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Author | Cruz, J.A. dela; Hendrickx, I.; Larson, M. | ||||
Title | Towards XAI for Information Extraction on Online Media Data for Disaster Risk Management | Type | Conference Article | ||
Year | 2023 | Publication | Proceedings of the 20th International ISCRAM Conference | Abbreviated Journal | Iscram 2023 |
Volume | Issue | Pages | 478-486 | ||
Keywords | Disaster Risk Management; Information Extraction; Explainable AI (XAI); Explainabilit | ||||
Abstract | Disaster risk management practitioners have the responsibility to make decisions at every phase of the disaster risk management cycle: mitigation, preparedness, response and recovery. The decisions they make affect human life. In this paper, we consider the current state of the use of AI in information extraction (IE) for disaster risk management (DRM), which makes it possible to leverage disaster information in social media. We consolidate the challenges and concerns of using AI for DRM into three main areas: limitations of DRM data, limitations of AI modeling and DRM domain-specific concerns, i.e., bias, privacy and security, transparency and accountability, and hype and inflated expectations. Then, we present a systematic discussion of how explainable AI (XAI) can address the challenges and concerns of using AI for IE in DRM. | ||||
Address | Centre for Language Studies, Centre for Language and Speech Technology, Radboud University; Centre for Language Studies, Centre for Language and Speech Technology, Radboud University; Centre for Language Studies,Inst. for Computing and Information Sciences,Radboud University | ||||
Corporate Author | Thesis | ||||
Publisher | University of Nebraska at Omaha | Place of Publication | Omaha, USA | Editor | Jaziar Radianti; Ioannis Dokas; Nicolas Lalone; Deepak Khazanchi |
Language | English | Summary Language | Original Title | ||
Series Editor | Hosssein Baharmand | Series Title | Abbreviated Series Title | ||
Series Volume | Series Issue | Edition | 1 | ||
ISSN | ISBN | Medium | |||
Track | Social Media for Crisis Management | Expedition | Conference | ||
Notes | http://dx.doi.org/10.59297/BHAE3912 | Approved | no | ||
Call Number | ISCRAM @ idladmin @ | Serial | 2541 | ||
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Author | Daniel E. Lane; Tracey L. O'Sullivan; Craig E. Kuziemsky; Fikret Berkes; Anthony Charles | ||||
Title | A structured equation model of collaborative community response | Type | Conference Article | ||
Year | 2013 | Publication | ISCRAM 2013 Conference Proceedings – 10th International Conference on Information Systems for Crisis Response and Management | Abbreviated Journal | ISCRAM 2013 |
Volume | Issue | Pages | 906-911 | ||
Keywords | Computer simulation; Decision theory; Information systems; Mathematical models; Risk analysis; Adaptation; C-change; Community collaboration; Community engagement; Emergency response; EnRiCH; Preparedness; Simulation; Structured equation modeling; Emergency services | ||||
Abstract | This paper analyses the collaborative dynamic of community in response to urgent situations. Community emergencies arising from natural or man-induced threats are considered as exogenous events that stimulate community resources to be unified around the response, action, and recovery activities related to the emergency. A structured equation model is derived to depict the actions of the community system. The system is described in terms of its resources including the propensity to trigger community action and collaboration among diverse groups. The community is profiled with respect to its ability to respond. The system defines the trigger mechanisms that are considered to be the drivers of collaborative action. A simulation model is presented to enact the system emergencies, community profiles, and collaborative response. The results develop an improved understanding of conditions that engage community collaborative actions as illustrated by examples from community research in the EnRiCH and the C-Change community research projects. | ||||
Address | Telfer School of Management, University of Ottawa, Canada; Interdisciplinary Faculty of Health Sciences, University of Ottawa, Canada; Natural Resources Institute, University of Manitoba, Canada; Department of Finance and Management Science, Saint Mary's University, Canada | ||||
Corporate Author | Thesis | ||||
Publisher | Karlsruher Institut fur Technologie | Place of Publication | KIT; Baden-Baden | Editor | T. Comes, F. Fiedrich, S. Fortier, J. Geldermann and T. Müller |
Language | English | Summary Language | English | Original Title | |
Series Editor | Series Title | Abbreviated Series Title | |||
Series Volume | Series Issue | Edition | |||
ISSN | 2411-3387 | ISBN | 9783923704804 | Medium | |
Track | Social Media | Expedition | Conference | 10th International ISCRAM Conference on Information Systems for Crisis Response and Management | |
Notes | Approved | no | |||
Call Number | Serial | 677 | |||
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Author | Daniel Iland; Don Voita; Elizabeth Belding | ||||
Title | Delay tolerant disaster communication with the One Laptop per Child XO laptop | Type | Conference Article | ||
Year | 2013 | Publication | ISCRAM 2013 Conference Proceedings – 10th International Conference on Information Systems for Crisis Response and Management | Abbreviated Journal | ISCRAM 2013 |
Volume | Issue | Pages | 863-867 | ||
Keywords | Disasters; Information systems; Internet; MESH networking; Delay Tolerant Networking; Disaster communications; Epidemic routing; Information sharing; Olpc; Peer to peer; Situational awareness; Telepathy salut; Ushahidi; Laptop computers | ||||
