| Records |
| Author |
Ly Dinh; Sumeet Kulkarni; Pingjing Yang; Jana Diesner |
| Title |
Reliability of Methods for Extracting Collaboration Networks from Crisis-related Situational Reports and Tweets |
Type |
Conference Article |
Year  |
2023 |
Publication |
Proceedings of the ISCRAM Asia Pacific Conference 2022 |
Abbreviated Journal |
Proc. ISCRAM AP 2022 |
| Volume |
|
Issue |
|
Pages |
181-195 |
| Keywords |
Collaboration Networks; Natural Language Processing; Interorganizational Collaboration; Situational Awareness |
| Abstract |
Assessing the effectiveness of crisis response is key to improving preparedness and adapting policies. One method for response evaluation is reviewing actual response activities and interactions. Response reports are often available in the form of natural language text data. Analyzing a large number of such reports requires automated or semi automated solutions. To improve the trustworthiness of methods for this purpose, we empirically validate the reliability of three relation extraction methods that we used to construct interorganizational collaboration networks by comparing them against human-annotated ground truth (crisis-specific situational reports and tweets). For entity extraction, we find that using a combination of two off-the-shelf methods (FlairNLP and SpaCy) is optimal for situational reports data and one method (SpaCy) for tweets data. For relation extraction, we find that a heuristics-based model that we built by leveraging word co-occurrence and deep and shallow syntax as features and training it on domain-specific text data outperforms two state-of-the-art relation extraction models (Stanford OpenIE and OneIE) that were pre-trained on general domain data. We also find that situational reports, on average, contain less entities and relations than tweets, but the extracted networks are more closely related to collaboration activities mentioned in the ground truth. As it is widely known that general domain tools might need adjustment to perform accurately in specific domains, we did not expect the tested off-the-shelf tools to perform highly accurately. Our point is to rather identify what accuracy one could reasonably expect when leveraging available resources as-is for domain specific work (in this case, crisis informatics), what errors (in terms of false positives and false negatives) to expect, and how to account for that. |
| Address |
University of South Florida; University of Illinois at Urbana-Champaign; University of Illinois at Urbana-Champaign; University of Illinois at Urbana-Champaign |
| Corporate Author |
|
Thesis |
|
| Publisher |
Massey Unversity |
Place of Publication |
Palmerston North, New Zealand |
Editor |
Thomas J. Huggins, V.L. |
| Language |
English |
Summary Language |
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Original Title |
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| Series Editor |
|
Series Title |
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Abbreviated Series Title |
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| 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 |
2492 |
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| Author |
Xiao Li; Julia Kotlarsky; Michael D. Myers |
| Title |
Crowdsourcing and the COVID-19 Response in China: An Actor-Network Perspective |
Type |
Conference Article |
| Year |
2023 |
Publication |
Proceedings of the ISCRAM Asia Pacific Conference 2022 |
Abbreviated Journal |
Proc. ISCRAM AP 2022 |
| Volume |
|
Issue |
|
Pages |
240-246 |
| Keywords |
Disaster; Crowdsourcing; Actor-Network; Social Media |
| Abstract |
Crowdsourcing, serving as a distributed problem-solving and production model, can help in the response to a disaster. The current literature focuses on the flow of crowdsourced information, but the question of how crowdsourcing contributes to physical disaster workflows remains to be addressed. Based on a case study of China’s response to COVID-19, this research aims to explore the role of crowdsourcing stakeholders and how they acted to respond to the outbreak. Actor network theory is applied as the lens to elucidate the roles of different heterogeneous actors. The preliminary results indicate that socio-technical actors activated, absorbed, associated, and aligned with each other to combat the pandemic. We suggest ways to augment the actor network to address potential future outbreaks. |
| Address |
University of Auckland; University of Auckland; University of Auckland |
| Corporate Author |
|
Thesis |
|
| Publisher |
Massey Unversity |
Place of Publication |
Palmerston North, New Zealand |
Editor |
Thomas J. Huggins, V.L. |
| Language |
English |
Summary Language |
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Original Title |
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| Series Editor |
|
Series Title |
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Abbreviated Series Title |
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| Series Volume |
|
Series Issue |
|
Edition |
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| ISSN |
2411-3387 |
ISBN |
978-0-473-66845-7 |
Medium |
|
| Track |
Social Media for Disaster Response |
Expedition |
|
Conference |
|
| Notes |
|
Approved |
no |
| Call Number |
