| Records |
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 |
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Thesis |
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| Publisher |
University of Nebraska at Omaha |
Place of Publication |
Omaha, USA |
Editor |
Jaziar Radianti; Ioannis Dokas; Nicolas Lalone; Deepak Khazanchi |
| Language |
English |
Summary Language |
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Original Title |
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| Series Editor |
Hosssein Baharmand |
Series Title |
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Abbreviated Series Title |
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| Series Volume |
|
Series Issue |
|
Edition |
1 |
| ISSN |
2411-3387 |
ISBN |
979-8-218-21749-5 |
Medium |
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| Track |
Social Media for Crisis Management |
Expedition |
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Conference |
|
| Notes |
http://dx.doi.org/10.59297/IMVX7820 |
Approved |
no |
| Call Number |
ISCRAM @ idladmin @ |
Serial |
2542 |
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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 |
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Thesis |
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| Publisher |
University of Nebraska at Omaha |
Place of Publication |
Omaha, USA |
Editor |
Jaziar Radianti; Ioannis Dokas; Nicolas Lalone; Deepak Khazanchi |
| Language |
English |
Summary Language |
|
Original Title |
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| Series Editor |
Hosssein Baharmand |
Series Title |
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Abbreviated Series Title |
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| Series Volume |
|
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 |
Zijun Long; Richard McCreadie |
| Title |
Is Multi-Modal Data Key for Crisis Content Categorization on Social Media? |
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 |
1068-1080 |
| Keywords |
Social Media Classification; Multi-modal Learning; Crisis Management; Deep Learning, BERT; Supervised Learning |
| Abstract |
The user-base of social media platforms, like Twitter, has grown dramatically around the world over the last decade. As people post everything they experience on social media, large volumes of valuable multimedia content are being recorded online, which can be analysed to help for a range of tasks. Here we specifically focus on crisis response. The majority of prior works in this space focus on using machine learning to categorize single-modality content (e.g. text of the posts, or images shared), with few works jointly utilizing multiple modalities. Hence, in this paper, we examine to what extent integrating multiple modalities is important for crisis content categorization. In particular, we design a pipeline for multi-modal learning that fuses textual and visual inputs, leverages both, and then classifies that content based on the specified task. Through evaluation using the CrisisMMD dataset, we demonstrate that effective automatic labelling for this task is possible, with an average of 88.31% F1 performance across two significant tasks (relevance and humanitarian category classification). while also analysing cases that unimodal models and multi-modal models success and fail. |
| Address |
University of Glasgow; University of Glasgow |
| Corporate Author |
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Thesis |
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| Publisher |
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Place of Publication |
Tarbes, France |
Editor |
Rob Grace; Hossein Baharmand |
| Language |
English |
Summary Language |
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Original Title |
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| Series Editor |
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Series Title |
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Abbreviated Series Title |
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| Series Volume |
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Series Issue |
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Edition |
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| ISSN |
2411-3387 |
ISBN |
978-82-8427-099-9 |
Medium |
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| Track |
Social Media for Crisis Management |
Expedition |
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Conference |
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| Notes |
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Approved |
no |
| Call Number |
ISCRAM @ idladmin @ |
Serial |
2472 |
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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 |
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Thesis |
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| Publisher |
University of Nebraska at Omaha |
Place of Publication |
Omaha, USA |
Editor |
Jaziar Radianti; Ioannis Dokas; Nicolas Lalone; Deepak Khazanchi |
| Language |
English |
Summary Language |
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Original Title |
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| Series Editor |
Hosssein Baharmand |
Series Title |
|
Abbreviated Series Title |
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| 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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