|
Records |
Links |
|
Author |
Pooneh Mousavi; Cody Buntain |
|
|
Title |
“Please Donate for the Affected”: Supporting Emergency Managers in Finding Volunteers and Donations in Twitter Across Disasters |
Type |
Conference Article |
|
Year |
2022 |
Publication |
ISCRAM 2022 Conference Proceedings – 19th International Conference on Information Systems for Crisis Response and Management |
Abbreviated Journal |
Iscram 2022 |
|
|
Volume |
|
Issue |
|
Pages |
605-622 |
|
|
Keywords |
social media; crisis in formatics; volunteers; donations; emergency support functions |
|
|
Abstract |
Despite the outpouring of social support posted to social media channels in the aftermath of disaster, finding and managing content that can translate into community relief, donations, volunteering, or other recovery support is difficult due to the lack of sufficient annotated data around volunteerism. This paper outlines three experiments to alleviate these difficulties. First, we estimate to what degree volunteerism content from one crisis is transferable to another by evaluating the consistency of language in volunteer-and donation-related social media content across 78 disasters. Second it introduces methods for providing computational support in this emergency support function and developing semi-automated models for classifying volunteer-and donation-related social media content in new disaster events. Results show volunteer-and donation-related social media content is sufficiently similar across disasters and disaster types to warrant transferring models across disasters, and we evaluate simple resampling techniques for tuning these models. We then introduce and evaluate a weak-supervision approach to integrate domain knowledge from emergency response officers with machine learningmodelstoimproveclassification accuracy andacceleratethisemergencysupportinnewevents. This method helps to overcome the scarcity in data that we observe related to volunteer-and donation-related social media content. |
|
|
Address |
University of Maryland, College Park; University of Maryland, College Park |
|
|
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 |
2442 |
|
Share this record to Facebook |
|
|
|
|
Author |
Humaira Waqas; Muhammad Imran |
|
|
Title |
#CampFireMissing: An Analysis of Tweets About Missing and Found People From California Wildfires |
Type |
Conference Article |
|
Year |
2019 |
Publication |
Proceedings of the 16th International Conference on Information Systems for Crisis Response And Management |
Abbreviated Journal |
Iscram 2019 |
|
|
Volume |
|
Issue |
|
Pages |
|
|
|
Keywords |
social media, Twitter, missing and found people, California wildfires, disaster response |
|
|
Abstract |
Several research studies have shown the importance of social media data for humanitarian aid. Among others,
the issue of missing and lost people during disasters and emergencies is crucial for disaster managers. This work
analyzes Twitter data from a recent wildfire event to determine its usefulness for the mitigation of the missing and
found people issue. Data analysis performed using various filtering techniques, and trend analysis revealed that
Twitter contains important information potentially useful for emergency managers and volunteers to tackle this
issue. Many tweets were found containing full names, partial names, location information, and other vital clues
which could be useful for finding missing people. |
|
|
Address |
Qatar Computing Research Institute, Qatar |
|
|
Corporate Author |
|
Thesis |
|
|
|
Publisher |
Iscram |
Place of Publication |
Valencia, Spain |
Editor |
Franco, Z.; González, J.J.; Canós, J.H. |
|
|
Language |
English |
Summary Language |
English |
Original Title |
|
|
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
|
Series Volume |
|
Series Issue |
|
Edition |
|
|
|
ISSN |
2411-3387 |
ISBN |
978-84-09-10498-7 |
Medium |
|
|
|
Track |
T8- Social Media in Crises and Conflicts |
Expedition |
|
Conference |
16th International Conference on Information Systems for Crisis Response and Management (ISCRAM 2019) |
|
|
Notes |
|
Approved |
no |
|
|
Call Number |
|
Serial |
1915 |
|
Share this record to Facebook |
|
|
|
|
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 |
|
Share this record to Facebook |
|
|
|
|
Author |
Antonin Segault; Federico Tajariol; Ioan Roxin |
|
|
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 |
|
|
Volume |
|
Issue |
|
Pages |
|
|
|
Keywords |
bots; long-term period; nuclear post-accident; radiations; Twitter |
|
|
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. |
|
|
Address |
|
|
|
Corporate Author |
|
Thesis |
|
|
|
Publisher |
University of Agder (UiA) |
Place of Publication |
Kristiansand, Norway |
Editor |
L. Palen; M. Buscher; T. Comes; A. Hughes |
|
|
Language |
English |
Summary Language |
English |
Original Title |
|
|
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
|
Series Volume |
|
Series Issue |
|
Edition |
|
|
|
ISSN |
2411-3387 |
ISBN |
9788271177881 |
Medium |
|
|
|
Track |
Social Media Studies |
Expedition |
|
Conference |
ISCRAM 2015 Conference Proceedings ? 12th International Conference on Information Systems for Crisis Response and Management |
|
|
Notes |
|
Approved |
yes |
|
|
Call Number |
|
Serial |
1239 |
|
Share this record to Facebook |
|
|
|
|
Author |
