scholarly journals Didn’t roger that: Social media message complexity and situational awareness of emergency responders

2018 ◽  
Vol 40 ◽  
pp. 166-174 ◽  
Author(s):  
Nicolai Pogrebnyakov ◽  
Edgar Maldonado
Keyword(s):  
Social Media ◽  
Situational Awareness ◽  
Message Complexity ◽  
Emergency Responders ◽  
Media Message

A guided latent Dirichlet allocation approach to investigate real-time latent topics of Twitter data during Hurricane Laura

2021 ◽  
pp. 016555152110077
Author(s):  
Sulong Zhou ◽  
Pengyu Kan ◽  
Qunying Huang ◽  
Janet Silbernagel
Keyword(s):  
Social Media ◽  
Real Time ◽  
Language Processing ◽  
Disaster Response ◽  
Domain Knowledge ◽  
Latent Dirichlet Allocation ◽  
Situational Awareness ◽  
High Performing ◽  
Latent Topics ◽  
Dirichlet Allocation

Natural disasters cause significant damage, casualties and economical losses. Twitter has been used to support prompt disaster response and management because people tend to communicate and spread information on public social media platforms during disaster events. To retrieve real-time situational awareness (SA) information from tweets, the most effective way to mine text is using natural language processing (NLP). Among the advanced NLP models, the supervised approach can classify tweets into different categories to gain insight and leverage useful SA information from social media data. However, high-performing supervised models require domain knowledge to specify categories and involve costly labelling tasks. This research proposes a guided latent Dirichlet allocation (LDA) workflow to investigate temporal latent topics from tweets during a recent disaster event, the 2020 Hurricane Laura. With integration of prior knowledge, a coherence model, LDA topics visualisation and validation from official reports, our guided approach reveals that most tweets contain several latent topics during the 10-day period of Hurricane Laura. This result indicates that state-of-the-art supervised models have not fully utilised tweet information because they only assign each tweet a single label. In contrast, our model can not only identify emerging topics during different disaster events but also provides multilabel references to the classification schema. In addition, our results can help to quickly identify and extract SA information to responders, stakeholders and the general public so that they can adopt timely responsive strategies and wisely allocate resource during Hurricane events.

scholarly journals “It’s Going to be Out There For a Long Time”: The Influence of Message Persistence on Users’ Political Opinion Expression in Social Media

2021 ◽  
pp. 009365022199531
Author(s):  
German Neubaum
Keyword(s):  
Social Media ◽  
Cost Benefit ◽  
Spiral Of Silence ◽  
Human Communication ◽  
Political Messages ◽  
Opinion Expression ◽  
Long Time ◽  
Silence Effect ◽  
Central Characteristic ◽  
Media Message

In light of the growing politicization of social media, the spiral of silence theory and its predictions on the conditions under which individuals express political opinions have gained increasing scholarly attention. This study contributes to this line of research by identifying the influence of a central characteristic of social media: message persistence. It was expected that high technical durability of political messages reduces users’ propensity to voice their opinion, moderating the silence effect. A pre-registered experiment ( N = 772) revealed a small-to-medium persistence effect in three out of four topical contexts. While perceived congruence with the opinion climate was not associated with the likelihood of opinion expression, the latter could be explained by a mental cost-benefit calculus that was shaped by message persistence. Theoretical implications are discussed referring to (a) a situational approach regarding silencing processes on social media and (b) its connection to a behavioral calculus of human communication.

scholarly journals Social media in emergency management: exploring Twitter use by emergency responders in the UK

2018 ◽  
Vol 14 (4) ◽  
pp. 322
Author(s):  
Nathaniel O'Grady ◽  
Peter M. Atkinson ◽  
Mark Weal ◽  
Sophie Parsons
Keyword(s):  
Social Media ◽  
Emergency Management ◽  
Emergency Responders ◽  
The Uk

scholarly journals MP01: Use of an unmanned aerial vehicle to provide situational awareness in a simulated mass casualty incident

