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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 | Guillermo Romera Rodriguez | ||||
| Title | Parler, Capitol Riots, Alt-Right and Radicalization in Social Media | Type | Conference Article | ||
| Year | 2023 | Publication | Proceedings of the ISCRAM Asia Pacific Conference 2022 | Abbreviated Journal | Proc. ISCRAM AP 2022 |
| Volume | Issue | Pages | 268-277 | ||
| Keywords |
Social Media; Parler; Sentiment Analysis; Alt-Right | ||||
| Abstract | Social media platforms have risen in popularity since their inception. These platforms have since then come to be at the forefront of controversies, from being accused of election interference to, more recently, disseminating fake news and campaigns to sway political behavior. One such episode took place on January 6 when a group of individuals stormed the United States Capitol, and the social media platform Parler came under scrutiny. The platform was accused of being a place for right-wing extremists and Trump supporters who claimed the 2020 election was fraudulent. Initial reports suggested these individuals used Parler to organize and call others to action. This paper explores the feasibility of using social media to detect alt-right radicalization and examines its possible relation to the Capitol Insurrection and Parler. Moreover, we examine if those events could have been detected and averted through the investigation of the platform. | ||||
| Address | Pennsylvania State University; Pennsylvania State University; Pennsylvania State University | ||||
| 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 | 2500 | ||
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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. | ||||
| Address | |||||
| Corporate Author | Thesis | ||||
| Publisher | Federal University of Rio de Janeiro | Place of Publication | Rio de Janeiro, Brasil | Editor | A. Tapia; P. Antunes; V.A. Bañuls; K. Moore; J. Porto |
| Language | English | Summary Language | English | Original Title | |
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | 2411-3388 | ISBN | 978-84-608-7984-9 | Medium | |
| Track | Social Media Studies | Expedition | Conference | 13th International Conference on Information Systems for Crisis Response and Management | |
| Notes | Approved | no | |||
| Call Number | ISCRAM @ idladmin @ | Serial | 1391 | ||
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| Author | Tom Wilson; Stephanie A. Stanek; Emma S. Spiro; Kate Starbird | ||||
| Title | Language Limitations in Rumor Research? Comparing French and English Tweets Sent During the 2015 Paris Attacks | 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 | 546-553 | ||
| Keywords |
social media; rumoring, language; crisis informatics; information diffusion | ||||
| Abstract | The ubiquity of social media facilitates widespread participation in crises. As individuals converge online to understand a developing situation, rumors can emerge. Little is currently known about how online rumoring behavior varies by language. Exploring a rumor from the 2015 Paris Attacks, we investigate Twitter rumoring behaviors across two languages: French, the primary language of the affected population; and English, the dominant language of Internet communication. We utilize mixed methods to qualitatively code and quantitatively analyze rumoring behaviors across French and English language tweets. Most interestingly, temporal engagement in the rumor varies across languages, but proportions of tweets affirming and denying a rumor are very similar. Analyzing tweet deletions and retweet counts, we find slight (but not significant) differences between languages. This work offers insight into potential limitations of previous research of online rumoring, which often focused exclusively on English language content, and demonstrates the importance of considering language in future work. | ||||
| Address | Human Centered Design and Engineering, University of Washington ; Information School, Department of Sociology, University of Washington | ||||
| 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 | 2042 | |||
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| Author | Yuhong Li; Christopher Zobel | ||||
| Title | Small Businesses and Social Media Usage in the 2013 Colorado Floods | 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; Small Business; Recovery; Disaster | ||||
| Abstract | The recovery of small businesses from a disaster is critical to community recovery. Such businesses can be extremely vulnerable to disasters, particularly because they often occupy a single location and have a localized customer base. Although social media is an effective platform for information dissemination, and has been extensively used in a disaster context, the way in which small businesses use social media in this context, and the effectiveness of those efforts, are still not well understood. With this in mind, this paper uses the 2013 floods along the Front Range in Colorado as a case study to help improve our understanding of how small businesses use social media in disaster situations. Characterizing the organizations' behavior involves using both qualitative and quantitative approaches, and the paper focuses on an initial qualitative analysis. | ||||
