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Real-time News Story Detection and Tracking with Hashtags
| Content Provider | Semantic Scholar |
|---|---|
| Author | Poghosyan, Gevorg Ifrim, Georgiana |
| Copyright Year | 2016 |
| 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 meta-data 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. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://anthology.aclweb.org/W/W16/W16-5703.pdf |
| Alternate Webpage(s) | http://wing.comp.nus.edu.sg/~antho/W/W16/W16-5703.pdf |
| Alternate Webpage(s) | http://aclweb.org/anthology//W/W16/W16-5703.pdf |
| Alternate Webpage(s) | https://researchrepository.ucd.ie/bitstream/10197/8131/1/insight_publication.pdf |
| Alternate Webpage(s) | http://www.aclweb.org/anthology/W/W16/W16-5703.pdf |
| Alternate Webpage(s) | http://aclweb.org/anthology/W16-5703 |
| Alternate Webpage(s) | http://aclweb.org/anthology/W/W16/W16-5703.pdf |
| Language | English |
| Access Restriction | Open |
| Subject Keyword | Concept drift Data mining Hashtag Information retrieval Keyword Real-time locating system Real-time transcription Social media Time-domain reflectometry Tracer |
| Content Type | Text |
| Resource Type | Article |