Abstract | In this paper, we describe the design, implementation, and evaluation of a mesh network based messaging application for the One Laptop Per Child XO laptop. We outline the creation of an easy-to-use OLPC Activity that exchanges Ushahidi-style messages with nearby OLPC users through the Internet or a mesh network. Our contributions are to implement an epidemic messaging scheme on mesh networks of OLPC XO laptops, to extend the Ushahidi web application to efficiently exchange messages with nodes in mesh networks, and to allow the Ushahidi server to distribute cures, notifications of message delivery, for each received message. Testing and analysis revealed substantial overhead is introduced by the OLPC's use of Telepathy Salut for activity sharing. | ||||
Address | University of California, Santa Barbara, United States | ||||
Corporate Author | Thesis | ||||
Publisher | Karlsruher Institut fur Technologie | Place of Publication | KIT; Baden-Baden | Editor | T. Comes, F. Fiedrich, S. Fortier, J. Geldermann and T. Müller |
Language | English | Summary Language | English | Original Title | |
Series Editor | Series Title | Abbreviated Series Title | |||
Series Volume | Series Issue | Edition | |||
ISSN | 2411-3387 | ISBN | 9783923704804 | Medium | |
Track | Social Media | Expedition | Conference | 10th International ISCRAM Conference on Information Systems for Crisis Response and Management | |
Notes | Approved | no | |||
Call Number | Serial | 611 | |||
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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 | Dario Salza; Edoardo Arnaudo; Giacomo Blanco; Claudio Rossi | ||||
Title | A 'Glocal' Approach for Real-time Emergency Event Detection in Twitter | Type | Conference Article | ||
Year | 2022 | Publication | ISCRAM 2022 Conference Proceedings – 19th International Conference on Information Systems for Crisis Response and Management | Abbreviated Journal | Iscram 2022 |
Volume | Issue | Pages | 570-583 | ||
Keywords | Emergency; Event Detection; Social Media; Twitter; Incremental Clustering | ||||
Abstract | Social media like Twitter offer not only an unprecedented amount of user-generated content covering developing emergencies but also act as a collector of news produced by heterogeneous sources, including big and small media companies as well as public authorities. However, this volume, velocity, and variety of data constitute the main value and, at the same time, the key challenge to implement and automatic detection and tracking of independent emergency events from the real-time stream of tweets. Leveraging online clustering and considering both textual and geographical features, we propose, implement, and evaluate an algorithm to automatically detect emergency events applying a ‘glocal’ approach, i.e., offering a global coverage while detecting events at local (municipality level) scale. | ||||
Address | LINKS Foundation; LINKS Foundation; LINKS Foundation; LINKS Foundation | ||||
Corporate Author | Thesis | ||||
Publisher | Place of Publication | Tarbes, France | Editor | Rob Grace; Hossein Baharmand | |
Language | English | Summary Language | Original Title | ||
Series Editor | Series Title | Abbreviated Series Title | |||
Series Volume | Series Issue | Edition | |||
ISSN | 2411-3387 | ISBN | 978-82-8427-099-9 | Medium | |
Track | Social Media for Crisis Management | Expedition | Conference | ||
Notes | Approved | no | |||
Call Number | ISCRAM @ idladmin @ | Serial | 2440 | ||
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Author | Dat T. Nguyen; Firoj Alam; Ferda Ofli; Muhammad Imran | ||||
Title | Automatic Image Filtering on Social Networks Using Deep Learning and Perceptual Hashing During Crises | 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 | 499-511 | ||
Keywords | social media; image processing; supervised classification; disaster management | ||||
Abstract | The extensive use of social media platforms, especially during disasters, creates unique opportunities for humanitarian organizations to gain situational awareness and launch relief operations accordingly. In addition to the textual content, people post overwhelming amounts of imagery data on social networks within minutes of a disaster hit. Studies point to the importance of this online imagery content for emergency response. Despite recent advances in the computer vision field, automatic processing of the crisis-related social media imagery data remains a challenging task. It is because a majority of which consists of redundant and irrelevant content. In this paper, we present an image processing pipeline that comprises de-duplication and relevancy filtering mechanisms to collect and filter social media image content in real-time during a crisis event. Results obtained from extensive experiments on real-world crisis datasets demonstrate the significance of the proposed pipeline for optimal utilization of both human and machine computing resources. | ||||
Address | Qatar Computing Research Institute Hamad Bin Khalifa University Doha, Qatar | ||||
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 | 2038 | |||
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Author | David F. Merrick; Tom Duffy | ||||
Title | Utilizing community volunteered information to enhance disaster situational awareness | Type | Conference Article | ||
Year | 2013 | Publication | ISCRAM 2013 Conference Proceedings – 10th International Conference on Information Systems for Crisis Response and Management | Abbreviated Journal | ISCRAM 2013 |
Volume | Issue | Pages | 858-862 | ||
Keywords | Civil defense; Disasters; Information systems; Risk management; Social networking (online); Community volunteered information; Crowd sourcing; Facebook; Situational awareness; Social media; Twitter; Emergency services | ||||