ISCRAM @ idladmin @ |
Serial |
2497 |
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| Author |
Robert Power; Bella Robinson; Mark Cameron |
| Title |
Insights from a Decade of Twitter Monitoring for Emergency Management |
Type |
Conference Article |
| Year |
2023 |
Publication |
Proceedings of the ISCRAM Asia Pacific Conference 2022 |
Abbreviated Journal |
Proc. ISCRAM AP 2022 |
| Volume |
|
Issue |
|
Pages |
247-257 |
| Keywords |
Crisis Coordination; Disaster Management; Situation Awareness; Social Media; System Architecture; Twitter |
| Abstract |
The Emergency Situation Awareness (ESA) tool began as a research study into automated web text mining to support emergency management use cases. It started in late 2009 by investigating how people respond on Twitter to specific emergency events and we quickly realized that every emergency situation is different and preemptively defining keywords to search for content on Twitter beforehand would likely miss important information. So, in late September 2011 we established location-based searches with the aim of collecting all the tweets published in Australia and New Zealand. This was the beginning of over a decade of collecting and processing tweets to help emergency response agencies and crisis coordination centres use social media content as a new channel of information to support their work practices and to engage with the community impacted by emergency events. This journey has seen numerous challenges overcome to continuously maintain a tweet stream for an operational system. This experience allows us to derive insights into the changing use of Twitter over this time. In this paper we present some of the lessons we’ve learned from maintaining a Twitter monitoring system for emergency management use cases and we provide some insights into the changing nature of Twitter usage by users over this period. |
| Address |
CSIRO Data61; CSIRO Data61; CSIRO Data61 |
| Corporate Author |
|
Thesis |
|
| Publisher |
Massey Unversity |
Place of Publication |
Palmerston North, New Zealand |
Editor |
Thomas J. Huggins, V.L. |
| Language |
English |
Summary Language |
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Original Title |
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| Series Editor |
|
Series Title |
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Abbreviated Series Title |
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| 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 |
2498 |
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| Author |
Dilini Rajapaksha; Kacper Sokol; Jeffrey Chan; Flora Salim; Mukesh Prasad; Mahendra Samarawickrama |
| Title |
Analysing Donors’ Behaviour in Non-profit Organisations for Disaster Resilience |
Type |
Conference Article |
| Year |
2023 |
Publication |
Proceedings of the ISCRAM Asia Pacific Conference 2022 |
Abbreviated Journal |
Proc. ISCRAM AP 2022 |
| Volume |
|
Issue |
|
Pages |
258-267 |
| Keywords |
Disaster Response; Social Media; Donors’ Behaviour; Australian Bushfires |
| Abstract |
With the advancement and proliferation of technology, non-profit organisations have embraced social media platforms to improve their operational capabilities through brand advocacy, among many other strategies. The effect of such social media campaigns on these institutions, however, remains largely underexplored, especially during disaster periods. This work introduces and applies a quantitative investigative framework to understand how social media influence the behaviour of donors and their usage of these platforms throughout (natural) disasters. More specifically, we explore how on-line engagement – as captured by Facebook interactions and Google search trends – corresponds to the donors’ behaviour during the catastrophic 2019–2020 Australian bushfire season. To discover this relationship, we analyse the record of donations made to the Australian Red Cross throughout this period. Our exploratory study reveals that social media campaigns are effective in encouraging on-line donations made via a dedicated website. We also compare this mode of giving to more regular, direct deposit gifting. |
| Address |
RMIT University; RMIT University; RMIT University; UNSW Sydney; University of Technology Sydney; Australian Red Cross |
| Corporate Author |
|
Thesis |
|
| Publisher |
Massey Unversity |
Place of Publication |
Palmerston North, New Zealand |
Editor |
Thomas J. Huggins, V.L. |
| Language |
English |
Summary Language |
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Original Title |
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| Series Editor |
|
Series Title |
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Abbreviated Series Title |
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| Series Volume |
|
Series Issue |
|
Edition |
|
| ISSN |
2411-3387 |
ISBN |
978-0-473-66845-7 |
Medium |
|
| Track |
Social Media for Disaster Response |
Expedition |
|
Conference |
|
| Notes |
|
Approved |
no |
| Call Number |
ISCRAM @ idladmin @ |
Serial |
2499 |
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| |
| Author |
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 |
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Original Title |