| Address | |||||
| Corporate Author | Thesis | ||||
| Publisher | Federal University of Rio de Janeiro | Place of Publication | Rio de Janeiro, Brasil | Editor | A. Tapia; P. Antunes; V.A. Bañuls; K. Moore; J. Porto |
| Language | English | Summary Language | English | Original Title | |
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | 2411-3388 | ISBN | 978-84-608-7984-9 | Medium | |
| Track | Social Media Studies | Expedition | Conference | 13th International Conference on Information Systems for Crisis Response and Management | |
| Notes | Approved | no | |||
| Call Number | Serial | 1392 | |||
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| Author | St. Denis, L.A.; Hughes, A.L. | ||||
| Title | Use of Statistics in Disaster by Local Individuals: An Examination of Tweets during COVID-19 | Type | Conference Article | ||
| Year | 2023 | Publication | Proceedings of the 20th International ISCRAM Conference | Abbreviated Journal | Iscram 2023 |
| Volume | Issue | Pages | 449-458 | ||
| Keywords |
Social Media; Statistics; COVID-19; Pandemic | ||||
| Abstract | We report on how individuals local to the US state of Colorado used statistics in tweets to make sense of the early stages of the COVID-19 pandemic. Tweets provided insight into how people interpreted statistical data, sometimes incorrectly, which has implications for crisis responders tasked with understanding public perceptions and providing accurate information. With widespread concerns about the accuracy and quality of online information, we show how monitoring public reactions to and uses of statistics on social media is important for improving crisis communication. Findings suggest that statistics can be a powerful tool for making sense of a crisis and coping with the stress and uncertainty of a global, rapidly evolving event like the COVID-19 pandemic. We conclude with broader implications for how crisis responders might improve their communications around statistics to the public, and suggestions for how this research might be expanded to look at other types of disasters. | ||||
| Address | CIRES, Earth Lab University of Colorado; Crisis Informatics Lab Brigham Young 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/KBIJ7756 | Approved | no | ||
| Call Number | ISCRAM @ idladmin @ | Serial | 2539 | ||
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| Author | Laura Petersen; Laure Fallou; Paul Reilly; Elisa Serafinelli | ||||
| Title | Public expectations of social media use by critical infrastructure operators in crisis communication | 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 | 522-531 | ||
| Keywords |
Social media; traditional media; crisis communication; critical infrastructure operators; public expectations | ||||
| Abstract | Previous research into the role of social media in crisis communication has tended to focus on how sites such as Twitter are used by emergency managers rather than other key stakeholders, such as critical infrastructure (CI) operators. This paper adds to this emergent field by empirically investigating public expectations of informatio provided by CI operators during crisis situations. It does so by drawing on key themes that emerged from a review of the literature on public expectations of disaster related information shared via social media, and presenting the results of an online questionnaire-based study of disaster-vulnerable communities in France, Norway, Portugal and Sweden. Results indicate that members of the public expect CI operators to provide disaster related information via traditional and social media and to respond to their queries on social media. CI operators should avail of the opportunities provided by social media to provide real-time information to disaster affected communities. | ||||
| Address | European-Mediterranean Seismological Centre (EMSC); University of Sheffield | ||||
| 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 | 2040 | |||
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| Author | Muhammad Imran; Prasenjit Mitra; Jaideep Srivastava | ||||
| Title | Cross-Language Domain Adaptation for Classifying Crisis-Related Short Messages | 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; Tweets Classification; Domain Adaptation | ||||