Abstract | Social media allows the public to engage in the disaster response and recovery process in new and exciting ways. Many emergency management agencies in the United States are embracing social media as a new channel for alerts, warnings, and public outreach, but very few are mining the massive amounts of data available for use in disaster response. The research reflected in this paper strives to help emergency management practitioners harness the power of community volunteered information in a way that is still novel in most parts of the country. Field verification and research combined with survey results attempts to identify and solve many of the barriers to adoption that currently exist. By helping practitioners understand the virtues and limitations of this type of data and information, this research will encourage the use of community volunteered information in the emergency operations center. | ||||
Address | Florida State University, United States | ||||
Corporate Author | Thesis | ||||
Publisher | Karlsruher Institut fur Technologie | Place of Publication | KIT; Baden-Baden | Editor | T. Comes, F. Fiedrich, S. Fortier, J. Geldermann and T. Müller |
Language | English | Summary Language | English | Original Title | |
Series Editor | Series Title | Abbreviated Series Title | |||
Series Volume | Series Issue | Edition | |||
ISSN | 2411-3387 | ISBN | 9783923704804 | Medium | |
Track | Social Media | Expedition | Conference | 10th International ISCRAM Conference on Information Systems for Crisis Response and Management | |
Notes | Approved | no | |||
Call Number | Serial | 767 | |||
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Author | Dharma Dailey; Kate Starbird | ||||
Title | Visible skepticism: Community vetting after Hurricane Irene | 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 | 777-781 | ||
Keywords | Hardware; Crisis informatics; Crowdsourcing; Information diffusion; Journalism; Misinformation; Rumors; Social media; Information systems | ||||
Abstract | Social media enable rapid, peer-to-peer information flow during crisis events, affordances that have both positive and negative consequences. The potential for spreading misinformation is a significant concern. Drawing on an empirical study of information-sharing practices in a crisis-affected community in the Catskill Mountains after Hurricane Irene, this paper describes how an ad hoc group of community members, led by a handful of journalists, employed specific work practices to mitigate misinformation. We illustrate how the group appropriated specific tools and performed visible skepticism, among other techniques, to help control the spread of false rumors. These findings suggest implications for the design of tools and the development of best practices for supporting community-led, crowd-powered response efforts during disasters. | ||||
Address | Human Centered Design and Engineering, University of Washington, 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 | 421 | |||
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Author | Diana Fischer; Carsten Schwemmer; Kai Fischbach | ||||
Title | Terror Management and Twitter: The Case of the 2016 Berlin Terrorist Attack | Type | Conference Article | ||
Year | 2018 | Publication | ISCRAM 2018 Conference Proceedings – 15th International Conference on Information Systems for Crisis Response and Management | Abbreviated Journal | Iscram 2018 |
Volume | Issue | Pages | 459-468 | ||
Keywords | Terrorist attacks, social networking sites, social media, Twitter, topic modeling, terror management, sense-making | ||||
Abstract | There is evidence that people increasingly use social networking sites like Twitter in the aftermath of terrorist attacks to make sense of the events at the collective level. This work-in-progress paper focuses on the content of Twitter messages related to the 2016 terrorist attack on the Berlin Christmas market. We chose topic modeling to investigate the Twitter data and the terror management theory perspective to understand why people used Twitter in the aftermath of the attack. In particular, by connecting people and providing a real-time communication channel, Twitter helps its users collectively negotiate their worldviews and re-establish self-esteem. We provide first results and discuss next steps. | ||||
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Corporate Author | Thesis | ||||
Publisher | Rochester Institute of Technology | Place of Publication | Rochester, NY (USA) | Editor | Kees Boersma; Brian Tomaszeski |
Language | English | Summary Language | English | Original Title | |
Series Editor | Series Title | Abbreviated Series Title | |||
Series Volume | Series Issue | Edition | |||
ISSN | 2411-3387 | ISBN | 978-0-692-12760-5 | Medium | |
Track | Social Media Studies | Expedition | Conference | ISCRAM 2018 Conference Proceedings - 15th International Conference on Information Systems for Crisis Response and Management | |
Notes | Approved | no | |||
Call Number | Serial | 2123 | |||
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Author | Dilini Rajapaksha; Kacper Sokol; Jeffrey Chan; Flora Salim; Mukesh Prasad; Mahendra Samarawickrama | ||||
Title | Analysing Donors’ Behaviour in Non-profit Organisations for Disaster Resilience | Type | Conference Article | ||
Year | 2023 | Publication | Proceedings of the ISCRAM Asia Pacific Conference 2022 | Abbreviated Journal | Proc. ISCRAM AP 2022 |
Volume | Issue | Pages | 258-267 | ||
Keywords | Disaster Response; Social Media; Donors’ Behaviour; Australian Bushfires | ||||