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| Series Editor |
|
Series Title |
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Abbreviated Series Title |
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| Series Volume |
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Series Issue |
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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 |
Yu, X.; Chen, J.; Liu, J. |
| Title |
Examining the influence of social media on individual’s protective action taking during Covid-19 in China |
Type |
Conference Article |
| Year |
2023 |
Publication |
Proceedings of the 20th International ISCRAM Conference |
Abbreviated Journal |
Iscram 2023 |
| Volume |
|
Issue |
|
Pages |
295-308 |
| Keywords |
Public Crisis; Social Mediated Crisis Communication Model; Risk Perception; Protective Action |
| Abstract |
In the context of COVID-19, this study utilizes the Social Mediated Crisis Communication Model (SMCC) and the Protective Action Decision Model (PADM) to investigate the relationship between social media users' protective actions and crisis information during public health crises in China. By constructing a structural equation model, this study aims to identify the influencing factors that affect social media users' personal’s cognitive, emotional, and behavioral reactions given crisis relevant information. Results findings are that warning information can significantly increase risk perception; emotional responses are not significantly affected by warning information and risk perception; risk perception has a negative impact on information gathering and sharing behavior; risk perception has a significant mediating effect on the relationship between information features and protective action. |
| Address |
University of International Business and Economics |
| 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 |
2411-3387 |
ISBN |
979-8-218-21749-5 |
Medium |
|
| Track |
Social Media for Crisis Management |
Expedition |
|
Conference |
|
| Notes |
http://dx.doi.org/10.59297/HPVH6600 |
Approved |
no |
| Call Number |
ISCRAM @ idladmin @ |
Serial |
2527 |
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| Author |
Long, Z.; McCreadiem, R.; Imran, M. |
| Title |
CrisisViT: A Robust Vision Transformer for Crisis Image Classification |
Type |
Conference Article |
| Year |
2023 |
Publication |
Proceedings of the 20th International ISCRAM Conference |
Abbreviated Journal |
Iscram 2023 |
| Volume |
|
Issue |
|
Pages |
309-319 |
| Keywords |
Social Media Classification; Crisis Management; Deep Learning; Vision Transformers; Supervised Learning |
| Abstract |
In times of emergency, crisis response agencies need to quickly and accurately assess the situation on the ground in order to deploy relevant services and resources. However, authorities often have to make decisions based on limited information, as data on affected regions can be scarce until local response services can provide first-hand reports. Fortunately, the widespread availability of smartphones with high-quality cameras has made citizen journalism through social media a valuable source of information for crisis responders. However, analyzing the large volume of images posted by citizens requires more time and effort than is typically available. To address this issue, this paper proposes the use of state-of-the-art deep neural models for automatic image classification/tagging, specifically by adapting transformer-based architectures for crisis image classification (CrisisViT). We leverage the new Incidents1M crisis image dataset to develop a range of new transformer-based image classification models. Through experimentation over the standard Crisis image benchmark dataset, we demonstrate that the CrisisViT models significantly outperform previous approaches in emergency type, image relevance, humanitarian category, and damage severity classification. Additionally, we show that the new Incidents1M dataset can further augment the CrisisViT models resulting in an additional 1.25% absolute accuracy gain. |
| Address |
University of Glasgow |
| 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/SDSM9194 |
Approved |
no |
| Call Number |
ISCRAM @ idladmin @ |
Serial |
2528 |
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| Author |
McCreadie, R.; Buntain, C. |
| Title |
CrisisFACTS: Buidling and Evaluating Crisis Timelines |
Type |
Conference Article |
| Year |
2023 |
Publication |
Proceedings of the 20th International ISCRAM Conference |
Abbreviated Journal |
Iscram 2023 |
| Volume |
|
Issue |
|
Pages |
320-339 |
| Keywords |
Emergency Management; Crisis Informatics News; Twitter; Facebook; Reddit; Wikipedia; Summarization |
| Abstract |
Between 2018 and 2021, the Incident Streams track (TREC-IS) developed standard approaches for classifying information types and criticality of tweets during crises. While successful in producing substantial collections of labeled data, TREC-IS as a data challenge had several limitations: It only evaluated information at type-level rather than what was reported; it only used Twitter data; and it lacked measures of redundancy in system output. This paper introduces Crisis Facts and Cross-Stream Temporal Summarization (CrisisFACTS), a new data challenge piloted in 2022 and developed to address these limitations. The CrisisFACTS framework recasts TREC-IS into an event-summarization task using multiple disaster-relevant data streams and a new fact-based evaluation scheme, allowing the community to assess state-of-the-art methods for summarizing disaster events Results from CrisisFACTS in 2022 include a new test-collection comprising human-generated disaster summaries along with multi-platform datasets of social media, crisis reports and news coverage for major crisis events. |
