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Author |
Adam Flizikowski, Marcin Przybyszewski; Anna Stachowicz; Tomasz Olejniczak; Rafael Renk |
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Title |
Text Analysis Tool TWeet lOcator ? TAT2 |
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Conference Article |
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Year |
2015 |
Publication |
ISCRAM 2015 Conference Proceedings ? 12th International Conference on Information Systems for Crisis Response and Management |
Abbreviated Journal |
ISCRAM 2015 |
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Keywords |
AIDA; Crisis Management; iSAR+; location of Twitter messages; social media |
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Abstract |
Information about location and geographical coordinates in particular, may be very important during a crisis event, especially for search and rescue operations ? but currently geo-tagged tweets are extremely rare. Improved capabilities of capturing additional location from Twitter (up to 4 times improvement) are crucial for response efforts given a vast amount of messages exchanged during a crisis event. That is why authors have designed a tool (Text Analysis TWeet lOcator ? TAT2) that relies on existing open source text analysis tools with additional services to provide additional hints about people location. Validation process, complementing experimentation and test results, included involvement of end-users (i.e. Public Protection and Disaster Relief services and citizens during a realistic crisis exercise showcase. In addition, the integration of TAT2 with external tools has also been validated. |
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University of Agder (UiA) |
Place of Publication |
Kristiansand, Norway |
Editor |
L. Palen; M. Buscher; T. Comes; A. Hughes |
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English |
Summary Language |
English |
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ISSN |
2411-3387 |
ISBN |
9788271177881 |
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Track |
Social Media Studies |
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Conference |
ISCRAM 2015 Conference Proceedings ? 12th International Conference on Information Systems for Crisis Response and Management |
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yes |
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Call Number |
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1227 |
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Author |
Ahmed Alnuhayt; Suvodeep Mazumdar; Vitaveska Lanfranchi; Frank Hopfgartner |
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Title |
Understanding Reactions to Misinformation – A Covid-19 Perspective |
Type |
Conference Article |
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Year |
2022 |
Publication |
ISCRAM 2022 Conference Proceedings – 19th International Conference on Information Systems for Crisis Response and Management |
Abbreviated Journal |
Iscram 2022 |
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Pages |
687-700 |
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Keywords |
Misinformation; social reactions; twitter; people; COVID-19 |
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Abstract |
The increasing use of social media as an information source brings further challenges – social media platforms can be an excellent medium for disseminating public awareness and critical information, that can be shared across large populations. However, misinformation in social media can have immense implications on public health, risking the effectiveness of health interventions as well as lives. This has been particularly true in the case of COVID-19 pandemic, with a range of misinformation, conspiracy theories and propaganda being spread across social channels. In our study, through a questionnaire survey, we set out to understand how members of the public interact with different sources when looking for information on COVID-19. We explored how participants react when they encounter information they believe to be misinformation. Through a set of three behaviour tasks, synthetic misinformation posts were provided to the participants who chose how they would react to them. In this work in progress study, we present initial findings and insights into our analysis of the data collected. We highlight what are the most common reactions to misinformation and also how these reactions are different based on the type of misinformation. |
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Information School University of Sheffield; Information School University of Sheffield; Computer Science University of Sheffield; Information School University of Sheffield |
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Place of Publication |
Tarbes, France |
Editor |
Rob Grace; Hossein Baharmand |
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English |
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ISSN |
2411-3387 |
ISBN |
978-82-8427-099-9 |
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Track |
Social Media for Crisis Management |
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no |
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Call Number |
ISCRAM @ idladmin @ |
Serial |
2448 |
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Author |
Ahmed Nagy; Jeannie Stamberger |
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Title |
Crowd sentiment detection during disasters and crises |
Type |
Conference Article |
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Year |
2012 |
Publication |
ISCRAM 2012 Conference Proceedings – 9th International Conference on Information Systems for Crisis Response and Management |
Abbreviated Journal |
ISCRAM 2012 |
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Keywords |
Bayesian networks; Emergency services; Information systems; Risk management; Social networking (online); Crisis management; Disaster response; Emergency management; Short message; Twitter; Disasters |
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Abstract |
Microblogs are an opportunity for scavenging critical information such as sentiments. This information can be used to detect rapidly the sentiment of the crowd towards crises or disasters. It can be used as an effective tool to inform humanitarian efforts, and improve the ways in which informative messages are crafted for the crowd regarding an event. Unique characteristics of microblogs (lack of context, use of jargon etc) in Tweets expressed by a message-sharing social network during a disaster response require special handling to identify sentiment. We present a systematic evaluation of approaches to accurately and precisely identify sentiment in these Tweets. This paper describes sentiment detection expressed in 3698 Tweets, collected during the September 2010, San Bruno, California gas explosion and resulting fires. The data collected was manually coded to benchmark our techniques. We start by using a library of words with annotated sentiment, SentiWordNet 3.0, to detect the basic sentiment of each Tweet. We complemented that technique by adding a comprehensive list of emoticons, a sentiment based dictionary and a list of out-of-vocabulary words that are popular in brief, online text communications such as lol, wow, etc. Our technique performed 27% better than Bayesian Networks alone, and the combination of Bayesian networks with annotated lists provided marginal improvements in sentiment detection than various combinations of lists. © 2012 ISCRAM. |
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Address |
Carnegie Mellon Silicon Valley, IMT Lucca Institute of Advanced Studies, United States; Disaster Management Initiative, Carnegie Mellon Silicon Valley, United States |
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Publisher |
Simon Fraser University |
Place of Publication |
Vancouver, BC |
Editor |
L. Rothkrantz, J. Ristvej, Z.Franco |
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Language |
English |
Summary Language |
English |
Original Title |
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Abbreviated Series Title |
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Series Volume |
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Series Issue |
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Edition |
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ISSN |
2411-3387 |
ISBN |
9780864913326 |
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Track |
Social Media and Collaborative Systems |
Expedition |