Lise Ann St. Denis; Amanda Lee Hughes; Jeremy Diaz; Kylen Solvik; Maxwell B. Joseph; Jennifer K. Balch |
|
|
Title |
'What I Need to Know is What I Don't Know!': Filtering Disaster Twitter Data for Information from Local Individuals |
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 |
730-743 |
|
|
Keywords |
Crisis Informatics, Social Media, Emergency Management, Situational Awareness. |
|
|
Abstract |
We report on the design, development, and evaluation of a user labeling framework for social media monitoring by emergency responders. By labeling Twitter user accounts based on behavior and content, this novel approach identifies tweets from accounts belonging to Individuals generating Personalized content and captures information that might otherwise be missed. We evaluate the framework using training data from the 2018 Camp, Woolsey, and Hill fires. Approximately 30% of the Individual-Personalized tweets contain first-hand information, providing a rich stream of content for social media monitoring. Because it can quickly eliminate most redundant tweets, this framework could be a critical first step in an end-to-end information extraction pipeline. It may also generalize more easily for new disaster events since it relies on general user account attributes rather than tweet content. We conclude with next steps for refining and evaluating our framework in near real-time during a disaster response. |
|
|
Address |
CIRES, Earth Lab, University of Colorado, Boulder; Crisis Informatics Lab Brigham Young University; Institute for Computational and Data Sciences, Department of Geography, Penn State University; CIRES, Earth Lab, Department of Geography, University of Colorado, Boulder; CIRES, Earth Lab, University of Colorado, Boulder; CIRES, Earth Lab, Department of Geography, University of Colorado, Boulder |
|
|
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 |
978-1-949373-27-66 |
ISBN |
2411-3452 |
Medium |
|
|
|
Track |
Social Media for Disaster Response and Resilie |
Expedition |
|
Conference |
17th International Conference on Information Systems for Crisis Response and Management |
|
|
Notes |
Lise.St.Denis@Colorado.edu |
Approved |
no |
|
|
Call Number |
|
Serial |
2267 |
|
Share this record to Facebook |
|
|
|
|
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 |
|
Share this record to Facebook |
|
|
|
|
Author |
Dario Salza; Edoardo Arnaudo; Giacomo Blanco; Claudio Rossi |
|
|
Title |
A 'Glocal' Approach for Real-time Emergency Event Detection in Twitter |
Type |
Conference Article |
|
Year |
2022 |
Publication |
ISCRAM 2022 Conference Proceedings – 19th International Conference on Information Systems for Crisis Response and Management |
Abbreviated Journal |
Iscram 2022 |
|
|
Volume |
|
Issue |
|
Pages |
570-583 |
|
|
Keywords |
Emergency; Event Detection; Social Media; Twitter; Incremental Clustering |
|
|
Abstract |
Social media like Twitter offer not only an unprecedented amount of user-generated content covering developing emergencies but also act as a collector of news produced by heterogeneous sources, including big and small media companies as well as public authorities. However, this volume, velocity, and variety of data constitute the main value and, at the same time, the key challenge to implement and automatic detection and tracking of independent emergency events from the real-time stream of tweets. Leveraging online clustering and considering both textual and geographical features, we propose, implement, and evaluate an algorithm to automatically detect emergency events applying a ‘glocal’ approach, i.e., offering a global coverage while detecting events at local (municipality level) scale. |
|
|
Address |
LINKS Foundation; LINKS Foundation; LINKS Foundation; LINKS Foundation |
|
|
Corporate Author |
|
Thesis |
|
|
|
Publisher |
|
Place of Publication |
Tarbes, France |
Editor |
Rob Grace; Hossein Baharmand |
|
|
Language |
English |
Summary Language |
|
Original Title |
|
|
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
|
Series Volume |
|
Series Issue |
|
Edition |
|
|
|
ISSN |
2411-3387 |
ISBN |
978-82-8427-099-9 |
Medium |
|
|
|
Track |
Social Media for Crisis Management |
Expedition |
|
Conference |
|
|
|
Notes |
|
Approved |
no |
|
|
Call Number |
ISCRAM @ idladmin @ |
Serial |
2440 |
|
Share this record to Facebook |
|
|
|
|
Author |
Robert Power; Bella Robinson; John Colton; Mark Cameron |
|
|
Title |
A Case Study for Monitoring Fires with Twitter |
Type |
Conference Article |
|
Year |
2015 |
Publication |
ISCRAM 2015 Conference Proceedings ? 12th International Conference on Information Systems for Crisis Response and Management |
Abbreviated Journal |
ISCRAM 2015 |
|
|
Volume |
|
Issue |
|
Pages |
|
|
|
Keywords |
Disaster Management; Situational Awareness; social media; Twitter |
|
|
Abstract |
This paper presents a user configurable monitoring system to track in near-real-time tweets describing fire events. The system targets fire related words in a user defined region of interest published on Twitter which are further processed by a text classifier to determine if they describe a known fire event of interest. The system was motivated from a case study that examined a corpus of tweets posted during active bushfires. This demonstrated that useful information is available on Twitter about fire events from people who are in the vicinity.
