned to analysing its content for tracking real-world events, from politics to sports and natural disasters. This paper describes the techniques we employed for the SNOW Data Challenge 2014, described in [16]. We show that aggressive lettering of tweets based on length and structure, combined with hierarchical clustering of tweets and ranking of the resulting clusters, achieves encouraging results. We present empirical results and discussion for two different Twitter streams focusing on the US presidential elections in 2012 and the recent events about Ukraine, Syria and the Bitcoin, in February 2014.
      1603