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Author Nada Matta; Thomas Godard; Guillaume Delatour; Ludovic Blay; Franck Pouzet; Audrey Senator
Title Analyzing Social Media in Crisis Management Using Expertise Feedback Modelling Type Conference Article
Year (down) 2021 Publication ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management Abbreviated Journal Iscram 2021
Volume Issue Pages 17-27
Keywords Social Media analysis, TextMining, sentiment analysis, crisis management, decision making
Abstract Currently social media are largely used in interactions, especially in crisis situations. We note a big volume of interactions around events. Observing these interactions give information even to alert the existence of an incident, event, or to understand the expansion of a problem. Crisis management actors observe social media to be aware about this type of information in order to consider them in their decisions. Specific organizations are founded in order to observe social media interactions and send their analysis to rescue and crisis management actors. In our work, an experience feedback of this type of organizations (VISOV, a crisis social media analysis association) is capitalized in order to emphasize from one side, main dimensions of this analysis and from another side, to simulate some aspects using TextMining that help to explore big volume of data.
Address University of Technology of Troyes; University of Technology of Troyes; University of Technology of Troyes; VISOV; CS Group; ENSOSP
Corporate Author Thesis
Publisher Virginia Tech Place of Publication Blacksburg, VA (USA) Editor Anouck Adrot; Rob Grace; Kathleen Moore; Christopher W. Zobel
Language English Summary Language English Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 978-1-949373-61-5 ISBN Medium
Track AI and Intelligent Systems for Crises and Risks Expedition Conference 18th International Conference on Information Systems for Crisis Response and Management
Notes nada.matta@utt.fr Approved no
Call Number ISCRAM @ idladmin @ Serial 2309
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Author Sooji Han; Fabio Ciravegna
Title Rumour Detection on Social Media for Crisis Management Type Conference Article
Year (down) 2019 Publication Proceedings of the 16th International Conference on Information Systems for Crisis Response And Management Abbreviated Journal Iscram 2019
Volume Issue Pages
Keywords Rumours, large-scale data, event summarisation, sub-event detection, social media analysis
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.
Address The University of Sheffield, United Kingdom
Corporate Author Thesis
Publisher Iscram Place of Publication Valencia, Spain Editor Franco, Z.; González, J.J.; Canós, J.H.
Language English Summary Language English Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 2411-3387 ISBN 978-84-09-10498-7 Medium
Track T8- Social Media in Crises and Conflicts Expedition Conference 16th International Conference on Information Systems for Crisis Response and Management (ISCRAM 2019)
Notes Approved no
Call Number Serial 1860
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Author Stephen Kelly; Xiubo Zhang; Khurshid Ahmad
Title Mining Multimodal Information on Social Media for Increased Situational Awareness Type Conference Article
Year (down) 2017 Publication Proceedings of the 14th International Conference on Information Systems for Crisis Response And Management Abbreviated Journal Iscram 2017
Volume Issue Pages 613-622
Keywords Spatio-temporal; Social media analysis; Multimodal analysis; Geolocation
Abstract Social media platforms have become a source of high volume, real-time information describing significant events in a timely fashion. In this paper we describe a system for the real-time extraction of information from text and image content in Twitter messages and combine the spatio-temporal metadata of the messages to filter the data stream for emergency events and visualize the output on an interactive map. Twitter messages for a geographic region are monitored for flooding events by analysing the text content and images posted. Events detected are compared with a ground truth to see if information in social media correlates with actual events. We propose an Intrusion Index as part of this prototype to facilitate ethical harvesting of data. A map layer is created by the prototype system that visualises the analysis and filtered Twitter messages by geolocation.
Address rinity College Dublin, Ireland
Corporate Author Thesis
Publisher Iscram Place of Publication Albi, France Editor Tina Comes, F.B., Chihab Hanachi, Matthieu Lauras, Aurélie Montarnal, eds
Language English Summary Language English Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 2411-3387 ISBN Medium
Track Social Media Studies Expedition Conference 14th International Conference on Information Systems for Crisis Response And Management
Notes Approved no
Call Number Serial 2049
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Author Andrés Moreno; Philip Garrison; Karthik Bhat
Title WhatsApp for Monitoring and Response during Critical Events: Aggie in the Ghana 2016 Election Type Conference Article
Year (down) 2017 Publication Proceedings of the 14th International Conference on Information Systems for Crisis Response And Management Abbreviated Journal Iscram 2017
Volume Issue Pages 645-655
Keywords social media analysis; election monitoring; crisis prevention; WhatsApp; Ghana; mobile instant messaging
Abstract Mobile Instant Messaging platforms like WhatsApp are becoming increasingly popular. They have expanded access to digital text, audio, picture, and video messaging. Integrating them into existing crisis monitoring and response platforms and workflows can help reach a wider population. This paper describes a first attempt to integrate WhatsApp into Aggie, a social media aggregating and monitoring platform. We report on the deployment of this integration during Ghana's 2016 election, along with Twitter, Facebook, and RSS. The WhatsApp messages collected by Aggie during the election improved the eectiveness of the monitoring eorts. Thanks to these messages, more incidents were found and escalated to the Electoral Commission and security forces. From interviews with people involved in monitoring and response, we found that the WhatsApp integration helped their coordination and monitoring activities.