| Address |
University of Glasgow; University of Maryland, College Park (UMD) |
| 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/JVQZ9405 |
Approved |
no |
| Call Number |
ISCRAM @ idladmin @ |
Serial |
2529 |
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| Author |
Encarnación, T.; Wilks, C.R. |
| Title |
Role of Expressed Emotions on the Retransmission of Help-Seeking Messages during Disasters |
Type |
Conference Article |
| Year |
2023 |
Publication |
Proceedings of the 20th International ISCRAM Conference |
Abbreviated Journal |
Iscram 2023 |
| Volume |
|
Issue |
|
Pages |
340-352 |
| Keywords |
Social Amplification; Retweet Prediction; Crisis Informatics |
| Abstract |
Emergency managers rely on formal and informal communication channels to identify needs in post-disaster environments. Message retransmission is a critical factor to ensure that help-seekers are identified by disaster responders. This paper uses a novel annotated dataset of Twitter posts from four major disasters that impacted the United States in 2021, to quantify the effect that expressed emotions and support typology have on retransmission. Poisson regression models are estimated, and the results show that messages seeking instrumental support are more likely to be retransmitted. Expressions of anger, fear, and sadness increase overall retweets. Moreover, expressions of anger, anticipation, or sadness increase the likelihood of retransmission for messages that seek instrumental help. |
| Address |
College of Business Administration University of Missouri-St |
| Corporate Author |
|
Thesis |
|
| Publisher |
University of Nebraska at Omaha |
Place of Publication |
Omaha, USA |
Editor |
Jaziar Radianti; Ioannis Dokas; Nicolas Lalone; Deepak Khazanchi |
| Language |
English |
Summary Language |
|
Original Title |
|
| Series Editor |
Hosssein Baharmand |
Series Title |
|
Abbreviated Series Title |
|
| Series Volume |
|
Series Issue |
|
Edition |
1 |
| ISSN |
|
ISBN |
|
Medium |
|
| Track |
Social Media for Crisis Management |
Expedition |
|
Conference |
|
| Notes |
http://dx.doi.org/10.59297/DDXJ4655 |
Approved |
no |
| Call Number |
ISCRAM @ idladmin @ |
Serial |
2530 |
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| Author |
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 |
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| 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 |
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| Author |
Pereira, J.; Fidalgo, R.; Lotufo, R.; Nogueira, R. |
| Title |
Crisis Event Social Media Summarization with GPT-3 and Neural Reranking |
Type |
Conference Article |
| Year |
2023 |
Publication |
Proceedings of the 20th International ISCRAM Conference |
Abbreviated Journal |
Iscram 2023 |
| Volume |
|
Issue |
|
Pages |
371-384 |
| Keywords |
Crisis Management; Social Media; Multi-Document Summarization; Query-Based Summarization. |
| Abstract |
Managing emergency events, such as natural disasters, requires management teams to have an up-to-date view of what is happening throughout the event. In this paper, we demonstrate how a method using a state-of-the-art open-sourced search engine and a large language model can generate accurate and comprehensive summaries by retrieving information from social media and online news sources. We evaluated our method on the TREC CrisisFACTS challenge dataset using automatic summarization metrics (e.g., Rouge-2 and BERTScore) and the manual evaluation performed by the challenge organizers. Our approach is the best in comprehensiveness despite presenting a high redundancy ratio in the generated summaries. In addition, since all pipeline components are few-shot, there is no need to collect training data, allowing us to deploy the system rapidly. Code is available at https://github.com/neuralmind-ai/visconde-crisis-summarization. |
| Address |
Centro de Inform´atica, Universidade Federal de Pernambuco; NeuralMind |
| 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 |
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| Series Volume |
|
Series Issue |
|
Edition |
1 |
| ISSN |
|
ISBN |
|
Medium |
|
| Track |
Social Media for Crisis Management |
Expedition |
|
Conference |
|
| Notes |
http://dx.doi.org/10.59297/JJYT4136 |
Approved |
no |
| Call Number |
ISCRAM @ idladmin @ |
Serial |
2532 |
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| Author |
Zou, H.P.; Caragea, C.; Zhou, Y.; Caragea, D. |
| Title |
Semi-Supervised Few-Shot Learning for Fine-Grained Disaster Tweet Classification |
Type |
Conference Article |
| Year |
2023 |
Publication |
Proceedings of the 20th International ISCRAM Conference |
Abbreviated Journal |
Iscram 2023 |
| Volume |
|
Issue |
|
Pages |
385-395 |
| Keywords |
