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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 | 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 | Yongzhong Sha; Jinsong Yan; Guoray Cai | ||||
Title | Detecting public sentiment over PM2.5 pollution hazards through analysis of Chinese microblog | Type | Conference Article | ||
Year | 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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Author | Daniel Link; Bernd Hellingrath; Jie Ling | ||||
Title | A Human-is-the-Loop Approach for Semi-Automated Content Moderation | Type | Conference Article | ||
Year | 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. | ||||
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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 | Stephen Kelly; Xiubo Zhang; Khurshid Ahmad | ||||
Title | Mining Multimodal Information on Social Media for Increased Situational Awareness | Type | Conference Article | ||
Year | 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 | 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 | Sooji Han; Fabio Ciravegna | ||||
Title | Rumour Detection on Social Media for Crisis Management | Type | Conference Article | ||
Year | 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. |
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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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