We present an overview of the system describing how it is initially configured by a user to focus on specific fire events in Australia, the development of a text classifier to identify tweets of interest, especially those with accompanying photos, and the monitoring system that can track multiple events at once. |
|
|
Address |
|
|
|
Corporate Author |
|
Thesis |
|
|
|
Publisher |
University of Agder (UiA) |
Place of Publication |
Kristiansand, Norway |
Editor |
L. Palen; M. Buscher; T. Comes; A. Hughes |
|
|
Language |
English |
Summary Language |
English |
Original Title |
|
|
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
|
Series Volume |
|
Series Issue |
|
Edition |
|
|
|
ISSN |
2411-3387 |
ISBN |
9788271177881 |
Medium |
|
|
|
Track |
Social Media Studies |
Expedition |
|
Conference |
ISCRAM 2015 Conference Proceedings ? 12th International Conference on Information Systems for Crisis Response and Management |
|
|
Notes |
|
Approved |
yes |
|
|
Call Number |
|
Serial |
1237 |
|
Share this record to Facebook |
|
|
|
|
Author |
Imen Bizid; Patrice Boursier; Jacques Morcos; Sami Faiz |
|
|
Title |
A Classification Model for the Identification of Prominent Microblogs Users during a Disaster |
Type |
Conference Article |
|
Year |
2015 |
Publication |
ISCRAM 2015 Conference Proceedings ? 12th International Conference on Information Systems for Crisis Response and Management |
Abbreviated Journal |
ISCRAM 2015 |
|
|
Volume |
|
Issue |
|
Pages |
|
|
|
Keywords |
Disaster Management; Information retrieval; Microblogs |
|
|
Abstract |
Content shared in microblogs during disasters is expressed in various formats and languages. This diversity makes the information retrieval process more complex and computationally infeasible in real time. To address this, we propose a classification model for the identification of prominent users who are sharing relevant and exclusive information during the disaster. Users who have shared at least one tweet about the disaster are modeled using three kinds of time-sensitive features, including topical, social and geographical features. Then, these users are classified into two classes using a linear Support Vector Machine (SVM) to evaluate them over the extracted features and identify the most prominent ones. The first results using the actual dataset, show that our model has a high accuracy by detecting most of the prominent users. Moreover, we demonstrate that all the proposed features used by our model are indispensable to achieve this high accuracy. |
|
|
Address |
|
|
|
Corporate Author |
|
Thesis |
|
|
|
Publisher |
University of Agder (UiA) |
Place of Publication |
Kristiansand, Norway |
Editor |
L. Palen; M. Buscher; T. Comes; A. Hughes |
|
|
Language |
English |
Summary Language |
English |
Original Title |
|
|
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
|
Series Volume |
|
Series Issue |
|
Edition |
|
|
|
ISSN |
2411-3387 |
ISBN |
9788271177881 |
Medium |
|
|
|
Track |
Social Media Studies |
Expedition |
|
Conference |
ISCRAM 2015 Conference Proceedings ? 12th International Conference on Information Systems for Crisis Response and Management |
|
|
Notes |
|
Approved |
yes |
|
|
Call Number |
|
Serial |
1241 |
|
Share this record to Facebook |
|
|
|
|
Author |
Therese Habig; Richard Lüke; Simon Gehlhar; Torben Sauerland; Daniel Tappe |
|
|
Title |
A Consolidated Understanding of Disaster Community Technologies |
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 |
778-791 |
|
|
Keywords |
Disaster Community Technologies, social media and crowdsourcing, categorization and classification schema, knowledge base |
|
|
Abstract |
Since the beginning of this millennium, there has been an increasing use of social media and crowdsourcing (SMCS) technologies in disaster situations (Reuter & Kaufhold, 2018). Disaster management organizations and corresponding research are increasingly working on ways of integrating SMCS into the processes of crisis management. In a changing technological landscape to address disasters, and with increasing diversity of stakeholders in disasters, the purpose of this research is to provide an overview of technologies for SMCS within disasters to improve community resilience. The identified and analyzed technologies are summarized under the term “Disaster Community Technologies” (DCT). The paper presents a classification schema (the “DCT-schema”) for those technologies. The goal is to generate an overview of DCT in a rapidly evolving environment and to provide the practical benefit for different stakeholders to identify the right one from the overview. |
|
|
Address |
safety innovation center; safety innovation center; safety innovation center; safety innovation center; safety innovation 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 |
habig@safetyinnovation.center |
Approved |
no |
|
|
Call Number |
ISCRAM @ idladmin @ |
Serial |
2373 |
|
Share this record to Facebook |
|
|
|
|
Author |
Axel Schulz; Tung Dang Thanh; Heiko Paulheim; Immanuel Schweizer |
|
|
Title |
A fine-grained sentiment analysis approach for detecting crisis related microposts |
Type |
Conference Article |
|
Year |
2013 |
Publication |
ISCRAM 2013 Conference Proceedings – 10th International Conference on Information Systems for Crisis Response and Management |
Abbreviated Journal |
ISCRAM 2013 |
|
|
Volume |
|
Issue |
|
Pages |
846-851 |
|
|
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 |
|
|
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. |
|
|
Address |
Technische Universität Darmstadt, Germany; Universität Mannheim, Germany |
|
|
Corporate Author |
|
Thesis |
|
|
|
Publisher |
Karlsruher Institut fur Technologie |
