18 research outputs found
Social Media Analysis in Crisis Situations: Can Social Media be a Reliable Information Source for Emergency Management Services?
Learning and understanding what happened before, during, and after a crisis is extremely important for the improvement of the response process. For this purpose, social media has become an important communication medium used by both the affected persons and the emergency management services (EMSs). However, in different crises, different information may be needed, and the information shared in social media varies in its usefulness: It could be highly critical or completely irrelevant to the rescue operation. Supplying the best possible up-to-date information is crucial to the EMS, whose actions based on that information may save lives and resources. This paper studies a particular use case of extreme weather in Norway and identifies the information needs, the problem faced by EMSs, and how they use social media. It, further, pinpoints what different social media analysis platforms can provide in this type of crisis. The results of the research are criteria that social media analysis should follow to address EMSs\u27 concerns. The output of this work can be used to more precisely describe social media communication for crises and to design more efficient platforms for information retrieval from social media
Recommended from our members
Verifying baselines for crisis event information classification on Twitter
Social media are rich information sources during and in the aftermath of crisis events such as earthquakes and terrorist attacks. Despite myriad challenges, with the right tools, significant insight can be gained which can assist emergency responders and related applications. However, most extant approaches are incomparable, using bespoke definitions, models, datasets and even evaluation metrics. Furthermore, it is rare that code, trained models, or exhaustive parametrisation details are made openly available. Thus, even confirmation of self-reported performance is problematic; authoritatively determining the state of the art (SOTA) is essentially impossible. Consequently, to begin addressing such endemic ambiguity, this paper seeks to make 3 contributions: 1) the replication and results confirmation of a leading (and generalisable) technique; 2) testing straightforward modifications of the technique likely to improve performance; and 3) the extension of the technique to a novel and complimentary type of crisis-relevant information to demonstrate it’s generalisability
Unsupervised Detection of Sub-events in Large Scale Disasters
Social media plays a major role during and after major natural disasters
(e.g., hurricanes, large-scale fires, etc.), as people ``on the ground'' post
useful information on what is actually happening. Given the large amounts of
posts, a major challenge is identifying the information that is useful and
actionable. Emergency responders are largely interested in finding out what
events are taking place so they can properly plan and deploy resources. In this
paper we address the problem of automatically identifying important sub-events
(within a large-scale emergency ``event'', such as a hurricane). In particular,
we present a novel, unsupervised learning framework to detect sub-events in
Tweets for retrospective crisis analysis. We first extract noun-verb pairs and
phrases from raw tweets as sub-event candidates. Then, we learn a semantic
embedding of extracted noun-verb pairs and phrases, and rank them against a
crisis-specific ontology. We filter out noisy and irrelevant information then
cluster the noun-verb pairs and phrases so that the top-ranked ones describe
the most important sub-events. Through quantitative experiments on two large
crisis data sets (Hurricane Harvey and the 2015 Nepal Earthquake), we
demonstrate the effectiveness of our approach over the state-of-the-art. Our
qualitative evaluation shows better performance compared to our baseline.Comment: AAAI-20 Social Impact Trac
NARMADA: Need and Available Resource Managing Assistant for Disasters and Adversities
Although a lot of research has been done on utilising Online Social Media
during disasters, there exists no system for a specific task that is critical
in a post-disaster scenario -- identifying resource-needs and
resource-availabilities in the disaster-affected region, coupled with their
subsequent matching. To this end, we present NARMADA, a semi-automated platform
which leverages the crowd-sourced information from social media posts for
assisting post-disaster relief coordination efforts. The system employs Natural
Language Processing and Information Retrieval techniques for identifying
resource-needs and resource-availabilities from microblogs, extracting
resources from the posts, and also matching the needs to suitable
availabilities. The system is thus capable of facilitating the judicious
management of resources during post-disaster relief operations.Comment: ACL 2020 Workshop on Natural Language Processing for Social Media
(SocialNLP
The role of social media in the process of informing the public about disaster risks
Social media informs the public about the most important events and conveys important information. Before, during, and after disasters, social media are used to disseminate information about disasters and collect data relevant to the implementation of preparedness, response, and recovery activities and measures. Social networks are effective in disseminating information and warnings, as well as in educating the public. The subject of the research is examining the influence of demographic factors on the effectiveness of social media in informing the public about the risks of disasters. Using an online survey questionnaire and according to the snowball principle, a survey of 247 respondents was conducted in 2022. The research results show no statistically significant relationship between the respondents’ education level and the assessment of the effectiveness of social media reporting on disasters. Using social media can improve communication between stakeholders in disaster management and facilitate coordination of efforts, fostering communication and allocation of resources. To effectively use social media in disaster management, decision-makers in the disaster management system must be aware of new technologies, their disadvantages and advantages, and ways to collect and analyze data from social networks
Social Media Analytics in Disaster Response: A Comprehensive Review
Social media has emerged as a valuable resource for disaster management,
revolutionizing the way emergency response and recovery efforts are conducted
during natural disasters. This review paper aims to provide a comprehensive
analysis of social media analytics for disaster management. The abstract begins
by highlighting the increasing prevalence of natural disasters and the need for
effective strategies to mitigate their impact. It then emphasizes the growing
influence of social media in disaster situations, discussing its role in
disaster detection, situational awareness, and emergency communication. The
abstract explores the challenges and opportunities associated with leveraging
social media data for disaster management purposes. It examines methodologies
and techniques used in social media analytics, including data collection,
preprocessing, and analysis, with a focus on data mining and machine learning
approaches. The abstract also presents a thorough examination of case studies
and best practices that demonstrate the successful application of social media
analytics in disaster response and recovery. Ethical considerations and privacy
concerns related to the use of social media data in disaster scenarios are
addressed. The abstract concludes by identifying future research directions and
potential advancements in social media analytics for disaster management. The
review paper aims to provide practitioners and researchers with a comprehensive
understanding of the current state of social media analytics in disaster
management, while highlighting the need for continued research and innovation
in this field.Comment: 11 page
A Tutorial on Event Detection using Social Media Data Analysis: Applications, Challenges, and Open Problems
In recent years, social media has become one of the most popular platforms
for communication. These platforms allow users to report real-world incidents
that might swiftly and widely circulate throughout the whole social network. A
social event is a real-world incident that is documented on social media.
Social gatherings could contain vital documentation of crisis scenarios.
Monitoring and analyzing this rich content can produce information that is
extraordinarily valuable and help people and organizations learn how to take
action. In this paper, a survey on the potential benefits and applications of
event detection with social media data analysis will be presented. Moreover,
the critical challenges and the fundamental tradeoffs in event detection will
be methodically investigated by monitoring social media stream. Then,
fundamental open questions and possible research directions will be introduced