CJEM ◽  
2018 ◽  
Vol 20 (S1) ◽  
pp. S40-S40
Author(s):  
A. K. Sibley ◽  
T. Jain ◽  
B. Nicholson ◽  
M. Butler ◽  
S. David ◽  
...  
Keyword(s):  
Situational Awareness ◽  
Optimal Strategies ◽  
Mass Casualty Incident ◽  
Mass Casualty ◽  
Treatment Area ◽  
Incident Command ◽  
Emergency Responders ◽  
Video Footage ◽  
Scene Management ◽  
Patient Localization

Introduction: Situational awareness (SA) is essential for maintenance of scene safety and effective resource allocation in mass casualty incidents (MCI). Unmanned aerial vehicles (UAV) can potentially enhance SA with real-time visual feedback during chaotic and evolving or inaccessible events. The purpose of this study was to test the ability of paramedics to use UAV video from a simulated MCI to identify scene hazards, initiate patient triage, and designate key operational locations. Methods: A simulated MCI, including fifteen patients of varying acuity (blast type injuries), plus four hazards, was created on a college campus. The scene was surveyed by UAV capturing video of all patients, hazards, surrounding buildings and streets. Attendees of a provincial paramedic meeting were invited to participate. Participants received a lecture on SALT Triage and the principles of MCI scene management. Next, they watched the UAV video footage. Participants were directed to sort patients according to SALT Triage step one, identify injuries, and localize the patients within the campus. Additionally, they were asked to select a start point for SALT Triage step two, identify and locate hazards, and designate locations for an Incident Command Post, Treatment Area, Transport Area and Access/Egress routes. Summary statistics were performed and a linear regression model was used to assess relationships between demographic variables and both patient triage and localization. Results: Ninety-six individuals participated. Mean age was 35 years (SD 11), 46% (44) were female, and 49% (47) were Primary Care Paramedics. Most participants (80 (84%)) correctly sorted at least 12 of 15 patients. Increased age was associated with decreased triage accuracy [-0.04(-0.07,-0.01);p=0.031]. Fifty-two (54%) were able to localize 12 or more of the 15 patients to a 27x 20m grid area. Advanced paramedic certification, and local residency were associated with improved patient localization [2.47(0.23,4.72);p=0.031], [-3.36(-5.61,-1.1);p=0.004]. The majority of participants (78 (81%)) chose an acceptable location to start SALT triage step two and 84% (80) identified at least three of four hazards. Approximately half (53 (55%)) of participants designated four or more of five key operational areas in appropriate locations. Conclusion: This study demonstrates the potential of UAV technology to remotely provide emergency responders with SA in a MCI. Additional research is required to further investigate optimal strategies to deploy UAVs in this context.

scholarly journals Augmenting Situational Awareness for First responders using Social media as a sensor

2013 ◽  
Vol 46 (15) ◽  
pp. 133-140 ◽  
Author(s):  
Rakesh Dave ◽  
Sanjay K. Boddhu ◽  
Matt McCartney ◽  
James West
Keyword(s):  
Social Media ◽  
Situational Awareness ◽  
First Responders

Tweeting about TV: Sharing television viewing experiences via social media message streams

First Monday ◽  
2011 ◽  
Author(s):  
D. Yvette Wohn ◽  
Eun-Kyung Na
Keyword(s):  
Social Media ◽  
Content Analysis ◽  
Television Viewing ◽  
Different Types ◽  
Program Content ◽  
Media Message

Through content analysis of messages posted on Twitter, we categorize the types of content into a matrix — attention, emotion, information, and opinion. We use this matrix to analyze televised political and entertainment programs, finding that different types of messages are salient for different types of programs, and that the frequencies of the types correspond with program content. Our analyses suggest that Twitter picks up where formal social television systems failed: people are using the tool to selectively seek others who have similar interests and communicate their thoughts synchronous with television viewing.

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