| Abstract | Rapid crisis response requires real-time analysis of messages. After a disaster happens, volunteers attempt to classify tweets to determine needs, e.g., supplies, infrastructure damage, etc. Given labeled data, supervised machine learning can help classify these messages. Scarcity of labeled data causes poor performance in machine training. Can we reuse old tweets to train classifiers? How can we choose labeled tweets for training? Specifically, we study the usefulness of labeled data of past events. Do labeled tweets in different language help? We observe the performance of our classifiers trained using different combinations of training sets obtained from past disasters. We perform extensive experimentation on real crisis datasets and show that the past labels are useful when both source and target events are of the same type (e.g. both earthquakes). For similar languages (e.g., Italian and Spanish), cross-language domain adaptation was useful, however, when for different languages (e.g., Italian and English), the performance decreased. | ||||
| Address | |||||
| Corporate Author | Thesis | ||||
| Publisher | Federal University of Rio de Janeiro | Place of Publication | Rio de Janeiro, Brasil | Editor | A. Tapia; P. Antunes; V.A. Bañuls; K. Moore; J. Porto |
| Language | English | Summary Language | English | Original Title | |
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | 2411-3388 | ISBN | 978-84-608-7984-9 | Medium | |
| Track | Social Media Studies | Expedition | Conference | 13th International Conference on Information Systems for Crisis Response and Management | |
| Notes | Approved | no | |||
| Call Number | Serial | 1396 | |||
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| Author | Asmelash Teka Hadgu; Sallam Abualhaija; Claudia Niederée | ||||
| Title | Real-time Adaptive Crawler for Tracking Unfolding Events on Twitter | 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 search, real-time adaptive search, event tracking, crsis communication | ||||
| Abstract | When a major event such as a crisis situation occurs, people post messages on social media sites such as Twitter, in order to exchange information or to share emotions. These posts can provide useful information to raise situation awareness and support decision making, e.g., by aid organizations. In this paper, we propose a novel method for social media crawling, which exploits a Bayesian inference framework to keep track of keyword changes over time and uses a counter-stream to gauge the inclusion of noise and irrelevant information. In addition, we present a framework to evaluate real-time adaptive social search algorithms in a reproducible manner, which relies on a semi-automated approach for ground-truth construction. We show that our method outperforms previous methods for very large scale events. |
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| Address | L3S Research Center, Leibniz universität Hannover, Germany;Interdisciplinary Centre for Security, Reliability and Trust | ||||
| 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 | 1985 | |||
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| Author | Stephen Kelly; Xiubo Zhang; Khurshid Ahmad | ||||
| Title | Mining Multimodal Information on Social Media for Increased Situational Awareness | Type | Conference Article | ||
| Year | 2017 | Publication | Proceedings of the 14th International Conference on Information Systems for Crisis Response And Management | Abbreviated Journal | Iscram 2017 |
| Volume | Issue | Pages | 613-622 | ||
| Keywords |
Spatio-temporal; Social media analysis; Multimodal analysis; Geolocation | ||||
| Abstract | Social media platforms have become a source of high volume, real-time information describing significant events in a timely fashion. In this paper we describe a system for the real-time extraction of information from text and image content in Twitter messages and combine the spatio-temporal metadata of the messages to filter the data stream for emergency events and visualize the output on an interactive map. Twitter messages for a geographic region are monitored for flooding events by analysing the text content and images posted. Events detected are compared with a ground truth to see if information in social media correlates with actual events. We propose an Intrusion Index as part of this prototype to facilitate ethical harvesting of data. A map layer is created by the prototype system that visualises the analysis and filtered Twitter messages by geolocation. | ||||
| Address | rinity College Dublin, Ireland | ||||
| Corporate Author | Thesis | ||||
| Publisher | Iscram | Place of Publication | Albi, France | Editor | Tina Comes, F.B., Chihab Hanachi, Matthieu Lauras, Aurélie Montarnal, eds |
| Language | English | Summary Language | English | Original Title | |
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | 2411-3387 | ISBN | Medium | ||
| Track | Social Media Studies | Expedition | Conference | 14th International Conference on Information Systems for Crisis Response And Management | |
| Notes | Approved | no | |||