Abstract | With the advancement and proliferation of technology, non-profit organisations have embraced social media platforms to improve their operational capabilities through brand advocacy, among many other strategies. The effect of such social media campaigns on these institutions, however, remains largely underexplored, especially during disaster periods. This work introduces and applies a quantitative investigative framework to understand how social media influence the behaviour of donors and their usage of these platforms throughout (natural) disasters. More specifically, we explore how on-line engagement – as captured by Facebook interactions and Google search trends – corresponds to the donors’ behaviour during the catastrophic 2019–2020 Australian bushfire season. To discover this relationship, we analyse the record of donations made to the Australian Red Cross throughout this period. Our exploratory study reveals that social media campaigns are effective in encouraging on-line donations made via a dedicated website. We also compare this mode of giving to more regular, direct deposit gifting. | ||||
Address | RMIT University; RMIT University; RMIT University; UNSW Sydney; University of Technology Sydney; Australian Red Cross | ||||
Corporate Author | Thesis | ||||
Publisher | Massey Unversity | Place of Publication | Palmerston North, New Zealand | Editor | Thomas J. Huggins, V.L. |
Language | English | Summary Language | Original Title | ||
Series Editor | Series Title | Abbreviated Series Title | |||
Series Volume | Series Issue | Edition | |||
ISSN | 2411-3387 | ISBN | 978-0-473-66845-7 | Medium | |
Track | Social Media for Disaster Response | Expedition | Conference | ||
Notes | Approved | no | |||
Call Number | ISCRAM @ idladmin @ | Serial | 2499 | ||
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Author | Elodie Fichet; John Robinson; Dharma Dailey; Kate Starbird | ||||
Title | Eyes on the Ground: Emerging Practices in Periscope Use during Crisis Events | 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 | Social Media; Periscope; Twitter; Crisis Informatics; Emergency Management | ||||
Abstract | This empirical analysis examines the use of the live-streaming application Periscope in three crises that occurred in 2015. Qualitative analyses of tweets relating to the Amtrak derailment in Philadelphia, Baltimore protests after Freddie Grey?s death, and Hurricane Joaquin flooding in South Carolina reveal that this recently deployed application is being used by both citizens and journalists for information sharing, crisis coverage and commentary. The accessibility and immediacy of live video directly from crisis situations, and the embedded chats which overlay on top of a video feed, extend the possibilities of real-time interaction between remote crowds and those on the ground in a crisis. These empirical findings suggest several potential challenges and opportunities for responders. | ||||
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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 | ISCRAM @ idladmin @ | Serial | 1391 | ||
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Author | Emma Potter | ||||
Title | Balancing conflicting operational and communications priorities: social media use in an emergency management organization | 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 | Emergency Management; Social Media; Internal Communication; Disasters; Ethnography | ||||
Abstract | Social media are now widely used by affected members of the public during an emergency. As these platforms have become mainstream, governments have responded to the public?s expectation that information is available online, particularly during disasters. Emergency management organizations (EMOs) now widely use social media to communicate with the public alongside occasional intelligence gathering. While EMOs increasingly use social media, breakdowns in internal communication can inhibit the dissemination of timely information to their online followers. Drawing on a two-year ethnography at the Queensland Fire and Emergency Services (QFES), an Australian EMO, this paper outlines how the organization uses social media to disseminate information during emergencies and identifies the internal tensions around its use. These tensions include the prioritization of operational duties over public information responsibilities, and the difficulties around requesting and receiving information from operational personnel located on the ground. | ||||
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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 | 1398 | |||
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Author | Encarnación, T.; Wilks, C.R. | ||||
Title | Role of Expressed Emotions on the Retransmission of Help-Seeking Messages during Disasters | Type | Conference Article | ||
Year | 2023 | Publication | Proceedings of the 20th International ISCRAM Conference | Abbreviated Journal | Iscram 2023 |
Volume | Issue | Pages | 340-352 | ||
Keywords | Social Amplification; Retweet Prediction; Crisis Informatics | ||||
Abstract | Emergency managers rely on formal and informal communication channels to identify needs in post-disaster environments. Message retransmission is a critical factor to ensure that help-seekers are identified by disaster responders. This paper uses a novel annotated dataset of Twitter posts from four major disasters that impacted the United States in 2021, to quantify the effect that expressed emotions and support typology have on retransmission. Poisson regression models are estimated, and the results show that messages seeking instrumental support are more likely to be retransmitted. Expressions of anger, fear, and sadness increase overall retweets. Moreover, expressions of anger, anticipation, or sadness increase the likelihood of retransmission for messages that seek instrumental help. | ||||
Address | College of Business Administration University of Missouri-St | ||||
Corporate Author | Thesis | ||||
Publisher | University of Nebraska at Omaha | Place of Publication | Omaha, USA | Editor | Jaziar Radianti; Ioannis Dokas; Nicolas Lalone; Deepak Khazanchi |