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Conference |
9th International ISCRAM Conference on Information Systems for Crisis Response and Management |
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Approved |
no |
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Call Number |
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Serial |
173 |
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Author |
Amanda L. Hughes; Leysia Palen |
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Title |
Twitter adoption and use in mass convergence and emergency events |
Type |
Conference Article |
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Year |
2009 |
Publication |
ISCRAM 2009 – 6th International Conference on Information Systems for Crisis Response and Management: Boundary Spanning Initiatives and New Perspectives |
Abbreviated Journal |
ISCRAM 2009 |
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Keywords |
Information systems; Crisis informatics; Emergency; Micro-blogging; Social media; Twitter; Social networking (online) |
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Abstract |
This paper offers a descriptive account of Twitter (a micro-blogging service) across four high profile, mass convergence events-two emergency and two national security. We statistically examine how Twitter is being used surrounding these events, and compare and contrast how that behavior is different from more general Twitter use. Our findings suggest that Twitter messages sent during these types of events contain more displays of information broadcasting and brokerage, and we observe that general Twitter use seems to have evolved over time to offer more of an information-sharing purpose. We also provide preliminary evidence that Twitter users who join during and in apparent relation to a mass convergence or emergency event are more likely to become long-term adopters of the technology. |
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University of Colorado, Boulder, United States |
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Publisher |
Information Systems for Crisis Response and Management, ISCRAM |
Place of Publication |
Gothenburg |
Editor |
J. Landgren, S. Jul |
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Language |
English |
Summary Language |
English |
Original Title |
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Abbreviated Series Title |
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ISSN |
2411-3387 |
ISBN |
9789163347153 |
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Track |
Collaboration and Social Networking |
Expedition |
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Conference |
6th International ISCRAM Conference on Information Systems for Crisis Response and Management |
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Approved |
no |
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Call Number |
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Serial |
604 |
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Author |
André Dittrich; Christian Lucas |
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Title |
A step towards real-time analysis of major disaster events based on tweets |
Type |
Conference Article |
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Year |
2013 |
Publication |
ISCRAM 2013 Conference Proceedings – 10th International Conference on Information Systems for Crisis Response and Management |
Abbreviated Journal |
ISCRAM 2013 |
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Issue |
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Pages |
868-874 |
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Keywords |
Information systems; Semantics; Social networking (online); Crisis management; Event detection; Functional model; Micro-blogging platforms; Real time analysis; Semantic content analysis; Social sensors; Twitter; Disasters |
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Abstract |
The most popular micro blogging platform Twitter has been the topic of a variety of research papers related to disaster and crisis management. As an essential first step and basis for a real-time methodology to exploit Twitter for event detection, localization and ultimately semantic content analysis, a functional model to describe the amount of tweets during a day has been developed. It was derived from a corpus of messages in an exemplary area of investigation. To satisfy the different daily behavior on particular days, two types of days are distinguished in this paper. Moreover, keyword-adjusted data is used to point out the potential of semantic tweet analysis in following steps. The consideration of spatial event descriptions in relevant tweets could significantly improve and accelerate the perception of a disaster. The results from the conducted tests demonstrate the capability of the functional model to detect events with significant social impact in Twitter data. |
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Karlsruhe Institute of Technology (KIT), Germany |
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Publisher |
Karlsruher Institut fur Technologie |
Place of Publication |
KIT; Baden-Baden |
Editor |
T. Comes, F. Fiedrich, S. Fortier, J. Geldermann and T. Müller |
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Language |
English |
Summary Language |
English |
Original Title |
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Series Volume |
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Edition |
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ISSN |
2411-3387 |
ISBN |
9783923704804 |
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Track |
Social Media |
Expedition |
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Conference |
10th International ISCRAM Conference on Information Systems for Crisis Response and Management |
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Notes |
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Approved |
no |
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Call Number |
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Serial |
452 |
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Author |
Andrea H. Tapia; Kartikeya Bajpai; Bernard J. Jansen; John Yen |
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Title |
Seeking the trustworthy tweet: Can microblogged data fit the information needs of disaster response and humanitarian relief organizations |
Type |
Conference Article |
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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 |
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Pages |
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Keywords |
Disasters; Information systems; Public relations; Humanitarian; Microblogging; Ngo; Relief; Trust; Twitter; Emergency services |
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Abstract |
Message data has, as yet, not been adopted by large-scale, international humanitarian relief organizations in an instrumental fashion. While the largest of these organizations have adopted messaging as part of their Public Relations functions, few have used any form of message data originating in the field, at the time of disaster. The message data being contributed by bystanders and those affected by a disaster, as it is happening, has largely been deemed as unverifiable and untrustworthy, and thus construed as unsuitable for incorporation into established mechanisms for organizational decision-making. In this paper, we describe the discursive barriers to the use of microblogged data by Humanitarian NGOs during times of disaster. We present data and findings from a study involving representatives from thirteen humanitarian organizations. Our analysis suggests that the organizational barriers, both in terms of function and structure, and the data itself, form barriers to organizational use of microblogged data. We propose three socio-technical solutions to surpassing adoption bottlenecks, namely bounded microblogging, microblogged data as contextual data, and/or use of computational solutions. |
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Pennsylvania State University, United States |
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Publisher |
Information Systems for Crisis Response and Management, ISCRAM |
Place of Publication |
Lisbon |
Editor |
M.A. Santos, L. Sousa, E. Portela |
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Language |
English |
Summary Language |
English |
Original Title |
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Abbreviated Series Title |