| Call Number | Serial | 2049 | |||
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| Author | Sophie Gerstmann; Hans Betke; Stefan Sackmann | ||||
| Title | Towards Automated Individual Communication for Coordination of Spontaneous Volunteers | 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 |
Spontaneous volunteers, chatbot, social media, system architecture | ||||
| Abstract | In recent years, spontaneous volunteers often turned out to be a critical factor to overcome disaster situations and avoid further damages to life and assets. These Volunteers coordinate their activities using social media and mobile devices but are not integrated in usual command and control structures of disaster responders. The lack of professional disaster response knowledge leads to a waste of potential workforce or even dangerous situations for the volunteers. In this paper, a novel approach for a centralized coordination of spontaneous volunteers through disaster response professionals while using popular communication channels esp. messaging services (e.g. Facebook Messenger, WhatsApp) is presented. The architecture of a volunteer coordination system focusing on automated multi-channel communication is shown and the possibilities of a universal chatbot for individual assignment and scheduling of volunteers are discussed. The paper also provides first insights in a demonstrator system as a practical solution. |
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| Address | Martin-Luther University Halle-Wittenberg, Germany | ||||
| 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 | 1965 | |||
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| Author | Nils Bourgon; Benamara Farah; Alda Mari; Véronique Moriceau; Gaetan Chevalier; Laurent Leygue; Yasmine Djadda | ||||
| Title | Are Sudden Crises Making me Collapse? Measuring Transfer Learning Performances on Urgency Detection | 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 | 701-709 | ||
| Keywords |
Sudden crises; Transfer learning; Few-shot learning; Zero-shot learning; Social media content | ||||
| Abstract | This paper aims at measuring transfer learning performances across different types of crises related to sudden or unexpected events (like earthquakes, terror attacks, explosions, technological incidents) that cannot be foreseen by emergency services and on the occurrence of which they have virtually no control. Although sudden crises are present in most existing crisis datasets, as far as we are aware, no one studied their impact on classifiers performances when evaluated in an out-of-type scenario in which models are tested on a particular type of crisis unseen during training. Our contribution is threefold: (1) A new dataset of about 3,800 French tweets related to four sudden events that occurred in France annotated for both relatedness (i.e., useful vs. not useful for emergency responders) and urgency (i.e., not useful vs. urgent vs. not urgent), (2) A set of monotask and multitask zero-shot learning experiments to transfer knowledge across events and types, and finally, (3) Experiments involving few-shot learning to measure the amount of sudden events instances needed during training to guarantee good performances. When compared to a cross-event setting, our preliminary results are encouraging and show that transfer from predictable ecological crisis to sudden events is feasible and constitutes a first step towards real-time crisis management systems from social media content. | ||||
| Address | IRIT, Université de Toulouse, CNRS, Toulouse INP, UT3; IRIT, Université de Toulouse, CNRS, Toulouse INP, UT3; IJN, CNRS/ENS/EHESS PSL University; IRIT, Université de Toulouse, CNRS, Toulouse INP, UT3; DGSCGC SDAIRS; DGSCGC SDAIRS | ||||
| 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 | 2449 | ||
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| Author | Hannah Van Wyk; Kate Starbird | ||||
| Title | Analyzing Social Media Data to Understand How Disaster-Affected Individuals Adapt to Disaster-Related Telecommunications Disruptions | 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 | 704-717 | ||
| Keywords |
Telecommunications, Adaptations, Social Media, Cellular Phone Service, Wi-Fi Access. | ||||
| Abstract | Information is a critical need during disasters such as hurricanes. Increasingly, people are relying upon cellular and internet-based technology to communicate that information--modalities that are acutely vulnerable to the disruptions to telecommunication infrastructure that are common during disasters. Focusing on Hurricane Maria (2017) and its long-term impacts on Puerto Rico, this research examines how people affected by severe and sustained disruptions to telecommunications services adapt to those disruptions. Leveraging social media trace data as a window into the real-time activities of people who were actively adapting, we use a primarily qualitative approach to identify and characterize how people changed their telecommunications practices and routines--and especially how they changed their locations--to access Wi-Fi and cellular service in the weeks and months after the hurricane. These findings have implications for researchers seeking to better understand human responses to disasters and responders seeking to identify strategies to support affected populations. | ||||