Language | English | Summary Language | Original Title | ||
Series Editor | Hosssein Baharmand | Series Title | Abbreviated Series Title | ||
Series Volume | Series Issue | Edition | 1 | ||
ISSN | ISBN | Medium | |||
Track | Social Media for Crisis Management | Expedition | Conference | ||
Notes | http://dx.doi.org/10.59297/DDXJ4655 | Approved | no | ||
Call Number | ISCRAM @ idladmin @ | Serial | 2530 | ||
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Author | Faisal Luqman; Martin Griss | ||||
Title | Leveraging mobile context for effective collaboration and task management | Type | Conference Article | ||
Year | 2011 | Publication | 8th International Conference on Information Systems for Crisis Response and Management: From Early-Warning Systems to Preparedness and Training, ISCRAM 2011 | Abbreviated Journal | ISCRAM 2011 |
Volume | Issue | Pages | |||
Keywords | Carrier mobility; Information systems; Mobile devices; Agent-based systems; Collaboration; Command and control; Context information; Dynamic role-based; Emergent volunteer; Large scale disasters; Multi-agent; Human resource management | ||||
Abstract | Collaboration and task management is challenging in distributed, dynamically-formed teams, typical in large scale disaster response scenarios. Ineffective collaboration may potentially result in poor performance and loss of life. The increased adoption of sensor rich mobile devices allow for mobile context to be leveraged. In this paper, we present Overseer, an agent-based system that exploits context information from mobile devices to facilitate collaboration and task allocation. We describe how mobile context can be used to create dynamic role-based assignments to enhance collaboration and effective task management. | ||||
Address | Carnegie Mellon Silicon Valley, United States | ||||
Corporate Author | Thesis | ||||
Publisher | Information Systems for Crisis Response and Management, ISCRAM | Place of Publication | Lisbon | Editor | M.A. Santos, L. Sousa, E. Portela |
Language | English | Summary Language | English | Original Title | |
Series Editor | Series Title | Abbreviated Series Title | |||
Series Volume | Series Issue | Edition | |||
ISSN | 2411-3387 | ISBN | 9789724922478 | Medium | |
Track | Social Media and Collaborative Systems | Expedition | Conference | 8th International ISCRAM Conference on Information Systems for Crisis Response and Management | |
Notes | Approved | no | |||
Call Number | Serial | 730 | |||
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Author | Fatehkia, M.; Imran, M.; Weber, I. | ||||
Title | Towards Real-time Remote Social Sensing via Targeted Advertising | Type | Conference Article | ||
Year | 2023 | Publication | Proceedings of the 20th International ISCRAM Conference | Abbreviated Journal | Iscram 2023 |
Volume | Issue | Pages | 396-406 | ||
Keywords | Remote Social Sensing; Real-Time Polling; Flood Mapping; Facebook Advertising | ||||
Abstract | Social media serves as an important communication channel for people affected by crises, creating a data source for emergency responders wanting to improve situational awareness. In particular, social listening on Twitter has been widely used for real-time analysis of crisis-related messages. This approach, however, is often hindered by the small fraction of (hyper-)localized content and by the inability to explicitly ask affected populations about aspects with the most operational value. Here, we explore a new form of social media data collected through targeted poll ads on Facebook. Using geo-targeted ads during flood events in six countries, we show that it is possible to collect thousands of poll responses within hours of launching the ad campaign, and at a cost of a few (US dollar) cents per response. We believe that this flexible, fast, and affordable data collection can serve as a valuable complement to existing approaches. | ||||
Address | Qatar Computing Research Institute; Qatar Computing Research Institute; Saarland Informatics Campus | ||||
Corporate Author | Thesis | ||||
Publisher | University of Nebraska at Omaha | Place of Publication | Omaha, USA | Editor | Jaziar Radianti; Ioannis Dokas; Nicolas Lalone; Deepak Khazanchi |
Language | English | Summary Language | Original Title | ||
Series Editor | Hosssein Baharmand | Series Title | Abbreviated Series Title | ||
Series Volume | Series Issue | Edition | 1 | ||
ISSN | ISBN | Medium | |||
Track | Social Media for Crisis Management | Expedition | Conference | ||
Notes | http://dx.doi.org/10.59297/NEFN8739 | Approved | no | ||
Call Number | ISCRAM @ idladmin @ | Serial | 2534 | ||
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Author | Fedor Vitiugin; Carlos Castillo | ||||
Title | Comparison of Social Media in English and Russian During Emergencies and Mass Convergence Events | 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 | Social Media, Crisis Informatics, Twitter, Information Extraction. | ||||
Abstract | Twitter is used for spreading information during crisis events. In this paper, we first retrieve event-related information posted in English and Russian during six disasters and sports events that received wide media coverage in both languages, using an adaptive information filtering method for automating the collection of about 100 000 messages. We then compare the contents of these messages in terms of 17 informational and linguistic features using a difference in differences approach. Our results suggest that posts in each language are focused on different types of information. For instance, almost 50% of the popular people mentioned in these messages appear exclusively in either the English messages or the Russian messages, but not both. Our results also suggest differences in the adoption of platform mechanics during crises between Russian-speaking and English-speaking users. This has important implications for data collection during crises, which is almost always focused on a single language. |