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Series Volume |
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ISSN |
2411-3387 |
ISBN |
9789724922478 |
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Track |
Humanitarian Challenges |
Expedition |
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Conference |
8th International ISCRAM Conference on Information Systems for Crisis Response and Management |
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Approved |
no |
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Call Number |
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Serial |
991 |
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Author |
Andrea H. Tapia; Kathleen A. Moore; Nichloas J. Johnson |
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Title |
Beyond the trustworthy tweet: A deeper understanding of microblogged data use by disaster response and humanitarian relief organizations |
Type |
Conference Article |
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Year |
2013 |
Publication |
ISCRAM 2013 Conference Proceedings – 10th International Conference on Information Systems for Crisis Response and Management |
Abbreviated Journal |
ISCRAM 2013 |
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Volume |
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Pages |
770-779 |
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Keywords |
Disasters; Information management; Information systems; Societies and institutions; Humanitarian; Microblogging; Ngo; Relief; Trust; Twitter; Emergency services |
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Abstract |
In this paper we present findings from interviews conducted with representatives from large international disaster response organizations concerning their use of social media data in crisis response. We present findings in which the barriers to use by responding organizations have gone beyond simple discussions of trustworthiness to that of more operational issues rather than mere data quality. We argue that the landscape of the use of microblogged data in crisis response is varied, with pockets of use and acceptance among organizations. We found that microblogged data is useful to responders in situations where information is limited, such as at the beginning of an emergency response effort, and when the risks of ignoring an accurate response outweigh the risks of acting on an incorrect one. In some situations, such as search and rescue operations, microblogged data may never meet the standards of quality required. In others, such as resource and supply management, microblogging data could be useful as long as it is appropriately verified and classified. |
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Address |
College of Information Sciences and Technology, Penn State University, United States |
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Publisher |
Karlsruher Institut fur Technologie |
Place of Publication |
KIT; Baden-Baden |
Editor |
T. Comes, F. Fiedrich, S. Fortier, J. Geldermann and T. Müller |
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Language |
English |
Summary Language |
English |
Original Title |
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Series Editor |
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Abbreviated Series Title |
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Series Volume |
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ISSN |
2411-3387 |
ISBN |
9783923704804 |
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Track |
Social Media |
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Conference |
10th International ISCRAM Conference on Information Systems for Crisis Response and Management |
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Notes |
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Approved |
no |
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Call Number |
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Serial |
993 |
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Author |
Andrea H. Tapia; Nicolas LaLone; Elizabeth MacDonald; Reid Priedhorsky; Hall Hall |
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Title |
Crowdsourcing rare events: Using curiosity to draw participants into science and early warning systems |
Type |
Conference Article |
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Year |
2014 |
Publication |
ISCRAM 2014 Conference Proceedings – 11th International Conference on Information Systems for Crisis Response and Management |
Abbreviated Journal |
ISCRAM 2014 |
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Issue |
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Pages |
135-144 |
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Keywords |
Information systems; Observatories; Satellite observatories; Aurora; Citizen science; Early Warning System; Space weather; Twitter; Alarm systems |
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Abstract |
This research presents a centralized boundary object website and mobile app focused on allowing participants to participate in developing an early warning system through space weather and the beauty of the aurora borealis. Because of the beauty and majesty of auroral activity, people will seek information about when and where these unpredictable events occur. This activity, commonly referred to as nowcasting, can be combined with scientific data collected from observatories and satellites and serve as an early warning system with potentially far greater accuracy and timeliness than the current state of the art. We believe that long-term engagement with a citizen science tool will help bridge the many social worlds surrounding the aurora borealis and lead to the development of an early warning system that may correlate the visibility of the northern lights to violent space weather. We hope this will lead to other real time crowdsourced early warning systems in the future. |
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Penn State University, United States; NASA, GSFC, United States; LANL, United States; Science Education Solutions, United States |
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The Pennsylvania State University |
Place of Publication |
University Park, PA |
Editor |
S.R. Hiltz, M.S. Pfaff, L. Plotnick, and P.C. Shih. |
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Language |
English |
Summary Language |
English |
Original Title |
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ISSN |
2411-3387 |
ISBN |
9780692211946 |
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Track |
Community Engagement in Crisis Informatics Research |
Expedition |
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Conference |
11th International ISCRAM Conference on Information Systems for Crisis Response and Management |
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Approved |
no |
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Call Number |
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Serial |
994 |
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Author |
Andrea H. Tapia; Nicolas LaLone; Hyun-Woo Kim |
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Title |
Run amok: Group crowd participation in identifying the bomb and bomber from the Boston marathon bombing |
Type |
Conference Article |
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Year |
2014 |
Publication |
ISCRAM 2014 Conference Proceedings – 11th International Conference on Information Systems for Crisis Response and Management |
Abbreviated Journal |
ISCRAM 2014 |
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Volume |
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Issue |
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Pages |
265-274 |
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Keywords |
Information systems; Social networking (online); Crowdsourcing; Ethical participation; First responders; Social responsibilities; Twitter; Emergency services |
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Abstract |
In this paper we tell a version of the story of the bombing of the Boston Marathon. At first, two online groups gathered images, video and textual information concerning the bombing of the Boston Marathon and shared these with the FBI and amongst themselves. Secondly, these groups then created mechanisms to conduct their own investigation into the identities of the perpetrators. Finally, the larger national media followed the results of these online group investigations and reported these as fact to a national audience. We choose Twitter as our data repository and conducted quantitative analyses of tweets sent during the Boston Bombing. The implications for not incorporating public crowd participation within the standard operating procedures of emergency services may result in either a loss of public confidence in the slow-moving nature of official response to uncontrollable, dangerous and irresponsible public and media participation that exacerbates the negative effects of any disaster. |