Crisis Tweet Classification; Semi-Supervised Few-Shot Learning; Pseudo-Labeling; TextMixUp. |
| Abstract |
The shared real-time information about natural disasters on social media platforms like Twitter and Facebook plays a critical role in informing volunteers, emergency managers, and response organizations. However, supervised learning models for monitoring disaster events require large amounts of annotated data, making them unrealistic for real-time use in disaster events. To address this challenge, we present a fine-grained disaster tweet classification model under the semi-supervised, few-shot learning setting where only a small number of annotated data is required. Our model, CrisisMatch, effectively classifies tweets into fine-grained classes of interest using few labeled data and large amounts of unlabeled data, mimicking the early stage of a disaster. Through integrating effective semi-supervised learning ideas and incorporating TextMixUp, CrisisMatch achieves performance improvement on two disaster datasets of 11.2% on average. Further analyses are also provided for the influence of the number of labeled data and out-of-domain results. |
| Address |
University of Illinois Chicago; Kansas State 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 |
2411-3387 |
ISBN |
979-8-218-21749-5 |
Medium |
|
| Track |
Social Media for Crisis Management |
Expedition |
|
Conference |
|
| Notes |
http://dx.doi.org/10.59297/FWXE4933 |
Approved |
no |
| Call Number |
ISCRAM @ idladmin @ |
Serial |
2533 |
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| Author |
Fatehkia, M.; Imran, M.; Weber, I. |
| Title |
Towards Real-time Remote Social Sensing via Targeted Advertising |
Type |
Conference Article |
| Year |
2023 |
Publication |
Proceedings of the 20th International ISCRAM Conference |
Abbreviated Journal |
Iscram 2023 |
| Volume |
|
Issue |
|
Pages |
396-406 |
| Keywords |
Remote Social Sensing; Real-Time Polling; Flood Mapping; Facebook Advertising |
| Abstract |
Social media serves as an important communication channel for people affected by crises, creating a data source for emergency responders wanting to improve situational awareness. In particular, social listening on Twitter has been widely used for real-time analysis of crisis-related messages. This approach, however, is often hindered by the small fraction of (hyper-)localized content and by the inability to explicitly ask affected populations about aspects with the most operational value. Here, we explore a new form of social media data collected through targeted poll ads on Facebook. Using geo-targeted ads during flood events in six countries, we show that it is possible to collect thousands of poll responses within hours of launching the ad campaign, and at a cost of a few (US dollar) cents per response. We believe that this flexible, fast, and affordable data collection can serve as a valuable complement to existing approaches. |
| Address |
Qatar Computing Research Institute; Qatar Computing Research Institute; Saarland Informatics Campus |
| Corporate Author |
|
Thesis |
|
| Publisher |
University of Nebraska at Omaha |
Place of Publication |
Omaha, USA |
Editor |
Jaziar Radianti; Ioannis Dokas; Nicolas Lalone; Deepak Khazanchi |
| Language |
English |
Summary Language |
|
Original Title |
|
| Series Editor |
Hosssein Baharmand |
Series Title |
|
Abbreviated Series Title |
|
| Series Volume |
|
Series Issue |
|
Edition |
1 |
| ISSN |
|
ISBN |
|
Medium |
|
| Track |
Social Media for Crisis Management |
Expedition |
|
Conference |
|
| Notes |
http://dx.doi.org/10.59297/NEFN8739 |
Approved |
no |
| Call Number |
ISCRAM @ idladmin @ |
Serial |
2534 |
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| |
| Author |
Rode-Hasinger, S.; Haberle, M.; Racek, D.; Kruspe, A.; Zhu Xiao Xiang |
| Title |
TweEvent: A dataset of Twitter messages about events in the Ukraine conflict |
Type |
Conference Article |
| Year |
2023 |
Publication |
Proceedings of the 20th International ISCRAM Conference |
Abbreviated Journal |
Iscram 2023 |
| Volume |
|
Issue |
|
Pages |
407-416 |
| Keywords |
Conflict; Ukraine; Dataset; Social Media; NLP |
| Abstract |
Information about incidents within a conflict, e.g., shelling of an area of interest, is scattered amongst different data or media sources. For example, the ACLED dataset continuously documents local incidents recorded within the context of a specific conflict such as Russia’s war in Ukraine. However, these blocks of information might be incomplete. Therefore, it is useful to collect data from several sources to enrich the information pool of a certain incident. In this paper, we present a dataset of social media messages covering the same war events as those collected in the ACLED dataset. The information is extracted from automatically geocoded Twitter text data using state-of-the-art natural language processing methods based on large pre-trained language models (LMs). Our method can be applied to various textual data sources. Both the data as well as the approach can serve to help human analysts obtain a broader understanding of conflict events. |
| Address |
Technical University of Munich; Technical University of Munich; Ludwig-Maximilians-Universitat M¨unchen; Technische Hochschule N¨urnberg; Technical University of Munich |
