Proceedings ArticleDOI
Emergency Management using Social Networks
Dolly Sharma,Prerna,Ranjit Kumar +2 more
- pp 721-726
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TLDR
An end-to-end framework is proposed that takes public posts from social networking sites and converts it into a structured format that makes the information actionable and applies influence maximization techniques to increase the reach and to warrant better public participation in the crisis in a timely manner.Abstract:
The popularity of social networks make them most efficient to integrate into the Emergency Management process. Posts on social networking sites can help people by ensuring timely detection of an emergency. Often during the situations of a natural disaster, there is an information chasm created between the affected and the unaffected area that further compounds the confusion and chaos. In this paper, we examine the various challenges that exist while attempting to integrate social networks and Emergency Management and trace the state-of-art techniques that exist in various domains that come together for this Emergency Management system. We propose an end-to-end framework that takes public posts from social networking sites and converts it into a structured format that makes the information actionable. A summarization technique may be applied to the acquired information post mining of social media feed to convert everything into a text message that can be released into various social platforms. To increase the reach of this post and to warrant better public participation in the crisis in a timely manner, we apply influence maximization techniques and monitor the diffusion process of this generated post through a diffusion modelling technique that we propose. We conduct experiments to analyze the performance of this model and of the influence maximization process and conclude with an analysis of the experiments and the observed results and list out improvements that we intend to incorporate in future versions of this work.read more
Citations
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Journal ArticleDOI
An application of media and network multiplexity theory to the structure and perceptions of information environments in hurricane evacuation
TL;DR: Survey data collected from households in Jacksonville, Florida affected by 2016's Hurricane Matthew identifies perceived consistency of information as a key predictor of uncertainty regarding hurricane impact and evacuation logistics and provides practical implications regarding the need of information coordination for improved evacuation decision‐making.
References
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Journal ArticleDOI
Small but slow world: how network topology and burstiness slow down spreading
Márton Karsai,Mikko Kivelä,Raj Kumar Pan,Kimmo Kaski,János Kertész,Albert-László Barabási,Jari Saramäki +6 more
TL;DR: The time evolution of information propagation is followed through communication networks by using empirical data on contact sequences and the susceptible-infected model and introducing null models where event sequences are appropriately shuffled to distinguish between the contributions of different impeding effects.
Journal ArticleDOI
Using Social Media to Enhance Emergency Situation Awareness
TL;DR: In this paper, a system uses natural language processing and data mining techniques to extract situation awareness information from Twitter messages generated during various disasters and crises, such as hurricanes, floods, and floods.
Proceedings ArticleDOI
Modeling Information Diffusion in Implicit Networks
Jaewon Yang,Jure Leskovec +1 more
TL;DR: The Linear Influence Model accurately models influences of nodes and reliably predicts the temporal dynamics of information diffusion, and finds that patterns of influence of individual participants differ significantly depending on the type of the node and the topic of the information.
Proceedings Article
Uncovering the Temporal Dynamics of Diffusion Networks
TL;DR: In this paper, the authors model diffusion processes as discrete networks of continuous temporal processes occurring at different rates and infer the edges of the global diffusion network and estimate the transmission rates of each edge that best explain the observed data.
Proceedings Article
Extracting Information Nuggets from Disaster- Related Messages in Social Media
TL;DR: This paper focuses on extracting valuable “information nuggets”, brief, self-contained information items relevant to disaster response, using automatic methods for extracting information from microblog posts that leverage machine learning methods for classifying posts and information extraction.