| Address | University of Washington; University of Washington | ||||
| 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 | 978-1-949373-27-64 | ISBN | 2411-3450 | Medium | |
| Track | Social Media for Disaster Response and Resilie | Expedition | Conference | 17th International Conference on Information Systems for Crisis Response and Management | |
| Notes | hcvw@uw.edu | Approved | no | ||
| Call Number | Serial | 2265 | |||
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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. | ||||
| 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 | 2123 | |||
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| Author | Valentin Barriere; Guillaume Jacquet | ||||
| Title | How does a Pre-Trained Transformer Integrate Contextual Keywords? Application to Humanitarian Computing | 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 | 766-771 | ||
| Keywords |
Transformers, Contextual keywords, Humanitarian Computing, Tweets analysis | ||||
| Abstract | In a classification task, dealing with text snippets and metadata usually requires to deal with multimodal approaches. When those metadata are textual, it is tempting to use them intrinsically with a pre-trained transformer, in order to leverage the semantic information encoded inside the model. This paper describes how to improve a humanitarian classification task by adding the crisis event type to each tweet to be classified. Based on additional experiments of the model weights and behavior, it identifies how the proposed neural network approach is partially over-fitting the particularities of the Crisis Benchmark, to better highlight how the model is still undoubtedly learning to use and take advantage of the metadata's textual semantics. | ||||
| Address | European Commission's Joint Research Center; European Commission's Joint Research Center | ||||
| Corporate Author | Thesis | ||||
| Publisher | Virginia Tech | Place of Publication | Blacksburg, VA (USA) | Editor | Anouck Adrot; Rob Grace; Kathleen Moore; Christopher W. Zobel |
| Language | English | Summary Language | English | Original Title | |
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | 978-1-949373-61-5 | ISBN | Medium | ||
| Track | Social Media for Disaster Response and Resilience | Expedition | Conference | 18th International Conference on Information Systems for Crisis Response and Management | |
| Notes | valbarrierepro@gmail.com | Approved | no | ||
| Call Number | ISCRAM @ idladmin @ | Serial | 2371 | ||
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| Author | Reza Mazloom; HongMin Li; Doina Caragea; Muhammad Imran; Cornelia Caragea | ||||
| Title | Classification of Twitter Disaster Data Using a Hybrid Feature-Instance Adaptation Approach | 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 | 727-735 | ||
| Keywords |
Tweet classification, Domain adaptation, Matrix factorization, k-Nearest Neighbors, Disaster response | ||||
| Abstract | Huge amounts of data that are generated on social media during emergency situations are regarded as troves of critical information. The use of supervised machine learning techniques in the early stages of a disaster is challenged by the lack of labeled data for that particular disaster. Furthermore, supervised models trained on labeled data from a prior disaster may not produce accurate results, given the inherent variation between the current and the prior disasters. To address the challenges posed by the lack of labeled data for a target disaster, we propose to use a hybrid feature-instance adaptation approach based on matrix factorization and the k nearest neighbors algorithm, respectively. The proposed hybrid adaptation approach is used to select a subset of the source disaster data that is representative for the target disaster. The selected subset is subsequently used to learn accurate Naive Bayes classifiers for the target disaster. | ||||
| 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 | 2146 | |||
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| Author | Nilani Algiriyage; Rangana Sampath; Raj Prasanna; Kristin Stock; Emma Hudson-Doyle; David Johnston | ||||
| Title | Identifying Disaster-related Tweets: A Large-Scale Detection Model Comparison | 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 | 731-743 | ||
| Keywords |
Tweet Classification, Machine Learning, Deep Learning, Disasters | ||||