| 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/AIDF1102 |
Approved |
no |
| Call Number |
ISCRAM @ idladmin @ |
Serial |
2535 |
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| |
| Author |
Chauhan, A. |
| Title |
Humor-Based COVID-19 Twitter Accounts |
Type |
Conference Article |
| Year |
2023 |
Publication |
Proceedings of the 20th International ISCRAM Conference |
Abbreviated Journal |
Iscram 2023 |
| Volume |
|
Issue |
|
Pages |
417-427 |
| Keywords |
COVID-19; Twitter; Humor; Crisis Named Resources |
| Abstract |
Crisis Named Resources (or CNRs) are social media pages and accounts named after a crisis event. Using the COVID-19 Pandemic as a case study, we identified and examined the role of CNRs that shared humor on Twitter. Our analyses showed that humor-based CNRs shared virus-related rumors, stigma, safety measures, opinions, sarcasm, and news updates. These resources also shared the overall anger and frustration over the year 2020. We conclude by discussing the critical role of humor based CNRs in crisis response. |
| Address |
Concordia University of Edmonton |
| 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/YHDI4576 |
Approved |
no |
| Call Number |
ISCRAM @ idladmin @ |
Serial |
2536 |
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| 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 |
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| Author |
Herrera, L.C.; Gjøsæter, T. |
| Title |
Leveraging Crisis Informatics Experts: A co-creating approach for validation of social media research insights |
Type |
Conference Article |
| Year |
2023 |
Publication |
Proceedings of the 20th International ISCRAM Conference |
Abbreviated Journal |
Iscram 2023 |
| Volume |
|
Issue |
|
Pages |
439-448 |
| Keywords |
Card Sorting Workshop; Practice-Based Research; Crisis Informatics; Support Information System; Validation. |
| Abstract |
Validation of findings is a challenge in practice-based research. While analysis is being conducted and findings are being constructed out of data collected in a defined period, practitioners continue with their activities. This issue is exacerbated in the field of crisis management, where high volatility and personnel turnover make the capacity to attend research demands scarce. Therefore, conducting classic member validation is logistically challenging for the researcher. The need for rigor and validity calls for alternative mechanisms to fulfill requirements for academic research. This article presents an approach for validation of results of a qualitative study with public organizations that use social media as a source of information in the context of crisis management. The unavailability of original interview-objects to validate our findings resulted in an alternative validation method that leveraged experts in crisis informatics. By presenting our approach, we contribute to encouraging rigor in qualitative research while maintaining the relationship between practice and academia. |
| Address |
University of Agder; University of Agder |
| 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/MHCV5804 |
Approved |
no |
| Call Number |
ISCRAM @ idladmin @ |
Serial |
2538 |
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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 |
Nurollahian, S.; Talegaonkar, I.; Bell, A.Z.; Kogan, M. |
| Title |
Factors Affecting Public’s Engagement with Tweets by Authoritative Sources During Crisis |
Type |
Conference Article |
| Year |
2023 |
Publication |
Proceedings of the 20th International ISCRAM Conference |
Abbreviated Journal |
Iscram 2023 |
| Volume |
|
Issue |
|
Pages |
459-477 |
| Keywords |
Crisis Informatics; Social Media; Public Engagement; Authoritative Sources; Topic Modeling |
| Abstract |
People increasingly use social media at the time of crisis, which produces a social media data deluge, where the public may find it difficult to locate trustworthy and credible information. Therefore, they often turn to authoritative sources: official individuals and organizations who are trusted to provide reliable information. It is then imperative that their credible messages reach and engage the widest possible audience, especially among those affected. In this study, we explore the role of metadata and linguistic factors in facilitating three types of engagement — retweets, replies, and favorites— with posts by authoritative sources. We find that many factors are similarly important across models (popularity, sociability, activity). However, some features are salient for only a specific type of engagement. We conclude by providing guidance to authoritative sources on how they may optimize specific types of engagement: retweets for information propagation, replies for in-depth sense-making, and favorites for cross-purpose visibility. |
| Address |
University of Utah; University of Utah; University of Utah; University of Utah |
| 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/AVHJ5856 |
Approved |
no |
| Call Number |
ISCRAM @ idladmin @ |
Serial |
2540 |
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| Author |
Cruz, J.A. dela; Hendrickx, I.; Larson, M. |
| Title |
Towards XAI for Information Extraction on Online Media Data for Disaster Risk Management |
