Han, S. and Ciravegna, F. (2019) Rumour detection on social media for crisis management. In: Franco, Z., González, J.J. and Canós, J.H., (eds.) Proceedings of the 16th International Conference on Information Systems for Crisis Response And Management. The 16th International Conference on Information Systems for Crisis Response and Management, 19-22 May 2019, Valencia, Spain. ISCRAM , pp. 660-673.
Abstract
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.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Editors: |
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Copyright, Publisher and Additional Information: | © 2019 ISCRAM. |
Keywords: | Rumours; large-scale data; event summarisation; sub-event detection; social media analysis |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield) |
Funding Information: | Funder Grant number EUROPEAN COMMISSION - HORIZON 2020 688082 |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 03 Jun 2019 13:44 |
Last Modified: | 03 Jun 2019 13:44 |
Published Version: | http://idl.iscram.org/files/soojihan/2019/1751_Soo... |
Status: | Published |
Publisher: | ISCRAM |
Refereed: | Yes |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:146587 |