Place of Publication |
KIT; Baden-Baden |
Editor |
T. Comes, F. Fiedrich, S. Fortier, J. Geldermann and T. Müller |
|
|
Language |
English |
Summary Language |
English |
Original Title |
|
|
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
|
Series Volume |
|
Series Issue |
|
Edition |
|
|
|
ISSN |
2411-3387 |
ISBN |
9783923704804 |
Medium |
|
|
|
Track |
Social Media |
Expedition |
|
Conference |
10th International ISCRAM Conference on Information Systems for Crisis Response and Management |
|
|
Notes |
|
Approved |
no |
|
|
Call Number |
|
Serial |
927 |
|
Share this record to Facebook |
|
|
|
|
Author |
Kartikeya Bajpai; Anuj Jaiswal |
|
|
Title |
A framework for analyzing collective action events on Twitter |
Type |
Conference Article |
|
Year |
2011 |
Publication |
8th International Conference on Information Systems for Crisis Response and Management: From Early-Warning Systems to Preparedness and Training, ISCRAM 2011 |
Abbreviated Journal |
ISCRAM 2011 |
|
|
Volume |
|
Issue |
|
Pages |
|
|
|
Keywords |
Information systems; Collective action; Content and structure; Government censorships; Micro-blogging platforms; Research goals; Social movements; Thailand; Twitter; Social networking (online) |
|
|
Abstract |
Recent years have witnessed multiple international protest movements which have purportedly been greatly affected by the use of Twitter, a micro-blogging platform. Social movement actors in Iran, Moldova, Kyrgyzstan and Thailand are thought to have utilized Twitter to spread information, co-ordinate protest activities, evade government censorship and, in some cases, to spread misinformation. We propose a framework for conceptualizing and analyzing Twitter data related to contentious collective action crises. Our primary research goal is to delineate a framework informed with a social movements lens and to demonstrate the framework by means of Twitter usage data related to the Thailand protests of 2010. Our proposed framework concerns itself with two aspects of protest activities and Twitter usage, namely, analyzing the content and structure of messages and our construct of Twitter protest waves. |
|
|
Address |
Pennsylvania State University, United States |
|
|
Corporate Author |
|
Thesis |
|
|
|
Publisher |
Information Systems for Crisis Response and Management, ISCRAM |
Place of Publication |
Lisbon |
Editor |
M.A. Santos, L. Sousa, E. Portela |
|
|
Language |
English |
Summary Language |
English |
Original Title |
|
|
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
|
Series Volume |
|
Series Issue |
|
Edition |
|
|
|
ISSN |
2411-3387 |
ISBN |
9789724922478 |
Medium |
|
|
|
Track |
Social Media and Collaborative Systems |
Expedition |
|
Conference |
8th International ISCRAM Conference on Information Systems for Crisis Response and Management |
|
|
Notes |
|
Approved |
no |
|
|
Call Number |
|
Serial |
283 |
|
Share this record to Facebook |
|
|
|
|
Author |
Lívia Castro Degrossi; João Porto de Albuquerque; Roberto dos Santos Rocha; Alexander Zipf |
|
|
Title |
A Framework of Quality Assessment Methods for Crowdsourced Geographic Information: a Systematic Literature Review |
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 |
532-545 |
|
|
Keywords |
Volunteered Geographic Information; VGI; Crowdsourced Geographic Information; Quality Assessment; Systematic Literature Review |
|
|
Abstract |
Crowdsourced Geographic Information (CGI) has emerged as a potential source of geographic information in different application domains. Despite the advantages associated with it, this information lacks quality assurance, since it is provided by different people. Therefore, several authors have started investigating different methods to assess the quality of CGI. Some of the existing methods have been summarized in different classification scheme. However, there is not an overview of the methods employed to assess the quality of CGI in the absence of authoritative data. On the basis of a systematic literature review, we found 13 methods that can be employed to this end. |
|
|
Address |
Department of Computer Systems University of São Paulo, São Carlos, Brazil; Centre for Interdisciplinary Methodologies University of Warwick, Coventry, UK; GIScience Research Group, Heidelberg University, Germany |
|
|
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 |
2041 |
|
Share this record to Facebook |
|
|
|
|
Author |
Daniel Link; Bernd Hellingrath; Jie Ling |
|
|
Title |
A Human-is-the-Loop Approach for Semi-Automated Content Moderation |
Type |
Conference Article |
|
Year |
2016 |
Publication |
ISCRAM 2016 Conference Proceedings ? 13th International Conference on Information Systems for Crisis Response and Management |
Abbreviated Journal |
ISCRAM 2016 |
|
|
Volume |
|
Issue |
|
Pages |
|
|
|
Keywords |
Disaster Management; Social Media Analysis; Human-Is-The-Loop; Content Moderation; Supervised Machine Learning |
|
|
Abstract |
Online social media has been recognized as a valuable information source for disaster management whose volume, velocity and variety exceed manual processing capacity. Current machine learning systems that support the processing of such data generally follow a human-in-the-loop approach, which has several inherent limitations. This work applies the human-is-the-loop concept from visual analytics to semi-automate a manual content moderation workflow, wherein human moderators take the dominant role. The workflow is instantiated with a supervised machine learning system that supports moderators with suggestions regarding the relevance and categorization of content. The instantiated workflow has been evaluated using in-depth interviews with practitioners and serious games. which suggest that it offers good compatibility with work practices in humanitarian assessment as well as improved moderation quality and higher flexibility than common approaches. |