Type |
Conference Article |
| Year |
2023 |
Publication |
Proceedings of the 20th International ISCRAM Conference |
Abbreviated Journal |
Iscram 2023 |
| Volume |
|
Issue |
|
Pages |
478-486 |
| Keywords |
Disaster Risk Management; Information Extraction; Explainable AI (XAI); Explainabilit |
| Abstract |
Disaster risk management practitioners have the responsibility to make decisions at every phase of the disaster risk management cycle: mitigation, preparedness, response and recovery. The decisions they make affect human life. In this paper, we consider the current state of the use of AI in information extraction (IE) for disaster risk management (DRM), which makes it possible to leverage disaster information in social media. We consolidate the challenges and concerns of using AI for DRM into three main areas: limitations of DRM data, limitations of AI modeling and DRM domain-specific concerns, i.e., bias, privacy and security, transparency and accountability, and hype and inflated expectations. Then, we present a systematic discussion of how explainable AI (XAI) can address the challenges and concerns of using AI for IE in DRM. |
| Address |
Centre for Language Studies, Centre for Language and Speech Technology, Radboud University; Centre for Language Studies, Centre for Language and Speech Technology, Radboud University; Centre for Language Studies,Inst. for Computing and Information Sciences,Radboud University |
| Corporate Author |
|
Thesis |
|
| Publisher |
University of Nebraska at Omaha |
Place of Publication |
Omaha, USA |
Editor |
Jaziar Radianti; Ioannis Dokas; Nicolas Lalone; Deepak Khazanchi |
| Language |
English |
Summary Language |
|
Original Title |
|
| Series Editor |
Hosssein Baharmand |
Series Title |
|
Abbreviated Series Title |
|
| Series Volume |
|
Series Issue |
|
Edition |
1 |
| ISSN |
|
ISBN |
|
Medium |
|
| Track |
Social Media for Crisis Management |
Expedition |
|
Conference |
|
| Notes |
http://dx.doi.org/10.59297/BHAE3912 |
Approved |
no |
| Call Number |
ISCRAM @ idladmin @ |
Serial |
2541 |
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| Author |
Wang, D.; Kogan, M. |
| Title |
Resonance+: Augmenting Collective Attention to Find Information on Public Cognition and Perception of Risk |
Type |
Conference Article |
| Year |
2023 |
Publication |
Proceedings of the 20th International ISCRAM Conference |
Abbreviated Journal |
Iscram 2023 |
| Volume |
|
Issue |
|
Pages |
487-500 |
| Keywords |
Crisis Informatics; Social Media Data; Word Embedding; Cognitive Process; Protective Action Decision Model |
| Abstract |
Microblogging platforms have been increasingly used by the public and crisis managers in crisis. The increasing volume of data has made such platforms more difficult for officials to find on-the-ground information and understand the public’s perception of the evolving risks. The crisis informatics literature has proposed various technological solutions to find relevant information from social media. However, the cognitive processes of the affected population and their subsequent responses, such as perceptions, emotional and behavioral responses, are still under-examined at scale. Yet, such information is important for gauging public perception of risks, an important task for PIOs and emergency managers. In this work, we leverage the noise-cutting power of collective attention and take cues from the Protective Action Decision Model, to propose a method that estimates shifts in collective attention with a special focus on the cognitive processes of those affected and their subsequent responses. |
| Address |
University of Utah, School of Computing; University of Utah, School of Computing |
| 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 |
2411-3387 |
ISBN |
979-8-218-21749-5 |
Medium |
|
| Track |
Social Media for Crisis Management |
Expedition |
|
Conference |
|
| Notes |
http://dx.doi.org/10.59297/IMVX7820 |
Approved |
no |
| Call Number |
ISCRAM @ idladmin @ |
Serial |
2542 |
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| Author |
Dario Salza; Edoardo Arnaudo; Giacomo Blanco; Claudio Rossi |
| Title |
A 'Glocal' Approach for Real-time Emergency Event Detection in Twitter |
Type |
Conference Article |
| Year |
2022 |
Publication |
ISCRAM 2022 Conference Proceedings – 19th International Conference on Information Systems for Crisis Response and Management |
Abbreviated Journal |
Iscram 2022 |
| Volume |
|
Issue |
|
Pages |
570-583 |
| Keywords |
Emergency; Event Detection; Social Media; Twitter; Incremental Clustering |
| Abstract |
Social media like Twitter offer not only an unprecedented amount of user-generated content covering developing emergencies but also act as a collector of news produced by heterogeneous sources, including big and small media companies as well as public authorities. However, this volume, velocity, and variety of data constitute the main value and, at the same time, the key challenge to implement and automatic detection and tracking of independent emergency events from the real-time stream of tweets. Leveraging online clustering and considering both textual and geographical features, we propose, implement, and evaluate an algorithm to automatically detect emergency events applying a ‘glocal’ approach, i.e., offering a global coverage while detecting events at local (municipality level) scale. |
