Information Dissemination From Social Network for Extreme Weather Scenario

Srivastava, Harshit; Sankar, Ravi

doi:10.1109/tcss.2020.2964253

Cited by 12 publications

(8 citation statements)

References 21 publications

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“…Srivastava and Sankar (2020) put forward a new method to identify errors under throwable in the server of online information platform and conduct a predicting survey on the social data of Twitter (online social platform) under extreme weathers. Then qualitive analyses can be made on the data collected from social media and website of weather information [ 19 ]. Tulu et al (2018) proposed condition-based Maintenance (CbM), which measured entropy that randomly moves between nodes and neighbors.…”

Section: Introductionmentioning

confidence: 99%

A multiple information model incorporating limited attention and information environment

Chen

Jin

2021

PLoS ONE

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Rapid development of intelligent information equipment accelerates the expansion of mobile social network. Speed of information spreading is gradually growing, there are lots of changes in the scale and mode of information spreading. But the basic communication network is not developed and not mature, when online information platforms breakdown sometimes it happens to be when important information appears. Therefore, the research is done to solve these occasion problems, help network information platform filter hot news and discuss the reason that hot news exists longer than other news in the Internet. In this paper, a multiple information propagation model incorporating both local information environment and people’s limited attention is proposed based on Susceptible Infected Recovered (SIR) model. Two new concepts are introduced into the model: heat rate and popular rate, to measure the local information influence power and people’s limited attention to information respectively, which are key factors determining node state transformation instead of fixed probability. In order to analyze the influence from limited attention, a situation is designed that several pieces of information are popular successively. The theoretical analysis shows that the early popular information gets more attention than the later popular information, and more attention makes it easier to spread. Besides, numerical simulation is conducted in both uniform network and scale-free network. The simulation results show that the early popular information is less vulnerable to the increase of information acceptance threshold and more sensitive to the decrease of information rejection threshold than the later popular information. Moreover, the model can also be used in the case of large amount of information transmission without adding too much complexity. Reasons are given in the research that the top hot news exists very much longer than the other ones, and latter news which have same influence as top news are hard to get the same focus. Meanwhile, results in the research can provide some ways for the other researches in the related fields. They also help related information platforms to filter and push news and referable strategies to maintain hot news.

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Section: Introductionmentioning

confidence: 99%

A multiple information model incorporating limited attention and information environment

Chen

Jin

2021

PLoS ONE

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“…This stage provides crucial information on how an event is spreading and helps to predict its future spread. Stage 3 represents the cooperation spread and control model [5] to determine the spread variable. This stage takes into account the interplay between social and physical data to determine the overall spread factor.…”

Section: Research Objectivesmentioning

confidence: 99%

“…It generates a massive amount of data in real time, but has a limit on the number of words that can be used for each post. This limitation on characters increases the inability to identify words that are critical for the occurrence of an event; techniques to remove this sparseness were recently developed using supervised and unsupervised learning approaches [2][3][4][5].…”

Section: Introductionmentioning

confidence: 99%

Cooperative Attention-Based Learning between Diverse Data Sources

Srivastava

Sankar

2023

Algorithms

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Cooperative attention provides a new method to study how epidemic diseases are spread. It is derived from the social data with the help of survey data. Cooperative attention enables the detection possible anomalies in an event by formulating the spread variable, which determines the disease spread rate decision score. This work proposes a determination spread variable using a disease spread model and cooperative learning. It is a four-stage model that determines answers by identifying semantic cooperation using the spread model to identify events, infection factors, location spread, and change in spread rate. The proposed model analyses the spread of COVID-19 throughout the United States using a new approach by defining data cooperation using the dynamic variable of the spread rate and the optimal cooperative strategy. Game theory is used to define cooperative strategy and to analyze the dynamic variable determined with the help of a control algorithm. Our analysis successfully identifies the spread rate of disease from social data with an accuracy of 67% and can dynamically optimize the decision model using a control algorithm with a complexity of order O(n2).

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“…The real-time availability of Twitter data is the starting point of many studies of large scale events, such as natural disasters, and how Twitter could be used to help emergency responders ( Palen et al, 2010 ; Vieweg et al, 2010 ; Kireyev et al, 2009 ; Terpstra and Stronkman, 2012 ; Priya et al, 2020 ; Srivastava and Sankar, 2020 ). Regarding applications to the air transportation field, most works mining Twitter data focus on creating and improving airline sentiment classification methods ( Breen, 2012 ; Wan and Gao, 2015 ), without proposing any direct use of their results to improve airline service or passenger satisfaction.…”

Section: Motivationmentioning

confidence: 99%

Impact of Covid-19 on passengers and airlines from passenger measurements: Managing customer satisfaction while putting the US Air Transportation System to sleep

Monmousseau

Marzuoli

Féron

et al. 2020

Transportation Research Interdisciplinary Perspectives

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The COVID-19 pandemic has had a significant impact on the air transportation system worldwide. This paper aims at analyzing the effect of the travel restriction measures implemented during the COVID-19 pandemic from a passenger perspective on the US air transportation system. Four metrics based on data generated by passengers and airlines on social media are proposed to measure how the travel restriction measures impacted the relation between passengers and airlines in close to real-time. The proposed metrics indicate that each airline has reacted differently to the COVID-19 travel restriction measures from a passenger perspective, therefore they can be used by airlines and passengers to improve their decision making process. This report comes ahead of official data related to the same sequence of events, thereby showing the value of passenger-borne data in an industry where corporate priorities, institutional prudence, and passenger satisfaction come close together.

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Information Dissemination From Social Network for Extreme Weather Scenario

Cited by 12 publications

References 21 publications

A multiple information model incorporating limited attention and information environment

A multiple information model incorporating limited attention and information environment

Cooperative Attention-Based Learning between Diverse Data Sources

Impact of Covid-19 on passengers and airlines from passenger measurements: Managing customer satisfaction while putting the US Air Transportation System to sleep

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