|
|
Address |
|
|
|
Corporate Author |
|
Thesis |
|
|
|
Publisher |
Federal University of Rio de Janeiro |
Place of Publication |
Rio de Janeiro, Brasil |
Editor |
A. Tapia; P. Antunes; V.A. Bañuls; K. Moore; J. Porto |
|
|
Language |
English |
Summary Language |
English |
Original Title |
|
|
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
|
Series Volume |
|
Series Issue |
|
Edition |
|
|
|
ISSN |
2411-3388 |
ISBN |
978-84-608-7984-9 |
Medium |
|
|
|
Track |
Social Media Studies |
Expedition |
|
Conference |
13th International Conference on Information Systems for Crisis Response and Management |
|
|
Notes |
|
Approved |
no |
|
|
Call Number |
|
Serial |
1401 |
|
Share this record to Facebook |
|
|
|
|
Author |
Sofia Eleni Spatharioti; Rebecca Govoni; Jennifer S. Carrera; Sara Wylie; Seth Cooper |
|
|
Title |
A Required Work Payment Scheme for Crowdsourced Disaster Response: Worker Performance and Motivations |
Type |
Conference Article |
|
Year |
2017 |
Publication |
Proceedings of the 14th International Conference on Iformation Systems for Crisis Response And Management |
Abbreviated Journal |
Iscram 2017 |
|
|
Volume |
|
Issue |
|
Pages |
475-488 |
|
|
Keywords |
crowdsourcing; Amazon Mechanical Turk; payment; motivation; required work |
|
|
Abstract |
Crowdsourcing is an increasingly popular approach for processing data in response to disasters. While volunteer crowdsourcing may suÿce for high-profile disasters, paid crowdsourcing may be necessary to recruit workers for less prominent events. Thus, understanding the impact of payment schemes on worker behavior and motivation may improve outcomes. In this work, we presented workers recruited from Amazon Mechanical Turk with a disaster response task in which they could provide a variable number of image ratings. We paid workers a fixed amount to provide a minimum number of image ratings, allowing them to voluntarily provide more if desired; this allowed us to examine the impact of dierent amounts of required work. We found that requiring no ratings resulted in workers voluntary completing more work, and being more likely to indicate motivation related to interest on a post survey, than when small numbers of ratings were required. This is consistent with the motivational crowding-out eect, even in paid crowdsourcing. We additionally found that providing feedback on progress positively impacted the amount of work done. |
|
|
Address |
Northeastern University; Michigan State University |
|
|
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 |
2036 |
|
Share this record to Facebook |
|
|
|
|
Author |
André Dittrich; Christian Lucas |
|
|
Title |
A step towards real-time analysis of major disaster events based on tweets |
Type |
Conference Article |
|
Year |
2013 |
Publication |
ISCRAM 2013 Conference Proceedings – 10th International Conference on Information Systems for Crisis Response and Management |
Abbreviated Journal |
ISCRAM 2013 |
|
|
Volume |
|
Issue |
|
Pages |
868-874 |
|
|
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 |
|
|
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. |
|
|
Address |
Karlsruhe Institute of Technology (KIT), Germany |
|
|
Corporate Author |
|
Thesis |
|
|
|
Publisher |
Karlsruher Institut fur Technologie |
Place of Publication |
KIT; Baden-Baden |
Editor |
T. Comes, F. Fiedrich, S. Fortier, J. Geldermann and T. Müller |
|
|
Language |
English |
Summary Language |
English |
Original Title |
|
|
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
|
Series Volume |
|
Series Issue |
|
Edition |
|
|
|
ISSN |
2411-3387 |
ISBN |
9783923704804 |
Medium |
|
|
|
Track |
Social Media |
Expedition |
|
Conference |
10th International ISCRAM Conference on Information Systems for Crisis Response and Management |
|
|
Notes |
|
Approved |
no |
|
|
Call Number |
|
Serial |
452 |
|
Share this record to Facebook |
|
|
|
|
Author |
Daniel E. Lane; Tracey L. O'Sullivan; Craig E. Kuziemsky; Fikret Berkes; Anthony Charles |
|
|
Title |
A structured equation model of collaborative community response |
Type |
Conference Article |
|
Year |
2013 |
Publication |
ISCRAM 2013 Conference Proceedings – 10th International Conference on Information Systems for Crisis Response and Management |
Abbreviated Journal |
ISCRAM 2013 |
|
|
Volume |
|
Issue |
|
Pages |
906-911 |
|
|
Keywords |
Computer simulation; Decision theory; Information systems; Mathematical models; Risk analysis; Adaptation; C-change; Community collaboration; Community engagement; Emergency response; EnRiCH; Preparedness; Simulation; Structured equation modeling; Emergency services |
|
|
Abstract |
This paper analyses the collaborative dynamic of community in response to urgent situations. Community emergencies arising from natural or man-induced threats are considered as exogenous events that stimulate community resources to be unified around the response, action, and recovery activities related to the emergency. A structured equation model is derived to depict the actions of the community system. The system is described in terms of its resources including the propensity to trigger community action and collaboration among diverse groups. The community is profiled with respect to its ability to respond. The system defines the trigger mechanisms that are considered to be the drivers of collaborative action. A simulation model is presented to enact the system emergencies, community profiles, and collaborative response. The results develop an improved understanding of conditions that engage community collaborative actions as illustrated by examples from community research in the EnRiCH and the C-Change community research projects. |
