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  5. Topy: Real-time Story Tracking via Social Tags
 
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Topy: Real-time Story Tracking via Social Tags

Author(s)
Poghosyan, Gevorg  
Qureshi, M. Atif  
Ifrim, Georgiana  
Uri
http://hdl.handle.net/10197/7832
Date Issued
2016-09-23
Date Available
2016-08-23T14:16:03Z
Abstract
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).
Sponsorship
Science Foundation Ireland
Type of Material
Conference Publication
Publisher
Springer
Start Page
45
End Page
49
Series
Lecture Notes in Computer Science book series (LNCS, volume 9853)
Copyright (Published Version)
2016 Springer
Subjects

Machine learning

Statistics

Story tracking

News

Social media

Social tags

DOI
10.1007/978-3-319-46131-1_10
Web versions
http://ecmlpkdd2016.org/
Language
English
Status of Item
Peer reviewed
Journal
Berendt, B., Bringmann, B., Fromont, E., Garriga, G., Miettinen, P., Tatti, N. and Tresp, V. (eds.). Proceedings Part 3: Machine Learning and Knowledge Discovery in Databases
ECML PKDD 2016: Machine Learning and Knowledge Discovery in Databases
Conference Details
The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery (ECML-PKDD), Riva del Garda, Italy, 19-23 September 2016
ISBN
9783319461304
This item is made available under a Creative Commons License
https://creativecommons.org/licenses/by-nc-nd/3.0/ie/
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insight_publication.pdf

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592.14 KB

Format

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Checksum (MD5)

a8b396b38ff9848975528bdb69f17445

Owning collection
Insight Research Collection

Item descriptive metadata is released under a CC-0 (public domain) license: https://creativecommons.org/public-domain/cc0/.
All other content is subject to copyright.

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