| Abstract | Social media applications such as Twitter and Facebook are fast becoming a key instrument in gaining situational awareness (understanding the bigger picture of the situation) during disasters. This has provided multiple opportunities to gather relevant information in a timely manner to improve disaster response. In recent years, identifying crisis-related social media posts is analysed as an automatic task using machine learning (ML) or deep learning (DL) techniques. However, such supervised learning algorithms require labelled training data in the early hours of a crisis. Recently, multiple manually labelled disaster-related open-source twitter datasets have been released. In this work, we create a large dataset with 186,718 tweets by combining a number of such datasets and evaluate the performance of multiple ML and DL algorithms in classifying disaster-related tweets in three settings, namely ``in-disaster'', ``out-disaster'' and ``cross-disaster''. Our results show that the Bidirectional LSTM model with Word2Vec embeddings performs well for the tweet classification task in all three settings. We also make available the preprocessing steps and trained weights for future research. | ||||
| Address | Massey University; Massey University; Massey University; Massey University; Joint Centre for Disaster Research, Massey University; Joint Center of Disaster Research, Massey University Wellington | ||||
| Corporate Author | Thesis | ||||
| Publisher | Virginia Tech | Place of Publication | Blacksburg, VA (USA) | Editor | Anouck Adrot; Rob Grace; Kathleen Moore; Christopher W. Zobel |
| Language | English | Summary Language | English | Original Title | |
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | 978-1-949373-61-5 | ISBN | Medium | ||
| Track | Social Media for Disaster Response and Resilience | Expedition | Conference | 18th International Conference on Information Systems for Crisis Response and Management | |
| Notes | rangika.nilani@gmail.com | Approved | no | ||
| Call Number | ISCRAM @ idladmin @ | Serial | 2368 | ||
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| Author | Matti Wiegmann; Jens Kersten; Friederike Klan; Martin Potthast; Benno Stein | ||||
| Title | Analysis of Detection Models for Disaster-Related Tweets | 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 | 872-880 | ||
| Keywords |
Tweet Filtering; Crisis Management; Evaluation Framework | ||||
| Abstract | Social media is perceived as a rich resource for disaster management and relief efforts, but the high class imbalance between disaster-related and non-disaster-related messages challenges a reliable detection. We analyze and compare the effectiveness of three state-of-the-art machine learning models for detecting disaster-related tweets. In this regard we introduce the Disaster Tweet Corpus~2020, an extended compilation of existing resources, which comprises a total of 123,166 tweets from 46~disasters covering 9~disaster types. Our findings from a large experiments series include: detection models work equally well over a broad range of disaster types when being trained for the respective type, a domain transfer across disaster types leads to unacceptable performance drops, or, similarly, type-agnostic classification models behave more robust at a lower effectiveness level. Altogether, the average misclassification rate of~3,8\% on performance-optimized detection models indicates effective classification knowledge but comes at the price of insufficient generalizability. | ||||
| Address | Bauhaus-Universit\“at Weimar German Aerospace Center (DLR); German Aerospace Center (DLR); German Aerospace Center (DLR); Leipzig University; Bauhaus-Universit\”at Weimar | ||||
| 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 | 978-1-949373-27-77 | ISBN | 2411-3463 | Medium | |
| Track | Social Media for Disaster Response and Resilie | Expedition | Conference | 17th International Conference on Information Systems for Crisis Response and Management | |
| Notes | matti.wiegmann@uni-weimar.de | Approved | no | ||
| Call Number | Serial | 2278 | |||
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| Author | Takuya Oki | ||||
| Title | Possibility of Using Tweets to Detect Crowd Congestion: A Case Study Using Tweets just before/after the Great East Japan Earthquake | 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 | 584-596 | ||
| Keywords |
Twitter, crowd congestion, time-series analysis, linguistic expression, disaster mitigation. | ||||