|
|
Address |
Telfer School of Management, University of Ottawa, Canada; Interdisciplinary Faculty of Health Sciences, University of Ottawa, Canada; Natural Resources Institute, University of Manitoba, Canada; Department of Finance and Management Science, Saint Mary's University, Canada |
|
|
Corporate Author |
|
Thesis |
|
|
|
Publisher |
Karlsruher Institut fur Technologie |
Place of Publication |
KIT; Baden-Baden |
Editor |
T. Comes, F. Fiedrich, S. Fortier, J. Geldermann and T. Müller |
|
|
Language |
English |
Summary Language |
English |
Original Title |
|
|
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
|
Series Volume |
|
Series Issue |
|
Edition |
|
|
|
ISSN |
2411-3387 |
ISBN |
9783923704804 |
Medium |
|
|
|
Track |
Social Media |
Expedition |
|
Conference |
10th International ISCRAM Conference on Information Systems for Crisis Response and Management |
|
|
Notes |
|
Approved |
no |
|
|
Call Number |
|
Serial |
677 |
|
Share this record to Facebook |
|
|
|
|
Author |
Lamsal, R.; Read, M.R.; Karunasekera, S. |
|
|
Title |
A Twitter narrative of the COVID-19 pandemic in Australia |
Type |
Conference Article |
|
Year |
2023 |
Publication |
Proceedings of the 20th International ISCRAM Conference |
Abbreviated Journal |
Iscram 2023 |
|
|
Volume |
|
Issue |
|
Pages |
353-370 |
|
|
Keywords |
Crisis Informatics; Situational Awareness; Topic Modeling; Granger Causality; Network Analysis |
|
|
Abstract |
Social media platforms contain abundant data that can provide comprehensive knowledge of historical and real-time events. During crisis events, the use of social media peaks, as people discuss what they have seen, heard, or felt. Previous studies confirm the usefulness of such socially generated discussions for the public, first responders, and decision-makers to gain a better understanding of events as they unfold at the ground level. This study performs an extensive analysis of COVID-19-related Twitter discussions generated in Australia between January 2020, and October 2022. We explore the Australian Twitterverse by employing state-of-the-art approaches from both supervised and unsupervised domains to perform network analysis, topic modeling, sentiment analysis, and causality analysis. As the presented results provide a comprehensive understanding of the Australian Twitterverse during the COVID-19 pandemic, this study aims to explore the discussion dynamics to aid the development of future automated information systems for epidemic/pandemic management. |
|
|
Address |
The University of Melbourne |
|
|
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/GQED8281 |
Approved |
no |
|
|
Call Number |
ISCRAM @ idladmin @ |
Serial |
2531 |
|
Share this record to Facebook |
|
|
|
|
Author |
Firoj Alam; Ferda Ofli; Muhammad Imran; Michael Aupetit |
|
|
Title |
A Twitter Tale of Three Hurricanes: Harvey, Irma, and Maria |
Type |
Conference Article |
|
Year |
2018 |
Publication |
ISCRAM 2018 Conference Proceedings – 15th International Conference on Information Systems for Crisis Response and Management |
Abbreviated Journal |
Iscram 2018 |
|
|
Volume |
|
Issue |
|
Pages |
553-572 |
|
|
Keywords |
social media, artificial intelligence, image processing, supervised classification, disaster management |
|
|
Abstract |
People increasingly use microblogging platforms such as Twitter during natural disasters and emergencies. Research studies have revealed the usefulness of the data available on Twitter for several disaster response tasks. However, making sense of social media data is a challenging task due to several reasons such as limitations of available tools to analyze high-volume and high-velocity data streams. This work presents an extensive multidimensional analysis of textual and multimedia content from millions of tweets shared on Twitter during the three disaster events. Specifically, we employ various Artificial Intelligence techniques from Natural Language Processing and Computer Vision fields, which exploit different machine learning algorithms to process the data generated during the disaster events. Our study reveals the distributions of various types of useful information that can inform crisis managers and responders as well as facilitate the development of future automated systems for disaster management. |
|
|
Address |
|
|
|
Corporate Author |
|
Thesis |
|
|
|
Publisher |
Rochester Institute of Technology |
Place of Publication |
Rochester, NY (USA) |
Editor |
Kees Boersma; Brian Tomaszeski |
|
|
Language |
English |
Summary Language |
English |
Original Title |
|
|
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
|
Series Volume |
|
Series Issue |
|
Edition |
|
|
|
ISSN |
2411-3387 |
ISBN |
978-0-692-12760-5 |
Medium |
|
|
|
Track |
Social Media Studies |
Expedition |
|
Conference |
ISCRAM 2018 Conference Proceedings - 15th International Conference on Information Systems for Crisis Response and Management |
|
|
Notes |
|
Approved |
no |
|
|
Call Number |
|
Serial |
2131 |
|
Share this record to Facebook |
|
|
|
|
Author |
Nasik Muhammad Nafi; Avishek Bose; Sarthak Khanal; Doina Caragea; William H. Hsu |
|
|
Title |
Abstractive Text Summarization of Disaster-Related Documents |
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 |
881-892 |
|
|
Keywords |
Disaster Reporting; Text Summarization; Information Extraction; Reinforcement Learning; Evaluation Metrics |
|
|
Abstract |
