Optimizing social media message dissemination problem for emergency communication

Ma, Xin; Yates, Justin

doi:10.1016/j.cie.2014.09.016

Cited by 14 publications

(3 citation statements)

References 48 publications

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“…Laboratory safety is the main content of laboratory management in universities [ 30 ]. Many scholars have put forward the management system and management suggestions of laboratory safety, in order to systematically reduce laboratory safety accidents [ 31 ].…”

Section: Related Workmentioning

confidence: 99%

Emergency treatment mechanism of laboratory safety accidents in university based on IoT and context aware computing

Shu,

Li,

Gao

2023

Heliyon

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Section: Related Workmentioning

confidence: 99%

Emergency treatment mechanism of laboratory safety accidents in university based on IoT and context aware computing

Shu,

Li,

Gao

2023

Heliyon

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“…In combination with other communication methods, the combined use of social media and social media analysis tools provides humanitarian actors with an opportunity to increase the effectiveness of their social media communication on disaster preparedness [ 25 ]. Other approaches include using mathematical models and heuristics to identify information dissemination in social media networks [ 26 ], or using the same heuristics to determine node optimization in social networks and gather insights on user’s behavior [ 27 ].…”

Section: Related Workmentioning

confidence: 99%

Content-based user classifier to uncover information exchange in disaster-motivated networks

et al. 2021

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Disasters strike communities around the world, with a reduced time-frame for warning and action leaving behind high rates of damage, mortality, and years in rebuilding efforts. For the past decade, social media has indicated a positive role in communicating before, during, and after disasters. One important question that remained un-investigated is that whether social media efficiently connect affected individuals to disaster relief agencies, and if not, how AI models can use historical data from previous disasters to facilitate information exchange between the two groups. In this study, the BERT model is first fine-tuned using historical data and then it is used to classify the tweets associated with hurricanes Dorian and Harvey based on the type of information provided; and alongside, the network between users is constructed based on the retweets and replies on Twitter. Afterwards, some network metrics are used to measure the diffusion rate of each type of disaster-motivated information. The results show that the messages by disaster eyewitnesses get the least spread while the posts by governments and media have the highest diffusion rates through the network. Additionally, the “cautions and advice” messages get the most spread among other information types while “infrastructure and utilities” and “affected individuals” messages get the least diffusion even compared with “sympathy and support”. The analysis suggests that facilitating the propagation of information provided by affected individuals, using AI models, will be a valuable strategy to pursue in order to accelerate communication between affected individuals and survival groups during the disaster and aftermath.

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“…firefighters and police), as well as aid the public in understanding the emergency situation. While that particular case referred to radio communications, it stands to reason that emergency communication on social media should also strive for -plain language‖, with greater adoption of social media by emergency responders and the increased expectation from the public that information about emergencies is communicated through this channel (Ma & Yates, 2014).…”

Section: Readability and Complexity On Social Mediamentioning

confidence: 99%

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

Pogrebnyakov

Maldonado

2018

International Journal of Information Management

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Optimizing social media message dissemination problem for emergency communication

Cited by 14 publications

References 48 publications

Emergency treatment mechanism of laboratory safety accidents in university based on IoT and context aware computing

Emergency treatment mechanism of laboratory safety accidents in university based on IoT and context aware computing

Content-based user classifier to uncover information exchange in disaster-motivated networks

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

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