| Abstract | During large earthquakes, it is critical to safely guide evacuation efforts and to prevent accidents caused by congestion. In this paper, we focus on detecting the degree of crowd congestion following an earthquake based on information posted to Social Networking Services (SNSs). This research uses text data posted to Twitter just before/after the occurrence of the Great East Japan Earthquake (11 March 2011 at 02:46 PM JST). First, we extract co-occurring place names, proper nouns, and time-series information from tweets about congestion in the Tokyo metropolitan area (TMA). Next, using these extracted data, we analyze the frequency and spatiotemporal characteristics of these tweets. Finally, we identify expressions that describe the degree of crowd congestion and discuss methods to quantify these expressions based on a questionnaire survey and tweets that contain a photograph. | ||||
| 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 | 2133 | |||
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| Author | Shane Errol Halse; Rob Grace; Jess Kropczynski; Andrea Tapia | ||||
| Title | Simulating real-time Twitter data from historical datasets | 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 |
Twitter, Simulation, Crisis Response, Social Media | ||||
| Abstract | In this paper, we will discuss a system design for simulating social media data based on historical datasets. While many datasets containing data collected from social media during crisis have become publicly available, there is a lack of tools or systems can present this data on the same timeline as it was originally posted. Through the design and use of the tool discussed in this paper, we show how historical datasets can be used for algorithm testing, such as those used in machine learning, to improve the quality of the data. In addition, the use of simulated data also has its benefits in training scenarios, which would allow participants to see real, non-fabricated social media messages in the same temporal manner as found on a social media platform. Lastly, we will discuss the positive reception and future improvements suggested by 911 Public Service Answering Point (PSAP) professionals. | ||||
| Address | PSU, United States of America;University of Cincinnati | ||||
| 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 | 1898 | |||
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| Author | Hongmin Li; Doina Caragea; Cornelia Caragea | ||||
| Title | Towards Practical Usage of a Domain Adaptation Algorithm in the Early Hours of a Disaster | 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 | 692-704 | ||
| Keywords |
Twitter; Domain adaptation; Disaster; Classification | ||||
| Abstract | Many machine learning techniques have been proposed to reduce the information overload in social media data during an emergency situation. Among such techniques, domain adaptation approaches present greater potential as compared to supervised algorithms because they don't require labeled data from the current disaster for training. However, the use of domain adaptation approaches in practice is sporadic at best. One reason is that domain adaptation algorithms have parameters that need to be tuned using labeled data from the target disaster, which is presumably not available. To address this limitation, we perform a study on one domain adaptation approach with the goal of understanding how much source data is needed to obtain good performance in a practical situation, and what parameter values of the approach give overall good performance. The results of our study provide useful insights into the practical application of domain adaptation algorithms in real crisis situations. | ||||
| Address | Kansas State University; University of North Texas | ||||
| 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 | ISCRAM @ idladmin @ | Serial | 2057 | ||
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| Author | Antonin Segault; Federico Tajariol; Yang Ishigaki; Ioan Roxin | ||||
| 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 |
| Volume | Issue | Pages | |||
| Keywords |
Twitter; Nuclear Post-Accident; Radiation; Robots; Syntax | ||||
| 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. | ||||
| Address | |||||
| Corporate Author | Thesis | ||||
| Publisher | Federal University of Rio de Janeiro | Place of Publication | Rio de Janeiro, Brasil | Editor | A. Tapia; P. Antunes; V.A. Bañuls; K. Moore; J. Porto |
| Language | English | Summary Language | English | Original Title | |
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | 2411-3388 | ISBN | 978-84-608-7984-9 | Medium | |
| Track | Social Media Studies | Expedition | Conference | 13th International Conference on Information Systems for Crisis Response and Management | |
| Notes | Approved | no | |||
| Call Number | Serial | 1394 | |||
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| Author | Venkata Kishore Neppalli; Murilo Cerqueira Medeiros; Cornelia Caragea; Doina Caragea; Andrea Tapia; Shane Halse | ||||
| Title | Retweetability Analysis and Prediction during Hurricane Sandy | 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 |
Twitter; Retweetability Analysis; Retweetability Prediction; Hurricane Sandy; Disaster Events | ||||