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Address | Independent;Universitat Pompeu Fabra | ||||
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 | ISCRAM @ idladmin @ | Serial | 1916 | ||
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Author | Femke Mulder; Kees Boersma | ||||
Title | Linking up the last mile: how humanitarian power relations shape community e-resilience | 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 | 715-725 | ||
Keywords | Power relations; e-resilience; humanitarian disaster; social capital; Nepal | ||||
Abstract | In this paper we present a qualitative, social network based, power analysis of relief and recovery efforts in the aftermath of the 2015 earthquakes in Nepal. We examine how the interplay between humanitarian power relations and e-resilience influenced communities' ability to respond to the destruction brought about by the disaster. We focus in particular on how power dynamics affect online spaces and interactions at the hyper local level (or 'the last mile'). We explain how civic technology initiatives are affected by these power relationships and show how their efforts may reinforce social inequalities – or be sidelined – if power dynamics are not taken into consideration. However, on the basis of a case study based power analysis, we show that when civic technology initiatives do strategically engage with these dynamics, they have the potential to alter harmful power relations that limit community e-resilience. | ||||
Address | Vrije Universiteit Amsterdam | ||||
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 | 2059 | |||
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Author | Ferda Ofli; Firoj Alam; Muhammad Imran | ||||
Title | Analysis of Social Media Data using Multimodal Deep Learning for Disaster Response | Type | Conference Article | ||
Year | 2020 | Publication | ISCRAM 2020 Conference Proceedings – 17th International Conference on Information Systems for Crisis Response and Management | Abbreviated Journal | Iscram 2020 |
Volume | Issue | Pages | 802-811 | ||
Keywords | Multimodal Deep Learning, Multimedia Content, Natural Disasters, Crisis Computing, Social Media. | ||||
Abstract | Multimedia content in social media platforms provides significant information during disaster events. The types of information shared include reports of injured or deceased people, infrastructure damage, and missing or found people, among others. Although many studies have shown the usefulness of both text and image content for disaster response purposes, the research has been mostly focused on analyzing only the text modality in the past. In this paper, we propose to use both text and image modalities of social media data to learn a joint representation using state-of-the-art deep learning techniques. Specifically, we utilize convolutional neural networks to define a multimodal deep learning architecture with a modality-agnostic shared representation. Extensive experiments on real-world disaster datasets show that the proposed multimodal architecture yields better performance than models trained using a single modality (e.g., either text or image). | ||||
Address | Qatar Computing Research Institute Hamad Bin Khalifa University Doha, Qatar; Qatar Computing Research Institute Hamad Bin Khalifa University Doha, Qatar; Qatar Computing Research Institute Hamad Bin Khalifa University Doha, Qatar | ||||
Corporate Author | Thesis | ||||
Publisher | Virginia Tech | Place of Publication | Blacksburg, VA (USA) | Editor | Amanda Hughes; Fiona McNeill; Christopher W. Zobel |
Language | English | Summary Language | English | Original Title | |
Series Editor | Series Title | Abbreviated Series Title | |||
Series Volume | Series Issue | Edition | |||
ISSN | 978-1-949373-27-71 | ISBN | 2411-3457 | Medium | |
Track | Social Media for Disaster Response and Resilie | Expedition | Conference | 17th International Conference on Information Systems for Crisis Response and Management | |
Notes | fofli@hbku.edu.qa | Approved | no | ||
Call Number | Serial | 2272 | |||
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Author | Firoj Alam; Ferda Ofli; Muhammad Imran | ||||
Title | CrisisDPS: Crisis Data Processing Services | 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 | Social media, humanitarian data processing, text classification, application programming interfaces, data processing services | ||||
Abstract | Over the last few years, extensive research has been conducted to develop technologies to support humanitarian aid tasks. However, many technologies are still limited as they require both manual and automatic approaches, and more importantly, are not ready to be integrated into the disaster response workflows. To tackle this limitation, we develop automatic data processing services that are freely and publicly available, and made to be simple, efficient, and accessible to non-experts. Our services take textual messages (e.g., tweets, Facebook posts, SMS) as input to determine (i) which disaster type the message belongs to, (ii) whether it is informative or not, and (iii) what type of humanitarian information it conveys. We built our services upon machine learning classifiers that are obtained from large-scale comparative experiments utilizing both classical and deep learning algorithms. Our services outperform state-of-the-art publicly available tools in terms of classification accuracy. |
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Address | Qatar Computing Research Institute, Qatar | ||||
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 | 1891 | |||
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Author | Firoj Alam; Ferda Ofli; Muhammad Imran; Michael Aupetit | ||||