| Address |
LINKS Foundation; LINKS Foundation; LINKS Foundation; LINKS Foundation |
| Corporate Author |
|
Thesis |
|
| Publisher |
|
Place of Publication |
Tarbes, France |
Editor |
Rob Grace; Hossein Baharmand |
| Language |
English |
Summary Language |
|
Original Title |
|
| Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
| Series Volume |
|
Series Issue |
|
Edition |
|
| ISSN |
2411-3387 |
ISBN |
978-82-8427-099-9 |
Medium |
|
| Track |
Social Media for Crisis Management |
Expedition |
|
Conference |
|
| Notes |
|
Approved |
no |
| Call Number |
ISCRAM @ idladmin @ |
Serial |
2440 |
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| Author |
Cody Buntain; Richard Mccreadie; Ian Soboroff |
| Title |
Incident Streams 2021 Off the Deep End: Deeper Annotations and Evaluations 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 |
584-604 |
| Keywords |
Emergency Management; Crisis Informatics; Twitter; Categorization; Priorization; Multi-Modal; Public Safety; PSCR; TREC |
| Abstract |
This paper summarizes the final year of the four-year Text REtrieval Conference Incident Streams track (TREC-IS), which has produced a large dataset comprising 136,263 annotated tweets, spanning 98 crisis events. Goals of this final year were twofold: 1) to add new categories for assessing messages, with a focus on characterizing the audience, author, and images associated with these messages, and 2) to enlarge the TREC-IS dataset with new events, with an emphasis of deeper pools for sampling. Beyond these two goals, TREC-IS has nearly doubled the number of annotated messages per event for the 26 crises introduced in 2021 and has released a new parallel dataset of 312,546 images associated with crisis content – with 7,297 tweets having annotations about their embedded images. Our analyses of this new crisis data yields new insights about the context of a tweet; e.g., messages intended for a local audience and those that contain images of weather forecasts and infographics have higher than average assessments of priority but are relatively rare. Tweets containing images, however, have higher perceived priorities than tweets without images. Moving to deeper pools, while tending to lower classification performance, also does not generally impact performance rankings or alter distributions of information-types. We end this paper with a discussion of these datasets, analyses, their implications, and how they contribute both new data and insights to the broader crisis informatics community. |
| Address |
University of Maryland, College Park (UMD); University of Glasgow; National Institute of Standards and Technology (NIST) |
| 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 |
2441 |
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| 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 |
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| Author |
Thomas Papadimos; Nick Pantelidis; Stelios Andreadis; Aristeidis Bozas; Ilias Gialampoukidis; Stefanos Vrochidis; Ioannis Kompatsiaris |
| Title |
Real-time Alert Framework for Fire Incidents Using Multimodal Event Detection on Social Media Streams |
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 |
623-635 |
| Keywords |
Alert framework; social media; event detection; kernel density estimation; community detection |
| Abstract |
The frequency of wildfires is growing day by day due to vastly climate changes. Forest fires can have a severe impact on human lives and the environment, which can be minimised if the population has early and accurate warning mechanisms. To date, social media are able to contribute to early warning with the additional, crowd-sourced information they can provide to the emergency response workers during a crisis event. Nevertheless, the detection of real-world fire incidents using social media data, while filtering out the unavoidable noise, remains a challenging task. In this paper, we present an alert framework for the real-time detection of fire events and we propose a novel multimodal event detection model, which fuses both probabilistic and graph methodologies and is evaluated on the largest fires in Spain during 2019. |
| Address |
Centre for Research & Technology Hellas Information Technologies Institute Thessaloniki, Greece;Centre for Research & Technology Hellas Information Technologies Institute Thessaloniki, Greece;Centre for Research & Technology Hellas Information Technologie |
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| Publisher |
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Place of Publication |
Tarbes, France |
Editor |
Rob Grace; Hossein Baharmand |
| Language |
English |
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Edition |
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| ISSN |
2411-3387 |
ISBN |
978-82-8427-099-9 |
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| Track |
Social Media for Crisis Management |
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| Notes |
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Approved |
no |
| Call Number |
ISCRAM @ idladmin @ |
Serial |
2443 |
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