Topy: Real-time Story Tracking via Social Tags

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Title: Topy: Real-time Story Tracking via Social Tags
Authors: Poghosyan, GevorgQureshi, M. AtifIfrim, Georgiana
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Date: 23-Sep-2016
Online since: 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).
Funding Details: Science Foundation Ireland
Type of material: Conference Publication
Publisher: Springer
Start page: 45
End page: 49
Series/Report no.: Lecture Notes in Computer Science book series (LNCS, volume 9853)
Copyright (published version): 2016 Springer
Keywords: Machine learningStatisticsStory trackingNewsSocial mediaSocial tags
DOI: 10.1007/978-3-319-46131-1_10
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Language: en
Status of Item: Peer reviewed
Is part of: 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
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Appears in Collections:Insight Research Collection

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