Crowd or Hubs: information diffusion patterns in online social networks in disasters

Fan, Chao; Jiang, Yucheng; Yang, Yang; Zhang, Cheng; Mostafavi, Ali

doi:10.1016/j.ijdrr.2020.101498

Cited by 40 publications

(21 citation statements)

References 23 publications

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“…Also, the proposed network percolation-based contagion model can be adapted for modeling other network spread phenomena. Future studies can further investigate the proposed model in other general predictive tasks, such as the spread of traffic congestions in road networks 44 , infectious diseases in human contact networks 45 , and innovations in global communities 46 . This model also has some limitations; for instance, for initial flooded segments without flooded neighbors, it is usually difficult to predict these segments without other information.…”

Section: Discussion and Concluding Remarksmentioning

confidence: 99%

A network percolation-based contagion model of flood propagation and recession in urban road networks

Fan

Jiang

Mostafavi

2020

Sci Rep

Self Cite

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in this study, we propose a contagion model as a simple and powerful mathematical approach for predicting the spatial spread and temporal evolution of the onset and recession of floodwaters in urban road networks. A network of urban roads resilient to flooding events is essential for the provision of public services and for emergency response. The spread of floodwaters in urban networks is a complex spatial-temporal phenomenon. this study presents a mathematical contagion model to describe the spatial-temporal spread and recession process of floodwaters in urban road networks. The evolution of floods within networks can be captured based on three macroscopic characteristicsflood propagation rate (β), flood incubation rate (α), and recovery rate (µ)-in a system of ordinary differential equations analogous to the Susceptible-Exposed-Infected-Recovered (SEIR) model. We integrated the flood contagion model with the network percolation process in which the probability of flooding of a road segment depends on the degree to which the nearby road segments are flooded. The application of the proposed model is verified using high-resolution historical data of road flooding in Harris County during Hurricane Harvey in 2017. The results show that the model can monitor and predict the fraction of flooded roads over time. Additionally, the proposed model can achieve 90% precision and recall for the spatial spread of the flooded roads at the majority of tested time intervals. The findings suggest that the proposed mathematical contagion model offers great potential to support emergency managers, public officials, citizens, first responders, and other decision-makers for flood forecast in road networks. Given the essential role transportation plays in emergency response, provision of essential services, and maintenance of economic well-being 1 , the resilience of urban road networks to natural hazards, especially flooding events, has received increasing attention 2, 3. Floodwaters in urban networks propagate over time and space, inducing a great deal of spatial-temporal uncertainty visa -vis protective actions, such as evacuation, and rapid emergency response 4. Developing effective prediction tools to forecast the characteristics of flooding events is critical to the enhancement of urban road network resilience 5. Multiple studies have explored the spatial-temporal properties of floods in urban networks, including impact evaluation of environmental stress 6-8 and cascading effects in road networks 9, 10. In particular, empirical studies adopting remote sensors 11 , hydraulic data 12 , or satellite images 13 have attempted to capture the properties of urban flooding. Temporal evolution of flood status is driven by the time-dependent profile of environmental stress, such as the duration of rainfall in hurricanes 12. This temporal information facilitates identification of the outbreak and inflection points for flooding in affected networks. Flooding also exhibits high spatial correlation 14 in which the co-located road segments are...

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Section: Discussion and Concluding Remarksmentioning

confidence: 99%

A network percolation-based contagion model of flood propagation and recession in urban road networks

Fan

Jiang

Mostafavi

2020

Sci Rep

Self Cite

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“…Retweeting refers to the act of sharing others' tweets within users' networks. The importance of the retweet lies in its ability to act as a dissemination tool, and to validate and engage with other Twitter users (Fan et al, 2020). Retweet is equivalent to word-of-mouth (WOM)…”

Section: Predictive Models Using Twitter Data: Retweet Analysismentioning

confidence: 99%

Predicting tweet impact using a novel evidential reasoning prediction method

Rivadeneira

Yang

López-Ibáñez

2021

Expert Systems with Applications

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“…Based on the views of the authors of [23], "the text that contains the trending topics during the disaster time and right after the disaster time, that means the trending topic on Twitter during the disaster, classified as situational information, because it gives relevancy about the current event that is going on".…”

Section: Social Media In Situational Informationmentioning

confidence: 99%

Identifying and Characterizing the Propagation Scale of COVID-19 Situational Information on Twitter: A Hybrid Text Analytic Approach

et al. 2021

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During the recent pandemic of COVID-19, an increasing amount of information has been propagated on social media. This situational information is valuable for public authorities. Therefore, this study characterized the propagation scale of situational information types by harnessing the power of natural language processing techniques and machine learning algorithms. We observed that the length of the post has a positive correlation with type 1 information (announcements), and negative words were mostly used in type 5 information (criticizing the government), whereas anxiety-related words have a negative effect on the amount of retweeted type 0 (precautions) and type 2 (donations) information. This type of research study not only contributes to the situational information literature by comprehensively defining categories but also provides data-oriented practical insights into information so that management authorities can formulate response strategies after the pandemic. Our approach is one of its kind and combines Twitter content features, user features and LIWC linguistic features with machine learning algorithms to analyze the propagation scale of situational information, and it achieved 77% accuracy with SVM while classifying the information categories.

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Crowd or Hubs: information diffusion patterns in online social networks in disasters

Cited by 40 publications

References 23 publications

A network percolation-based contagion model of flood propagation and recession in urban road networks

A network percolation-based contagion model of flood propagation and recession in urban road networks

Predicting tweet impact using a novel evidential reasoning prediction method

Identifying and Characterizing the Propagation Scale of COVID-19 Situational Information on Twitter: A Hybrid Text Analytic Approach

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