Title | A Twitter Tale of Three Hurricanes: Harvey, Irma, and Maria | Type | Conference Article | ||
Year | 2018 | Publication | ISCRAM 2018 Conference Proceedings – 15th International Conference on Information Systems for Crisis Response and Management | Abbreviated Journal | Iscram 2018 |
Volume | Issue | Pages | 553-572 | ||
Keywords | social media, artificial intelligence, image processing, supervised classification, disaster management | ||||
Abstract | People increasingly use microblogging platforms such as Twitter during natural disasters and emergencies. Research studies have revealed the usefulness of the data available on Twitter for several disaster response tasks. However, making sense of social media data is a challenging task due to several reasons such as limitations of available tools to analyze high-volume and high-velocity data streams. This work presents an extensive multidimensional analysis of textual and multimedia content from millions of tweets shared on Twitter during the three disaster events. Specifically, we employ various Artificial Intelligence techniques from Natural Language Processing and Computer Vision fields, which exploit different machine learning algorithms to process the data generated during the disaster events. Our study reveals the distributions of various types of useful information that can inform crisis managers and responders as well as facilitate the development of future automated systems for disaster management. | ||||
Address | |||||
Corporate Author | Thesis | ||||
Publisher | Rochester Institute of Technology | Place of Publication | Rochester, NY (USA) | Editor | Kees Boersma; Brian Tomaszeski |
Language | English | Summary Language | English | Original Title | |
Series Editor | Series Title | Abbreviated Series Title | |||
Series Volume | Series Issue | Edition | |||
ISSN | 2411-3387 | ISBN | 978-0-692-12760-5 | Medium | |
Track | Social Media Studies | Expedition | Conference | ISCRAM 2018 Conference Proceedings - 15th International Conference on Information Systems for Crisis Response and Management | |
Notes | Approved | no | |||
Call Number | Serial | 2131 | |||
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Author | Francesca Comunello; Simone Mulargia | ||||
Title | A #cultural_change is needed. Social media use in emergency communication by Italian local level institutions | 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 | 512-521 | ||
Keywords | Social media; local level; emergency communication; barriers | ||||
Abstract | We discuss the results of a research project aimed at exploring the use of social media in emergency communication by officers operating at a local level. We performed 16 semi-structured interviews with national level expert informants, and with officers operating at the municipality and province (prefectures) level in an Italian region (respondents were selected based on their involvement in emergency communication and/or emergency management processes). Social media usage appears distributed over a continuum of engagement, ranging from very basic usage to using social media by adopting a broadcasting approach, to deeper engagement, which also includes continuous interaction with citizens. Two main attitudes emerge both in the narrative style and in social media representations: some respondents seem to adopt an institutional attitude, while others adopt a practical-professional attitude. Among the main barriers to a broader adoption of social media, cultural considerations seem to prevail, along with the lack of personnel, a general concern toward social media communication reliability, and the perceived distance between the formal role of institutions and the informal nature of social media communication. | ||||
Address | LUMSA University, Rome, Italy; Sapienza University of Rome, Italy | ||||
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 | 2039 | |||
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Author | Francesca Comunello; Simone Mulargia; Piero Polidoro; Emanuele Casarotti; Valentino Lauciani | ||||
Title | No Misunderstandings During Earthquakes: Elaborating and Testing a Standardized Tweet Structure for Automatic Earthquake Detection Information | Type | Conference Article | ||
Year | 2015 | Publication | ISCRAM 2015 Conference Proceedings ? 12th International Conference on Information Systems for Crisis Response and Management | Abbreviated Journal | ISCRAM 2015 |
Volume | Issue | Pages | |||
Keywords | Automatic detection; earthquakes; tweet comprehension; tweet syntax; Twitter | ||||
Abstract | Social media have proven to be useful resources for spreading verified information during natural disasters. Nevertheless, little attention has hitherto been devoted to the peculiarities of constructing effective tweets (and tweet formats), or to common users? comprehension of tweets conveying scientific information. In this paper, social scientists and seismologists collaborated in order to elaborate and test a standardized tweet structure to be used during earthquakes, expanding on the results of a quali-quantitative research project. The tweet format is specifically designed to launch an innovative information service by Istituto Nazionale di Geofisica e Vulcanologia (INGV): tweeting the automatic detection of earthquakes with a magnitude greater than 3. This paper illustrates the steps of the research process that led to elaborating a tweet format that will be used in the next few months by the official Twitter account @INGVterremoti. | ||||
Address | |||||
Corporate Author | Thesis | ||||
Publisher | University of Agder (UiA) | Place of Publication | Kristiansand, Norway | Editor | L. Palen; M. Buscher; T. Comes; A. Hughes |
Language | English | Summary Language | English | Original Title | |
