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Rumour Detection on Social Media for Crisis Management
Sooji Han
author
Fabio Ciravegna
author
2019
Iscram
Valencia, Spain
English
We address the problem of making sense of rumour evolution during crises and emergencies. We study how
understanding and capturing emerging rumours can benefit decision makers during such event. To this end, we
propose a two-step framework for detecting rumours during crises. In the first step, we introduce an algorithm to
identify noteworthy sub-events in real time. In the second step, we introduce a graph-based text ranking method
for summarising newsworthy sub-events while events unfold. We use temporal and content-based features to
achieve the effective and real-time response and management of crises situations. These features can improve
efficiency in the detection of key rumours in the context of a real-world application. The effectiveness of our
method is evaluated over large-scale Twitter data related to real-world crises. The results show that our framework
can efficiently and effectively capture key rumours circulated during natural and human-made disasters.
Rumours
large-scale data
event summarisation
sub-event detection
social media analysis
exported from refbase (http://idl.iscram.org/show.php?record=1860), last updated on Fri, 22 Nov 2019 11:47:11 +0100
text
http://idl.iscram.org/files/soojihan/2019/1860_SoojiHan+FabioCiravegna2019.pdf
SoojiHan+FabioCiravegna2019
Proceedings of the 16th International Conference on Information Systems for Crisis Response And Management
Iscram 2019
Franco
Z
editor
González
J
J
editor
Canós
J
H
editor
16th International Conference on Information Systems for Crisis Response and Management (ISCRAM 2019)
2019
Iscram
Valencia, Spain
conference publication
978-84-09-10498-7
2411-3387
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