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Address |
Penn State University, United States |
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Publisher |
The Pennsylvania State University |
Place of Publication |
University Park, PA |
Editor |
S.R. Hiltz, M.S. Pfaff, L. Plotnick, and P.C. Shih. |
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Language |
English |
Summary Language |
English |
Original Title |
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Series Volume |
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Edition |
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ISSN |
2411-3387 |
ISBN |
9780692211946 |
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Track |
Ethical, Legal and Social Issues of IT Supported Emergency Response |
Expedition |
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Conference |
11th International ISCRAM Conference on Information Systems for Crisis Response and Management |
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Approved |
no |
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Call Number |
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Serial |
992 |
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Author |
Andrea Zielinski; Ulrich Bügel |
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Title |
Multilingual analysis of twitter news in support of mass emergency events |
Type |
Conference Article |
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Year |
2012 |
Publication |
ISCRAM 2012 Conference Proceedings – 9th International Conference on Information Systems for Crisis Response and Management |
Abbreviated Journal |
ISCRAM 2012 |
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Issue |
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Pages |
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Keywords |
Disasters; Earthquakes; Information retrieval systems; Information systems; Sensor networks; Cross-lingual information; Early Warning System; Earthquake events; Event detection; Multilingual analysis; Social sensors; Support crisis management; Twitter; Social networking (online) |
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Abstract |
Social media are increasingly becoming a source for event-based early warning systems in the sense that they can help to detect natural disasters and support crisis management during or after disasters. In this work-in-progress paper we study the problems of analyzing multilingual twitter feeds for emergency events. The present work focuses on English as “lingua franca” and on under-resourced Mediterranean languages in endangered zones, particularly Turkey, Greece, and Romania Generally, as local civil protection authorities and the population are likely to respond in their native language. We investigated ten earthquake events and defined four language-specific classifiers that can be used to detect earthquakes by filtering out irrelevant messages that do not relate to the event. The final goal is to extend this work to more Mediterranean languages and to classify and extract relevant information from tweets, translating the main keywords into English. Preliminary results indicate that such a filter has the potential to confirm forecast parameters of tsunami affecting coastal areas where no tide gauges exist and could be integrated into seismographic sensor networks. © 2012 ISCRAM. |
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Address |
Fraunhofer IOSB, Germany |
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Publisher |
Simon Fraser University |
Place of Publication |
Vancouver, BC |
Editor |
L. Rothkrantz, J. Ristvej, Z.Franco |
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Language |
English |
Summary Language |
English |
Original Title |
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Series Volume |
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Edition |
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ISSN |
2411-3387 |
ISBN |
9780864913326 |
Medium |
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Track |
Command and Control Studies |
Expedition |
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Conference |
9th International ISCRAM Conference on Information Systems for Crisis Response and Management |
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Notes |
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Approved |
no |
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Call Number |
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Serial |
245 |
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Author |
Anjum, U.; Zadorozhny, V.; Krishnamurthy, P. |
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Title |
Localization of Events Using Neural Networks in Twitter Data |
Type |
Conference Article |
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Year |
2023 |
Publication |
Proceedings of the 20th International ISCRAM Conference |
Abbreviated Journal |
Iscram 2023 |
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Volume |
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Issue |
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Pages |
909-919 |
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Keywords |
Social Networking; Event Localization; Twitter; Neural Networks; GAN, BiLSTM |
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Abstract |
In this paper, we develop a model with neural networks to localize events using microblogging data. Localization is the task of finding the location of an event and can be done by discovering event signatures in microblogging data. We use the deep learning methodology of Bi-directional Long Short-Term Memory (Bi-LSTM) to learn event signatures. We propose a methodology for labeling the Twitter date for use in Bi-LSTM However, there might not be enough data available to train the Bi-LSTM and learn the event signatures. Hence, the data is augmented using generative adversarial networks (GAN). Finally, we combine event signatures at different temporal and spatial granularity to improve the accuracy of event localization. We use microblogging data collected from Twitter to evaluate our model and compare it with other baseline methods. |
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Address |
Tokyo Institute of Technology |
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Publisher |
University of Nebraska at Omaha |
Place of Publication |
Omaha, USA |
Editor |
Jaziar Radianti; Ioannis Dokas; Nicolas Lalone; Deepak Khazanchi |
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Language |
English |
Summary Language |
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Original Title |
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Series Editor |
Hosssein Baharmand |
Series Title |
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Series Issue |
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Edition |
1 |
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Track |
AI for Crisis Management |
Expedition |
|
Conference |
|
|
|
Notes |
http://dx.doi.org/10.59297/UVZV1884 |
Approved |
no |
|
|
Call Number |
ISCRAM @ idladmin @ |
Serial |
2575 |
|
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Author |
Anna Kruspe; Jens Kersten; Friederike Klan |
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Title |
Detecting event-related tweets by example using few-shot models |
Type |
Conference Article |
|
Year |
2019 |
Publication |
Proceedings of the 16th International Conference on Information Systems for Crisis Response And Management |
Abbreviated Journal |
Iscram 2019 |
|
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Volume |
|
Issue |
|
Pages |
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Keywords |
Social media, Twitter, Relevance, Keywords, Hashtags, Few-shot models, One-class classification |
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Abstract |
Social media sources can be helpful in crisis situations, but discovering relevant messages is not trivial. Methods
have so far focused on universal detection models for all kinds of crises or for certain crisis types (e.g. floods).