Address United Nations University Institute for Computing and Society
Corporate Author Thesis
Publisher Iscram Place of Publication Albi, France Editor Tina Comes, F.B., Chihab Hanachi, Matthieu Lauras, Aurélie Montarnal, eds
Language English Summary Language English Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 2411-3387 ISBN Medium
Track Social Media Studies Expedition Conference 14th International Conference on Information Systems for Crisis Response And Management
Notes Approved no
Call Number Serial 2052
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Author Daniel Link; Bernd Hellingrath; Jie Ling
Title A Human-is-the-Loop Approach for Semi-Automated Content Moderation Type Conference Article
Year (down) 2016 Publication ISCRAM 2016 Conference Proceedings ? 13th International Conference on Information Systems for Crisis Response and Management Abbreviated Journal ISCRAM 2016
Volume Issue Pages
Keywords Disaster Management; Social Media Analysis; Human-Is-The-Loop; Content Moderation; Supervised Machine Learning
Abstract Online social media has been recognized as a valuable information source for disaster management whose volume, velocity and variety exceed manual processing capacity. Current machine learning systems that support the processing of such data generally follow a human-in-the-loop approach, which has several inherent limitations. This work applies the human-is-the-loop concept from visual analytics to semi-automate a manual content moderation workflow, wherein human moderators take the dominant role. The workflow is instantiated with a supervised machine learning system that supports moderators with suggestions regarding the relevance and categorization of content. The instantiated workflow has been evaluated using in-depth interviews with practitioners and serious games. which suggest that it offers good compatibility with work practices in humanitarian assessment as well as improved moderation quality and higher flexibility than common approaches.
Address
Corporate Author Thesis
Publisher Federal University of Rio de Janeiro Place of Publication Rio de Janeiro, Brasil Editor A. Tapia; P. Antunes; V.A. Bañuls; K. Moore; J. Porto
Language English Summary Language English Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 2411-3388 ISBN 978-84-608-7984-9 Medium
Track Social Media Studies Expedition Conference 13th International Conference on Information Systems for Crisis Response and Management
Notes Approved no
Call Number Serial 1401
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Author Yongzhong Sha; Jinsong Yan; Guoray Cai
Title Detecting public sentiment over PM2.5 pollution hazards through analysis of Chinese microblog Type Conference Article
Year (down) 2014 Publication ISCRAM 2014 Conference Proceedings – 11th International Conference on Information Systems for Crisis Response and Management Abbreviated Journal ISCRAM 2014
Volume Issue Pages 722-726
Keywords Air pollution; Information systems; Time series analysis; Crisis; Pm2.5; Public opinions; Sentiment analysis; Social media analysis; Social aspects
Abstract Decision-making in crisis management can benefit from routine monitoring of the (social) media to discover the mass opinion on highly sensitive crisis events. We present an experiment that analyzes Chinese microblog data (extracted from Weibo.cn) to measure sentiment strength and its change in relation to the recent PM 2.5 air pollution events. The data were analyzed using SentiStrength algorithm together with a special sentiment words dictionary tailored and refined for Chinese language. The results of time series analysis on detected sentiment strength showed that less than one percent of the posts are strong-positive or strong negative. Weekly sentiment strength measures show symmetric changes in positive and negative strength, but overall trend moved towards more positive opinions. Special attention was given to sharp bursts of sentiment strength that coincide temporally with the occurrence of extreme social events. These findings suggest that sentiment strength analysis may generate useful alert and awareness of pending extreme social events.
Address Lanzhou University, Gansu, China; Penn State University, University Park, PA, United States
Corporate Author Thesis
Publisher The Pennsylvania State University Place of Publication University Park, PA Editor S.R. Hiltz, M.S. Pfaff, L. Plotnick, and P.C. Shih.
Language English Summary Language English Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 2411-3387 ISBN 9780692211946 Medium
Track Social Media in Crisis Response and Management Expedition Conference 11th International ISCRAM Conference on Information Systems for Crisis Response and Management
Notes Approved no
Call Number Serial 939
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