Abstractive summarization is intended to capture key information from the full text of documents. In the application domain of disaster and crisis event reporting, key information includes disaster effects, cause, and severity. While some researches regarding information extraction in the disaster domain have focused on keyphrase extraction from short disaster-related texts like tweets, there is hardly any work that attempts abstractive summarization of long disaster-related documents. Following the recent success of Reinforcement Learning (RL) in other domains, we leverage an RL-based state-of-the-art approach in abstractive summarization to summarize disaster-related documents. RL enables an agent to find an optimal policy by maximizing some reward. We design a novel hybrid reward metric for the disaster domain by combining \underline{Vec}tor Similarity and \underline{Lex}icon Matching (\textit{VecLex}) to maximize the relevance of the abstract to the source document while focusing on disaster-related keywords. We evaluate the model on a disaster-related subset of a CNN/Daily Mail dataset consisting of 104,913 documents. The results show that our approach produces more informative summaries and achieves higher \textit{VecLex} scores compared to the baseline. |
|
|
Address |
Kansas State University; Kansas State University; Kansas State University; Kansas State University; Kansas State University |
|
|
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 |
978-1-949373-27-78 |
ISBN |
2411-3464 |
Medium |
|
|
|
Track |
Social Media for Disaster Response and Resilie |
Expedition |
|
Conference |
17th International Conference on Information Systems for Crisis Response and Management |
|
|
Notes |
nnafi@ksu.edu |
Approved |
no |
|
|
Call Number |
|
Serial |
2279 |
|
Share this record to Facebook |
|
|
|
|
Author |
Keri K. Stephens; Jessica L. Ford; Ashley Barrett; Michael J. Mahometa |
|
|
Title |
Alert networks of ICTs and sources in campus emergencies |
Type |
Conference Article |
|
Year |
2014 |
Publication |
ISCRAM 2014 Conference Proceedings – 11th International Conference on Information Systems for Crisis Response and Management |
Abbreviated Journal |
ISCRAM 2014 |
|
|
Volume |
|
Issue |
|
Pages |
652-661 |
|
|
Keywords |
Information systems; Mobile devices; Risk management; Emergency alerts; Emergency management; ICTs; Safety communications; Social media; Social networking (online) |
|
|
Abstract |
This study contributes an understanding of how ICTs and varying information sources work together during emergency alerts. It builds on the prior work on campus active shooter events by examining an organization that used a range of ICTs including mobile devices, social media, organizational tools, and news media, to notify their stakeholders about an emergency. The study design used a survey to capture the responses from a random sample of over 1000 stakeholders-students, faculty, and staff-who were notified of an active shooter emergency. The findings from the first three notifications suggest that messages reaching the most stakeholders were (a) sent by official sources through ICTs like mobile phones; (b) official email communication, and (c) messages that included face-to-face communication. While 11 different ICTs were included in the study, mass media (i.e., television and radio), and social media (Twitter and Facebook) did not function substantially in the emergency alert process. |
|
|
Address |
University of Texas at Austin, United States |
|
|
Corporate Author |
|
Thesis |
|
|
|
Publisher |
The Pennsylvania State University |
Place of Publication |
University Park, PA |
Editor |
S.R. Hiltz, M.S. Pfaff, L. Plotnick, and P.C. Shih. |
|
|
Language |
English |
Summary Language |
English |
Original Title |
|
|
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
|
Series Volume |
|
Series Issue |
|
Edition |
|
|
|
ISSN |
2411-3387 |
ISBN |
9780692211946 |
Medium |
|
|
|
Track |
Social Media in Crisis Response and Management |
Expedition |
|
Conference |
11th International ISCRAM Conference on Information Systems for Crisis Response and Management |
|
|
Notes |
|
Approved |
no |
|
|
Call Number |
|
Serial |
974 |
|
Share this record to Facebook |
|
|
|
|
Author |
Online Media as a Means to Affect Public Trust in Emergency Responders |
|
|
Title |
Amanda Lee Hughes; Apoorva Chauhan |
Type |
Conference Article |
|
Year |
2015 |
Publication |
ISCRAM 2015 Conference Proceedings ? 12th International Conference on Information Systems for Crisis Response and Management |
Abbreviated Journal |
ISCRAM 2015 |
|
|
Volume |
|
Issue |
|
Pages |
|
|
|
Keywords |
computer-mediated communication; crisis informatics; emergency response; social media; trust |
|
|
Abstract |
This exploratory study examines how fire and police departments used online media during the 2012 Hurricane Sandy and how these media can be used to affect trust with members of the public during such an event. Using trust theory, we describe how online communications provide a means for emergency responders to appear trustworthy through online acts of ability, integrity, and benevolence. We conclude with implications and recommendations for emergency response practice and a trajectory of future work. |
|
|
Address |
|
|
|
Corporate Author |
|
Thesis |
|
|
|
Publisher |
University of Agder (UiA) |
Place of Publication |
Kristiansand, Norway |
Editor |
L. Palen; M. Buscher; T. Comes; A. Hughes |
|
|
Language |
English |
Summary Language |
English |
Original Title |
|
|
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
|
Series Volume |
|
Series Issue |
|
Edition |
|
|
|
ISSN |
2411-3387 |
ISBN |
9788271177881 |
Medium |
|
|
|
Track |
Social Media Studies |
Expedition |