Event-specific models could implement a more focused search area, but collecting data and training new models for
a crisis that is already in progress is costly and may take too much time for a prompt response. As a compromise,
manually collecting a small amount of example messages is feasible. Few-shot models can generalize to unseen
classes with such a small handful of examples, and do not need be trained anew for each event. We show how
these models can be used to detect crisis-relevant tweets during new events with just 10 to 100 examples and
counterexamples. We also propose a new type of few-shot model that does not require counterexamples. |
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Address |
German Aerospace Center (DLR), Germany |
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Corporate Author |
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Thesis |
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Publisher |
Iscram |
Place of Publication |
Valencia, Spain |
Editor |
Franco, Z.; González, J.J.; Canós, J.H. |
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Language |
English |
Summary Language |
English |
Original Title |
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Series Editor |
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Series Title |
|
Abbreviated Series Title |
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Series Volume |
|
Series Issue |
|
Edition |
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ISSN |
2411-3387 |
ISBN |
978-84-09-10498-7 |
Medium |
|
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Track |
T8- Social Media in Crises and Conflicts |
Expedition |
|
Conference |
16th International Conference on Information Systems for Crisis Response and Management (ISCRAM 2019) |
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Notes |
|
Approved |
no |
|
|
Call Number |
|
Serial |
1911 |
|
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Author |
Antone Evans Jr.; Yingyuan Yang; Sunshin Lee |
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Title |
Towards Predicting COVID-19 Trends: Feature Engineering on Social Media Responses |
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 |
792-807 |
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|
Keywords |
Big Data Analysis, Machine Learning, COVID-19, Twitter |
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Abstract |
During the course of this pandemic, the use of social media and virtual networks has been at an all-time high. Individuals have used social media to express their thoughts on matters related to this pandemic. It is difficult to predict current trends based on historic case data because trends are more connected to social activities which can lead to the spread of coronavirus. So, it's important for us to derive meaningful information from social media as it is widely used. Therefore, we grouped tweets by common keywords, found correlations between keywords and daily COVID-19 statistics and built predictive modeling. The features correlation analysis was very effective, so trends were predicted very well. A RMSE score of 0.0425504, MAE of 0.03295105 and RSQ of 0.5237014 in relation to daily deaths. In addition, we found a RMSE score of 0.07346836, MAE of 0.0491152 and RSQ 0.374529 in relation to daily cases. |
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Address |
University of Illinois Springfield; University of Illinois Springfield; University of Illinois Springfield |
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Corporate Author |
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Thesis |
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Publisher |
Virginia Tech |
Place of Publication |
Blacksburg, VA (USA) |
Editor |
Anouck Adrot; Rob Grace; Kathleen Moore; Christopher W. Zobel |
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Language |
English |
Summary Language |
English |
Original Title |
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Series Editor |
|
Series Title |
|
Abbreviated Series Title |
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Series Volume |
|
Series Issue |
|
Edition |
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ISSN |
978-1-949373-61-5 |
ISBN |
|
Medium |
|
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Track |
Social Media for Disaster Response and Resilience |
Expedition |
|
Conference |
18th International Conference on Information Systems for Crisis Response and Management |
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Notes |
aevan7@uis.edu |
Approved |
no |
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|
Call Number |
ISCRAM @ idladmin @ |
Serial |
2374 |
|
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Author |
Antonin Segault; Federico Tajariol; Ioan Roxin |
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Title |
#geiger : Radiation Monitoring Twitter Bots for Nuclear Post-Accident Situations |
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 |
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|
Volume |
|
Issue |
|
Pages |
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Keywords |
bots; long-term period; nuclear post-accident; radiations; Twitter |
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Abstract |
In the last decade, people have increasingly relied on social media platforms such as Twitter to share information on the response to a natural or a man-made disaster. This paper focuses on the aftermath of the Fukushima Daiichi nuclear disaster. Since the disaster, victims and volunteers have been sharing relevant information about radiation measurements by means of social media. The aim of this research is to explore the diffusion of information produced and shared by Twitter bots, to understand the degree of popularity of these sources and to check if these bots deliver original radiation measurements. |
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Address |
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Corporate Author |
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Thesis |
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Publisher |
University of Agder (UiA) |
Place of Publication |
Kristiansand, Norway |
Editor |
L. Palen; M. Buscher; T. Comes; A. Hughes |
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Language |
English |
Summary Language |
English |
Original Title |
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Series Editor |
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Series Title |
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Abbreviated Series Title |
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Series Volume |
|
Series Issue |
|
Edition |
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ISSN |
2411-3387 |
ISBN |
9788271177881 |
Medium |
|
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Track |
Social Media Studies |
Expedition |
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Conference |
ISCRAM 2015 Conference Proceedings ? 12th International Conference on Information Systems for Crisis Response and Management |
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Notes |
|
Approved |
yes |
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Call Number |
|
Serial |
1239 |
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Author |
Antonin Segault; Federico Tajariol; Yang Ishigaki; Ioan Roxin |
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Title |
#geiger 2: Developing Guidelines for Radiation Measurements Sharing on Social Media |
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 |
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Volume |
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Issue |
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Pages |
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Keywords |
Twitter; Nuclear Post-Accident; Radiation; Robots; Syntax |
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Abstract |
Radiation measurements are key information in post-nuclear accident situations. Automated Twitter accounts have been used to share the readings, but often in an incomplete way from the perspective of data sharing and risk communication between citizen and radiation experts. In this paper, we investigate the requirements for radiation measurements completeness, by analyzing the perceived usefulness of several metadata items that may go along the measurement itself. We carried out a benchmark of existing uses, and conducted a survey with both experts and lay citizens. We thus produced a set of guidelines regarding the metadata that should be used, and the way to publish it. |
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Address |
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Corporate Author |
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Thesis |
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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 |
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Language |
English |
Summary Language |
English |
Original Title |
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Series Editor |
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Series Title |
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Abbreviated Series Title |
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Series Volume |
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Series Issue |
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Edition |
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ISSN |
2411-3388 |
ISBN |
978-84-608-7984-9 |
Medium |
|
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Track |
Social Media Studies |
Expedition |
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Conference |
13th International Conference on Information Systems for Crisis Response and Management |
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Notes |
|
Approved |
no |
|
|
Call Number |
|
Serial |
1394 |
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Author |
Apoorva Chauhan; Amanda Hughes |
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Title |
COVID-19 Named Resources on Facebook, Twitter, and Reddit |
Type |
Conference Article |
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Year |
2021 |
Publication |
ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management |
Abbreviated Journal |
Iscram 2021 |
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Volume |
