Real time News Story Detection and Tracking with Hashtags
|Title:||Real time News Story Detection and Tracking with Hashtags||Authors:||Poghosyan, Gevorg
|Permanent link:||http://hdl.handle.net/10197/8131||Date:||6-Nov-2016||Online since:||2016-11-18T15:03:43Z||Abstract:||Topic Detection and Tracking (TDT) is an important research topic in data mining and information retrieval and has been explored for many years. Most of the studies have approached the problem from the event tracking point of view. We argue that the definition of stories as events is not reflecting the full picture. In this work we propose a story tracking method built on crowd-tagging in social media, where news articles are labeled with hashtags in real-time. The social tags act as rich metadata for news articles, with the advantage that, if carefully employed, they can capture emerging concepts and address concept drift in a story. We present an approach for employing social tags for the purpose of story detection and tracking and show initial empirical results. We compare our method to classic keyword query retrieval and discuss an example of story tracking over time.||Funding Details:||Science Foundation Ireland||Type of material:||Conference Publication||Publisher:||ACL||Copyright (published version):||2016 Association for Computational Linguistics||Keywords:||Machine learning; Statistics||Other versions:||http://www.emnlp2016.net/||Language:||en||Status of Item:||Peer reviewed||Is part of:||Proceedings of the 2nd Workshop on Computing News Storylines (CNS 2016)||Conference Details:||Computing News Storylines Workshop at EMNLP 2016, Austin, Texas, United States of America, 2-6 November 2016|
|Appears in Collections:||Insight Research Collection|
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