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Formalization and Visualization of the Narrative for Museum Guides Read chapter Read first chapter

2018 | OriginalPaper | Chapter

FIRE: Finding Important News REports

Authors : Nicholas Mamo, Joel Azzopardi

Published in: Semantic Keyword-Based Search on Structured Data Sources

Publisher: Springer International Publishing

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Abstract

Every day, an immeasurable number of news items are published. Social media greatly contributes to the dissemination of information, making it difficult to stay on top of what is happening. Twitter stands out among popular social networks due to its large user base and the immediateness with which news is spread.
In this paper, we present a solution named Finding Important News REports (FIRE) that exploits the information available on Twitter to identify and track breaking news, and the defining articles that discuss them. The methods used in FIRE present context-specific problems when dealing with the micro-messages of Twitter, and thus they are the subject of research.
FIRE demonstrates how Twitter’s conversation habits do nothing to shackle the detection of important news. To the contrary, the developed system is able to extract newsworthy stories that are important to the general population, and do so before Twitter itself. Moreover, the results emphasize the need for reliable and efficient spam and noise filtering tools.

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Footnotes
3
https://​github.​com/​fennb/​phirehose/​. Last accessed on April 9, 2017.
 
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Metadata
Title
FIRE: Finding Important News REports
Authors
Nicholas Mamo
Joel Azzopardi
Copyright Year
2018
Book
Semantic Keyword-Based Search on Structured Data Sources

Print ISBN: 978-3-319-74496-4

Electronic ISBN: 978-3-319-74497-1

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-319-74497-1_3