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Issue |
|
Pages |
679-690 |
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Keywords |
Crisis Named Resources, Facebook, Twitter, Reddit, COVID-19 |
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Abstract |
Crisis Named Resources (CNRs) are social media accounts and pages named after a crisis event. They are created soon after an event occurs. CNRs share a lot of information around an event and are followed by many. In this study, we identify CNRs created around COVID-19 on Facebook, Twitter, and Reddit. We analyze when these resources were created, why they were created, how they were received by members of the public, and who created them. We conclude by comparing CNRs created around COVID-19 with past crisis events and discuss how CNR owners attempt to manage content and combat misinformation. |
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Address |
University of Waterloo; Brigham Young University |
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Corporate Author |
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Thesis |
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Publisher |
Virginia Tech |
Place of Publication |
Blacksburg, VA (USA) |
Editor |
Anouck Adrot; Rob Grace; Kathleen Moore; Christopher W. Zobel |
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Language |
English |
Summary Language |
English |
Original Title |
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Series Editor |
|
Series Title |
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Abbreviated Series Title |
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Series Volume |
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Series Issue |
|
Edition |
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ISSN |
978-1-949373-61-5 |
ISBN |
|
Medium |
|
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Track |
Social Media for Disaster Response and Resilience |
Expedition |
|
Conference |
18th International Conference on Information Systems for Crisis Response and Management |
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Notes |
apoorva.chauhan@aggiemail.usu.edu |
Approved |
no |
|
|
Call Number |
ISCRAM @ idladmin @ |
Serial |
2364 |
|
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Author |
Axel Schulz; Tung Dang Thanh; Heiko Paulheim; Immanuel Schweizer |
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Title |
A fine-grained sentiment analysis approach for detecting crisis related microposts |
Type |
Conference Article |
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Year |
2013 |
Publication |
ISCRAM 2013 Conference Proceedings – 10th International Conference on Information Systems for Crisis Response and Management |
Abbreviated Journal |
ISCRAM 2013 |
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Volume |
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Issue |
|
Pages |
846-851 |
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Keywords |
Artificial intelligence; Information systems; Learning systems; Risk management; Social networking (online); Amount of information; Emergency management; Microposts; Real-time information; Sentiment analysis; Situational awareness; Systematic evaluation; Twitter; Data mining |
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Abstract |
Real-time information from microposts like Twitter is useful for applications in the crisis management domain. Currently, that potentially valuable information remains mostly unused by the command staff, mainly because the sheer amount of information cannot be handled efficiently. Sentiment analysis has been shown as an effective tool to detect microposts (such as tweets) that contribute to situational awareness. However, current approaches only focus on two or three emotion classes. But using only tweets with negative emotions for crisis management is not always sufficient. The amount of remaining information is still not manageable or most of the tweets are irrelevant. Thus, a more fine-grained differentiation is needed to identify relevant microposts. In this paper, we show the systematic evaluation of an approach for sentiment analysis on microposts that allows detecting seven emotion classes. A preliminary evaluation of our approach in a crisis related scenario demonstrates the applicability and usefulness. |
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Address |
Technische Universität Darmstadt, Germany; Universität Mannheim, Germany |
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Corporate Author |
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Thesis |
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Publisher |
Karlsruher Institut fur Technologie |
Place of Publication |
KIT; Baden-Baden |
Editor |
T. Comes, F. Fiedrich, S. Fortier, J. Geldermann and T. Müller |
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Language |
English |
Summary Language |
English |
Original Title |
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Series Editor |
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Series Title |
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Abbreviated Series Title |
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Series Volume |
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Series Issue |
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Edition |
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ISSN |
2411-3387 |
ISBN |
9783923704804 |
Medium |
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Track |
Social Media |
Expedition |
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Conference |
10th International ISCRAM Conference on Information Systems for Crisis Response and Management |
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Notes |
|
Approved |
no |
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|
Call Number |
|
Serial |
927 |
|
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Author |
Benjamin Herfort; João Porto De Albuquerque; Svend-Jonas Schelhorn; Alexander Zipf |
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Title |
Does the spatiotemporal distribution of tweets match the spatiotemporal distribution of flood phenomena? A study about the River Elbe Flood in June 2013 |
Type |
Conference Article |
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Year |
2014 |
Publication |
ISCRAM 2014 Conference Proceedings – 11th International Conference on Information Systems for Crisis Response and Management |
Abbreviated Journal |
ISCRAM 2014 |
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Volume |
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Issue |
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Pages |
747-751 |
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Keywords |
Catchments; Data mining; Information systems; Social networking (online); Spatial distribution; Water levels; Crisis management; Digital elevation model; Geographical features; Situational awareness; Social media; Social media platforms; Spatiotemporal distributions; Twitter; Floods |
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Abstract |
In this paper we present a new approach to enhance information extraction from social media that relies upon the geographical relations between twitter data and flood phenomena. We use specific geographical features like hydrological data and digital elevation models to analyze the spatiotemporal distribution of georeferenced twitter messages. This approach is applied to examine the River Elbe Flood in Germany in June 2013. Although recent research has shown that social media platforms like Twitter can be complementary information sources for achieving situation awareness, previous work is mostly concentrated on the classification and analysis of tweets without resorting to existing data related to the disaster, e.g. catchment borders or sensor data about river levels. Our results show that our approach based on geographical relations can help to manage the high volume and velocity of social media messages and thus can be valuable for both crisis response and preventive flood monitoring. |
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Address |
GIScience Department, Heidelberg University, Germany; Dept. of Computer Systems/ICMC, University of Sao Paulo, Brazil |
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Corporate Author |
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Thesis |
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Publisher |
The Pennsylvania State University |
Place of Publication |
University Park, PA |
Editor |
S.R. Hiltz, M.S. Pfaff, L. Plotnick, and P.C. Shih. |
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Language |
English |
Summary Language |
English |
Original Title |
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Series Editor |
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Series Title |
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Abbreviated Series Title |
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Series Volume |
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Series Issue |
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Edition |
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ISSN |
2411-3387 |
ISBN |
9780692211946 |
Medium |
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Track |
Social Media in Crisis Response and Management |
Expedition |
|
Conference |
11th International ISCRAM Conference on Information Systems for Crisis Response and Management |
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Notes |
|
Approved |
no |
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Call Number |
|
Serial |
572 |
|
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Author |
Chauhan, A. |
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Title |
Humor-Based COVID-19 Twitter Accounts |
Type |
Conference Article |
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Year |
2023 |
Publication |
Proceedings of the 20th International ISCRAM Conference |
Abbreviated Journal |
Iscram 2023 |
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Volume |
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Issue |
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Pages |
417-427 |
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Keywords |
COVID-19; Twitter; Humor; Crisis Named Resources |