|
Conference |
ISCRAM 2015 Conference Proceedings ? 12th International Conference on Information Systems for Crisis Response and Management |
|
|
Notes |
|
Approved |
yes |
|
|
Call Number |
|
Serial |
1226 |
|
Share this record to Facebook |
|
|
|
|
Author |
Kenneth Joseph; Peter M. Landwehr; Kathleen M. Carley |
|
|
Title |
An approach to selecting keywords to track on twitter during a disaster |
Type |
Conference Article |
|
Year |
2014 |
Publication |
ISCRAM 2014 Conference Proceedings – 11th International Conference on Information Systems for Crisis Response and Management |
Abbreviated Journal |
ISCRAM 2014 |
|
|
Volume |
|
Issue |
|
Pages |
672-676 |
|
|
Keywords |
Data mining; Disasters; Information systems; Keyword searching; Novel methodology; Situational awareness; Social media; Twitter; Social networking (online) |
|
|
Abstract |
Several studies on Twitter usage during disasters analyze tweets collected using ad-hoc keywords pre-defined by researchers. While recent efforts have worked to improve this methodology, open questions remain about which keywords can be used to uncover tweets contributing to situational awareness (SA) and the quality of tweets returned using different terms. Herein we consider a novel methodology for uncovering relevant keywords one can use to search for tweets containing situational awareness. We provide a description of the methodology and initial results which suggest our approach may lead to better keywords to use for keyword searching during disasters. |
|
|
Address |
Carnegie Mellon University, United States |
|
|
Corporate Author |
|
Thesis |
|
|
|
Publisher |
The Pennsylvania State University |
Place of Publication |
University Park, PA |
Editor |
S.R. Hiltz, M.S. Pfaff, L. Plotnick, and P.C. Shih. |
|
|
Language |
English |
Summary Language |
English |
Original Title |
|
|
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
|
Series Volume |
|
Series Issue |
|
Edition |
|
|
|
ISSN |
2411-3387 |
ISBN |
9780692211946 |
Medium |
|
|
|
Track |
Social Media in Crisis Response and Management |
Expedition |
|
Conference |
11th International ISCRAM Conference on Information Systems for Crisis Response and Management |
|
|
Notes |
|
Approved |
no |
|
|
Call Number |
|
Serial |
640 |
|
Share this record to Facebook |
|
|
|
|
Author |
Tasneem, F.; Chakraborty, S.; Chy, A.N. |
|
|
Title |
An Early Synthesis of Deep Neural Networks to Identify Multimodal Informative Disaster Tweets |
Type |
Conference Article |
|
Year |
2023 |
Publication |
Proceedings of the 20th International ISCRAM Conference |
Abbreviated Journal |
Iscram 2023 |
|
|
Volume |
|
Issue |
|
Pages |
428-438 |
|
|
Keywords |
Early Fusion; Crisis Tweets; BERT-LSTM; ResNet50; Multimodal Framework |
|
|
Abstract |
Twitter is always worthwhile in facilitating communication during disasters. It helps in raising situational awareness and undertaking disaster control actions as quickly as possible to alleviate the miseries. But the noisy essence of Twitter causes difficulty in distinguishing relevant information from the heterogeneous contents. Therefore, extracting informative tweets is a substantial task to help in crisis intervention. Analyzing only the text or image content of the tweet often misses necessary insights which might be helpful during disasters. In this paper, we propose a multimodal framework to address the challenges of identifying informative crisis-related tweets containing both texts and images. Our presented approach incorporates an early fusion strategy of BERT-LSTM and ResNet50 networks which effectively learns from the joint representation of texts and images. The experiments and evaluation on the benchmark CrisisMMD dataset show that our fusion method surpasses the baseline by 7% and substantiates its potency over the unimodal systems. |
|
|
Address |
University of Chittagong; University of Chittagong; University of Chittagong |
|
|
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/OMIR7766 |
Approved |
no |
|
|
Call Number |
ISCRAM @ idladmin @ |
Serial |
2537 |
|
Share this record to Facebook |
|
|
|
|
Author |
Shivam Sharma; Cody Buntain |
|
|
Title |
An Evaluation of Twitter Datasets from Non-Pandemic Crises Applied to Regional COVID-19 Contexts |
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 |
808-815 |
|
|
Keywords |
covid19, twitter, trecis, cross-validation, machine learning, transfer learning |
|
|
Abstract |
In 2020, we have witnessed an unprecedented crisis event, the COVID-19 pandemic. Various questions arise regarding the nature of this crisis data and the impacts it would have on the existing tools. In this paper, we aim to study whether we can include pandemic-type crisis events with general non-pandemic events and hypothesize that including labeled crisis data from a variety of non-pandemic events will improve classification performance over models trained solely on pandemic events. To test our hypothesis we study the model performance for different models by performing a cross validation test on pandemic only held-out sets for two different types of training sets, one containing only pandemic data and the other a combination of pandemic and non-pandemic crisis data, and comparing the results of the two. Our results approve our hypothesis and give evidence of some crucial information propagation upon inclusion of non-pandemic crisis data to pandemic data. |
|
|
Address |
New Jersey Institute of Technology; New Jersey Institute of Technology |
|
|
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 |
cbuntain@njit.edu |
Approved |
no |
|
|
Call Number |
ISCRAM @ idladmin @ |
Serial |
2375 |
|
Share this record to Facebook |