Series Editor | Series Title | Abbreviated Series Title | |||
Series Volume | Series Issue | Edition | |||
ISSN | 2411-3387 | ISBN | 9788271177881 | Medium | |
Track | Social Media Studies | Expedition | Conference | ISCRAM 2015 Conference Proceedings ? 12th International Conference on Information Systems for Crisis Response and Management | |
Notes | Approved | yes | |||
Call Number | Serial | 1232 | |||
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Author | Gabriela C Barrera; Maria C Yang | ||||
Title | Evaluation of Digital Volunteers using a Design Approach: Motivations and Contributions in Disaster Response | 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 | Crowd-sourcing, Social Media, Digital Volunteer, Spatial Data Quality, User Design | ||||
Abstract | With the growth of social media and crowdsourcing in disaster response, further research is needed on the motivations and contributions of digital volunteers. This study applies a user-centered design approach to understanding how we might make better tools to support digital volunteers. This user-centered design approach involves stated preference elicitation methods through an online survey to understand what digital volunteers want in such tools. Through choice-based conjoint analysis, we contribute to mixed-methods research to gain additional insight into motivations and user preferences for a set of design features that might be incorporated into an online tool specifically for digital volunteers. Initial results show preferences for measures of success that were not monetary, which aligned with directly stated motivations for volunteering. Our findings corroborate with previous research in that feedback to volunteers is very important, as well as being able to measure the impact of their work. |
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Address | MIT, Cambridge, MA, United States of America | ||||
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 | 1970 | |||
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Author | Gaëtan Caillaut; Cécile Gracianne; Nathalie Abadie; Guillaume Touya; Samuel Auclair | ||||
Title | Automated Construction of a French Entity Linking Dataset to Geolocate Social Network Posts in the Context of Natural Disasters | Type | Conference Article | ||
Year | 2022 | Publication | ISCRAM 2022 Conference Proceedings – 19th International Conference on Information Systems for Crisis Response and Management | Abbreviated Journal | Iscram 2022 |
Volume | Issue | Pages | 654-663 | ||
Keywords | Automated geotagging; French Entity Linking; Wikipedia; Twitter; Crisis Management; Natural Disaster | ||||
Abstract | During natural disasters, automatic information extraction from Twitter posts is a valuable way to get a better overview of the field situation. This information has to be geolocated to support effective actions, but for the vast majority of tweets, spatial information has to be extracted from texts content. Despite the remarkable advances of the Natural Language Processing field, this task is still challenging for current state-of-the-art models because they are not necessarily trained on Twitter data and because high quality annotated data are still lacking for low resources languages. This research in progress address this gap describing an analytic pipeline able to automatically extract geolocatable entities from texts and to annotate them by aligning them with the entities present in Wikipedia/Wikidata resources. We present a new dataset for Entity Linking on French texts as preliminary results, and discuss research perspectives for enhancements over current state-of-the-art modeling for this task. | ||||
Address | BRGM; BRGM; LASTIG, Univ Gustave Eiffel, IGN-ENSG; LASTIG, Univ Gustave Eiffel, IGN-ENSG; BRGM | ||||
Corporate Author | Thesis | ||||
Publisher | Place of Publication | Tarbes, France | Editor | Rob Grace; Hossein Baharmand | |
Language | English | Summary Language | Original Title | ||
Series Editor | Series Title | Abbreviated Series Title | |||
Series Volume | Series Issue | Edition | |||
ISSN | 2411-3387 | ISBN | 978-82-8427-099-9 | Medium | |
Track | Social Media for Crisis Management | Expedition | Conference | ||
Notes | Approved | no | |||
Call Number | ISCRAM @ idladmin @ | Serial | 2445 | ||
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Author | Gayane Shalunts; Gerhard Backfried; Prinz Prinz | ||||
Title | Sentiment analysis of German social media data for natural disasters | 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 | 752-756 | ||
Keywords | Disasters; Information systems; First responders; Integral part; Media analysis; Multiple languages; Natural disasters; Sentiment analysis; Social media; Social media datum; Data mining | ||||
Abstract | Analysis of social media and traditional media provides significant information to first responders in times of natural disasters. Sentiment analysis, particularly of social media originating from the affected population, forms an integral part of multifaceted media analysis. The current paper extends an existing methodology to the domain of natural disasters, broadens the support of multiple languages and introduces a new manner of classification. The performance of the approach is evaluated on a recently collected dataset manually annotated by three human annotators as a reference. The experiments show a high agreement rate between the approach taken and the annotators. Furthermore, the paper presents the initial application of the resulting technology and models to sentiment analysis of social media data in German, covering data collected during the Central European floods of 2013. | ||||
Address | SAIL LABS Technology AG, Vienna, Austria | ||||
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 | 940 | |||
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