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Abstract |
Crisis Named Resources (or CNRs) are social media pages and accounts named after a crisis event. Using the COVID-19 Pandemic as a case study, we identified and examined the role of CNRs that shared humor on Twitter. Our analyses showed that humor-based CNRs shared virus-related rumors, stigma, safety measures, opinions, sarcasm, and news updates. These resources also shared the overall anger and frustration over the year 2020. We conclude by discussing the critical role of humor based CNRs in crisis response. |
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Address |
Concordia University of Edmonton |
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Corporate Author |
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Thesis |
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Publisher |
University of Nebraska at Omaha |
Place of Publication |
Omaha, USA |
Editor |
Jaziar Radianti; Ioannis Dokas; Nicolas Lalone; Deepak Khazanchi |
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Language |
English |
Summary Language |
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Original Title |
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Series Editor |
Hosssein Baharmand |
Series Title |
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Abbreviated Series Title |
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Series Volume |
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Series Issue |
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Edition |
1 |
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ISSN |
|
ISBN |
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Medium |
|
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Track |
Social Media for Crisis Management |
Expedition |
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Conference |
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Notes |
http://dx.doi.org/10.59297/YHDI4576 |
Approved |
no |
|
|
Call Number |
ISCRAM @ idladmin @ |
Serial |
2536 |
|
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Author |
Cody Buntain; Richard Mccreadie; Ian Soboroff |
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Title |
Incident Streams 2021 Off the Deep End: Deeper Annotations and Evaluations in Twitter |
Type |
Conference Article |
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Year |
2022 |
Publication |
ISCRAM 2022 Conference Proceedings – 19th International Conference on Information Systems for Crisis Response and Management |
Abbreviated Journal |
Iscram 2022 |
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Volume |
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Issue |
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Pages |
584-604 |
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Keywords |
Emergency Management; Crisis Informatics; Twitter; Categorization; Priorization; Multi-Modal; Public Safety; PSCR; TREC |
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Abstract |
This paper summarizes the final year of the four-year Text REtrieval Conference Incident Streams track (TREC-IS), which has produced a large dataset comprising 136,263 annotated tweets, spanning 98 crisis events. Goals of this final year were twofold: 1) to add new categories for assessing messages, with a focus on characterizing the audience, author, and images associated with these messages, and 2) to enlarge the TREC-IS dataset with new events, with an emphasis of deeper pools for sampling. Beyond these two goals, TREC-IS has nearly doubled the number of annotated messages per event for the 26 crises introduced in 2021 and has released a new parallel dataset of 312,546 images associated with crisis content – with 7,297 tweets having annotations about their embedded images. Our analyses of this new crisis data yields new insights about the context of a tweet; e.g., messages intended for a local audience and those that contain images of weather forecasts and infographics have higher than average assessments of priority but are relatively rare. Tweets containing images, however, have higher perceived priorities than tweets without images. Moving to deeper pools, while tending to lower classification performance, also does not generally impact performance rankings or alter distributions of information-types. We end this paper with a discussion of these datasets, analyses, their implications, and how they contribute both new data and insights to the broader crisis informatics community. |
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Address |
University of Maryland, College Park (UMD); University of Glasgow; National Institute of Standards and Technology (NIST) |
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Corporate Author |
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Thesis |
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Publisher |
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Place of Publication |
Tarbes, France |
Editor |
Rob Grace; Hossein Baharmand |
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Language |
English |
Summary Language |
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Original Title |
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Series Editor |
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Series Title |
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Abbreviated Series Title |
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Series Volume |
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Series Issue |
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Edition |
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ISSN |
2411-3387 |
ISBN |
978-82-8427-099-9 |
Medium |
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Track |
Social Media for Crisis Management |
Expedition |
|
Conference |
|
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Notes |
|
Approved |
no |
|
|
Call Number |
ISCRAM @ idladmin @ |
Serial |
2441 |
|
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Author |
Cody Buntain; Richard Mccreadie; Ian Soboroff |
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Title |
Incident Streams 2020: TRECIS in the Time of COVID-19 |
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 |
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Volume |
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Issue |
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Pages |
621-639 |
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Keywords |
Emergency Management, Crisis Informatics, Twitter, Categorization, Prioritization, COVID-19 |
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Abstract |
Between 2018 and 2019, the Incident Streams track (TREC-IS) has developed standard approaches for classifying the types and criticality of information shared in online social spaces during crises, but the introduction of SARS-CoV-2 has shifted the landscape of online crises substantially. While prior editions of TREC-IS have lacked data on large-scale public-health emergencies as these events are exceedingly rare, COVID-19 has introduced an over-abundance of potential data, and significant open questions remain about how existing approaches to crisis informatics and datasets built on other emergencies adapt to this new context. This paper describes how the 2020 edition of TREC-IS has addressed these dual issues by introducing a new COVID-19-specific task for evaluating generalization of existing COVID-19 annotation and system performance to this new context, applied to 11 regions across the globe. TREC-IS has also continued expanding its set of target crises, adding 29 new events and expanding the collection of event types to include explosions, fires, and general storms, making for a total of 9 event types in addition to the new COVID-19 events. Across these events, TREC-IS has made available 478,110 COVID-related messages and 282,444 crisis-related messages for participant systems to analyze, of which 14,835 COVID-related and 19,784 crisis-related messages have been manually annotated. Analyses of these new datasets and participant systems demonstrate first that both the distributions of information type and priority of information vary between general crises and COVID-19-related discussion. Secondly, despite these differences, results suggest leveraging general crisis data in the COVID-19 context improves performance over baselines. Using these results, we provide guidance on which information types appear most consistent between general crises and COVID-19. |
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Address |
New Jersey Institute of Technology; University of Glasgow; National Institute of Standards and Technology |
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Corporate Author |
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Thesis |
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Publisher |
Virginia Tech |
Place of Publication |
Blacksburg, VA (USA) |
Editor |
Anouck Adrot; Rob Grace; Kathleen Moore; Christopher W. Zobel |
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Language |
English |
Summary Language |
English |
Original Title |
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Series Editor |
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Series Title |
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Abbreviated Series Title |
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Series Volume |
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Series Issue |
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Edition |
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ISSN |
978-1-949373-61-5 |
ISBN |
|
Medium |
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Track |
Social Media for Disaster Response and Resilience |
Expedition |
|
Conference |
18th International Conference on Information Systems for Crisis Response and Management |
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|
Notes |
cbuntain@njit.edu |
Approved |
no |
|
|
Call Number |
ISCRAM @ idladmin @ |
Serial |
2360 |
|
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Author |
Daniel Link; Bernd Hellingrath; Tom De Groeve |
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Title |
Twitter integration and content moderation in GDACSmobile |
Type |
Conference Article |
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Year |
2013 |
Publication |