| Abstract | Twitter is a very important source for obtaining information, especially during events such as natural disasters. Users can spread information in Twitter either by crafting new posts, which are called ?tweets,? or by using retweet mechanism to re-post the previously created tweets. During natural disasters, identifying how likely a tweet is to be highly retweeted is very important since it can help promote the spread of good information in a network such as Twitter, as well as it can help stop the spread of misinformation, when corroborated with approaches that identify trustworthy information or misinformation, respectively. In this paper, we present an analysis on retweeted tweets to determine several aspects affecting retweetability. We then extract features from tweets? content and user account information and perform experiments to develop models that automatically predict the retweetability of a tweet in the context of the Hurricane Sandy. | ||||
| Address | |||||
| Corporate Author | Thesis | ||||
| Publisher | Federal University of Rio de Janeiro | Place of Publication | Rio de Janeiro, Brasil | Editor | A. Tapia; P. Antunes; V.A. Bañuls; K. Moore; J. Porto |
| Language | English | Summary Language | English | Original Title | |
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | 2411-3388 | ISBN | 978-84-608-7984-9 | Medium | |
| Track | Social Media Studies | Expedition | Conference | 13th International Conference on Information Systems for Crisis Response and Management | |
| Notes | Approved | no | |||
| Call Number | Serial | 1389 | |||
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| Author | Shane Errol Halse; Andrea Tapia; Anna Squicciarini; Cornelia Caragea | ||||
| Title | Tweet Factors Influencing Trust and Usefulness During Both Man-Made and Natural Disasters | 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 |
Twitter; Sandy; Hurricane; Boston Bombing; Trust; Usefulness | ||||
| Abstract | To this date, research on crisis informatics has focused on the detection of trust in Twitter data through the use of message structure, sentiment, propagation and author. Little research has examined the usefulness of these messages in the crisis response domain. Toward detecting useful messages in case of crisis, in this paper, we characterize tweets, which are perceived useful or trustworthy, and determine their main features. Our analysis is carried out on two datasets (one natural and one man made) gathered from Twitter concerning hurricane Sandy in 2012 and the Boston Bombing 2013. The results indicate that there is a high correlation and similar factors (support for the victims, informational data, use of humor and type of emotion used) influencing trustworthiness and usefulness for both disaster types. This could have impacts on how messages from social media data are analyzed for use in crisis response. | ||||
| Address | |||||
| Corporate Author | Thesis | ||||
| Publisher | Federal University of Rio de Janeiro | Place of Publication | Rio de Janeiro, Brasil | Editor | A. Tapia; P. Antunes; V.A. Bañuls; K. Moore; J. Porto |
| Language | English | Summary Language | English | Original Title | |
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | 2411-3388 | ISBN | 978-84-608-7984-9 | Medium | |
| Track | Social Media Studies | Expedition | Conference | 13th International Conference on Information Systems for Crisis Response and Management | |
| Notes | Approved | no | |||
| Call Number | Serial | 1403 | |||
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| Author | Shane Halse; Jomara Binda; Samantha Weirman | ||||
| Title | It's what's outside that counts: Finding credibility metrics through non-message related Twitter features | 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 | 516-528 | ||
| Keywords |
Twitter; social media; trust | ||||
| Abstract | Social media data, such as Twitter, enables crisis response personnel and civilians to share information during a crisis situation. However, a lack of information gatekeeping processes also translates into concerns about both content and source credibility. This research aims to identify Twitter metrics which could assist with the latter. A 2 (average number of hashtags used) x 2 (ratio of tweets/retweets posted) x 2 (ratio of follower/followee) between-subjects experiment was conducted to evaluate the level of influence of Twitter broker metrics on behavioral intention and the perception of source credibility. The findings indicate that follower/followee ratio in conjunction with hashtag usage approached a significant effect on perceived source credibility. In addition, both Twitter awareness metrics and dispositional trust played an important role in determining behavioral intentions and perceived source credibility. Implications and limitations are also discussed. | ||||
| 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 | 2128 | |||
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