ISCRAM 2013 Conference Proceedings – 10th International Conference on Information Systems for Crisis Response and Management |
Abbreviated Journal |
ISCRAM 2013 |
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Volume |
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Issue |
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Pages |
67-71 |
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Keywords |
Disaster prevention; Disasters; Mobile devices; Social networking (online); Content moderation; Coordination; Gdacs; GDACSmobile; Needs Assessment; Social media; Twitter; Information management |
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Abstract |
Recent years have shown that mobile devices and Twitter can play a significant role in providing real-time data from disaster-affected areas to disaster managers. Against this background we present a workflow for Twitter integration into a disaster management information system, and a concept for content moderation that can increase the quality of disseminated information. |
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Address |
Dept. of Information Systems and Logistics, European Research Center for Information Systems (ERCIS), University of Münster, Germany; Joint Research Centre of European Commission, Italy |
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Corporate Author |
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Thesis |
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Publisher |
Karlsruher Institut fur Technologie |
Place of Publication |
KIT; Baden-Baden |
Editor |
T. Comes, F. Fiedrich, S. Fortier, J. Geldermann and T. Müller |
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|
Language |
English |
Summary Language |
English |
Original Title |
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Series Editor |
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Series Title |
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Abbreviated Series Title |
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Series Volume |
|
Series Issue |
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Edition |
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ISSN |
2411-3387 |
ISBN |
9783923704804 |
Medium |
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Track |
Coordination and Collaboration |
Expedition |
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Conference |
10th International ISCRAM Conference on Information Systems for Crisis Response and Management |
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Notes |
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Approved |
no |
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Call Number |
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Serial |
709 |
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Author |
Dario Salza; Edoardo Arnaudo; Giacomo Blanco; Claudio Rossi |
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Title |
A 'Glocal' Approach for Real-time Emergency Event Detection in Twitter |
Type |
Conference Article |
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Year |
2022 |
Publication |
ISCRAM 2022 Conference Proceedings – 19th International Conference on Information Systems for Crisis Response and Management |
Abbreviated Journal |
Iscram 2022 |
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Volume |
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Issue |
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Pages |
570-583 |
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Keywords |
Emergency; Event Detection; Social Media; Twitter; Incremental Clustering |
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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. |
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Address |
LINKS Foundation; LINKS Foundation; LINKS Foundation; LINKS Foundation |
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Corporate Author |
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Thesis |
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Publisher |
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Place of Publication |
Tarbes, France |
Editor |
Rob Grace; Hossein Baharmand |
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Language |
English |
Summary Language |
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Original Title |
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Series Editor |
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Series Title |
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Abbreviated Series Title |
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Series Volume |
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Series Issue |
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Edition |
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ISSN |
2411-3387 |
ISBN |
978-82-8427-099-9 |
Medium |
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Track |
Social Media for Crisis Management |
Expedition |
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Conference |
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Notes |
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Approved |
no |
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Call Number |
ISCRAM @ idladmin @ |
Serial |
2440 |
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Author |
David F. Merrick; Tom Duffy |
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Title |
Utilizing community volunteered information to enhance disaster situational awareness |
Type |
Conference Article |
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Year |
2013 |
Publication |
ISCRAM 2013 Conference Proceedings – 10th International Conference on Information Systems for Crisis Response and Management |
Abbreviated Journal |
ISCRAM 2013 |
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Volume |
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Issue |
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Pages |
858-862 |
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Keywords |
Civil defense; Disasters; Information systems; Risk management; Social networking (online); Community volunteered information; Crowd sourcing; Facebook; Situational awareness; Social media; Twitter; Emergency services |
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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. |
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Address |
Florida State University, United States |
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Corporate Author |
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Thesis |
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Publisher |
Karlsruher Institut fur Technologie |
Place of Publication |
KIT; Baden-Baden |
Editor |
T. Comes, F. Fiedrich, S. Fortier, J. Geldermann and T. Müller |
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Language |
English |
Summary Language |
English |
Original Title |
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Series Editor |
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Series Title |
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Abbreviated Series Title |
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Series Volume |
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Series Issue |
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Edition |
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ISSN |
2411-3387 |
ISBN |
9783923704804 |
Medium |
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Track |
Social Media |
Expedition |
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Conference |
10th International ISCRAM Conference on Information Systems for Crisis Response and Management |
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Notes |
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Approved |
no |
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Call Number |
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Serial |
767 |
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Author |
Diana Fischer; Carsten Schwemmer; Kai Fischbach |
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Title |
Terror Management and Twitter: The Case of the 2016 Berlin Terrorist Attack |
Type |
Conference Article |
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Year |
2018 |
Publication |
ISCRAM 2018 Conference Proceedings – 15th International Conference on Information Systems for Crisis Response and Management |
Abbreviated Journal |
Iscram 2018 |
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Volume |
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Issue |
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Pages |
459-468 |
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Keywords |
Terrorist attacks, social networking sites, social media, Twitter, topic modeling, terror management, sense-making |
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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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Publisher |
Rochester Institute of Technology |
Place of Publication |
Rochester, NY (USA) |
Editor |
Kees Boersma; Brian Tomaszeski |
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Language |
English |
Summary Language |
English |
Original Title |
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Series Editor |
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Series Title |
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Abbreviated Series Title |
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Series Volume |
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Series Issue |
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Edition |
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ISSN |
2411-3387 |
ISBN |
978-0-692-12760-5 |
Medium |
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Track |
Social Media Studies |
Expedition |
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Conference |
ISCRAM 2018 Conference Proceedings - 15th International Conference on Information Systems for Crisis Response and Management |
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Notes |
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Approved |
no |
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Call Number |
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Serial |
2123 |
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