Disaster Hashtags in Social Media

Murzintcev, Nikita; Cheng, Changxiu

doi:10.3390/ijgi6070204

Cited by 41 publications

(30 citation statements)

References 26 publications

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“…As a first step, we trained several models with topic numbers between 10 and 100 and calculated the density for each model following the approach presented by Cao, Xia, Li, Zhang, and Tang (2008) and implemented in the package ldatuning (Murzintcev, 2016). As a first step, we trained several models with topic numbers between 10 and 100 and calculated the density for each model following the approach presented by Cao, Xia, Li, Zhang, and Tang (2008) and implemented in the package ldatuning (Murzintcev, 2016).…”

Section: Topic Models-ldamentioning

confidence: 99%

“…We thus integrate both quantitative assessment and human judgement in the steps we performed in the identification of the right model for this study. As a first step, we trained several models with topic numbers between 10 and 100 and calculated the density for each model following the approach presented by Cao, Xia, Li, Zhang, and Tang (2008) and implemented in the package ldatuning (Murzintcev, 2016). The idea of the density-based method is to identify a topic number where the similarity of terms within a topic will be maximized while keeping the similarity between topics as small as possible.…”

Section: Topic Models-ldamentioning

confidence: 99%

See 1 more Smart Citation

Firms with benefits: A systematic review of responsible entrepreneurship and corporate social responsibility literature

Tiba

Rijnsoever

Hekkert

2018

Corp Soc Responsibility Env

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The scholarly literature has so far paid limited attention to responsibility by commercial entrepreneurs. This paper compares responsible entrepreneurship (RE) and corporate social responsibility (CSR) scholarship in order to identify future fields of research.For this purpose, we assess the strengths and weaknesses of extant RE scholarship through the lens of CSR. We have reviewed 11,260 papers via latent Dirichlet allocation for our work. We find that existing RE literature places disproportionate emphasis on how firms can benefit society instead of on how contributions to sustainable development can benefit a firm. Furthermore, the RE literature pays limited attention to employee well-being, customer preference, and civil society as a stakeholder. Also, environmental issues and their balancing with financial and social issues remain relatively under-researched. Overall, we hope that scholarly works inspired by this study may ultimately help to ensure responsible behaviour of start-ups.

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Section: Topic Models-ldamentioning

confidence: 99%

Section: Topic Models-ldamentioning

confidence: 99%

Firms with benefits: A systematic review of responsible entrepreneurship and corporate social responsibility literature

Tiba

Rijnsoever

Hekkert

2018

Corp Soc Responsibility Env

View full text Add to dashboard Cite

show abstract

“…The latter study used a combination of latent Dirichlet allocation (LDA) for semantic information extraction and local spatial autocorrelation for hotspot detection. Using machine-learning methods (support vector machine, supervised latent Dirichlet allocation) in combination with several public disaster-related datasets, it was shown that a classification model of Twitter content can be trained to automatically collect hashtags highly related to the event of interest [26].…”

Section: Related Literaturementioning

confidence: 99%

Using Twitter to Analyze the Effect of Hurricanes on Human Mobility Patterns

2019

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Understanding human mobility patterns becomes essential in crisis management and response. This study analyzes the effect of two hurricanes in the United States on human mobility patterns, more specifically on trip distance (displacement), radius of gyration, and mean square displacement, using Twitter data. The study examines three geographical regions which include urbanized areas (Houston, Texas; Miami-Dade County, Florida) and both rural and urbanized areas (North and South Carolina) affected by hurricanes Matthew (2016) and Harvey (2017). Comparison of movement patterns before, during, and after each hurricane shows that displacement and activity space decreased during the events in the regions. Part of this decline can be potentially tied to observed lower tweet numbers around supply facilities during hurricanes, when many of them are closed, as well as to numerous flooded and blocked roads reported in the affected regions. Furthermore, it is shown that displacement patterns can be modeled through a truncated power-law before, during, and after the analyzed hurricanes, which demonstrates the resilience of human mobility behavior in this regard. Analysis of hashtag use in the three study areas indicates that Twitter contributors post about the events primarily during the hurricane landfall and to some extent also during hurricane preparation. This increase in hurricane-related Twitter topics and decrease in activity space provides a tie between changed travel behavior in affected areas and user perception of hurricanes in the Twitter community. Overall, this study adds to the body of knowledge that connects human mobility to natural crises at the local level. It suggests that governmental and rescue operations need to respond to and be prepared for reduced mobility of residents in affected regions during natural crisis events. disasters including wildfires, earthquakes, floods, storms, tsunamis, and hurricanes [6,7]. As expected, the frequency of disaster related tweets is highest in the spatial proximity of a disaster [8,9].Related work on movement analysis focuses primarily on the effects of crisis events on a larger scale and longer-term movements. One study, for example, explored evacuation patterns by leveraging user location information from tweets posted in the hours prior and concurrent to Hurricane Matthew in 2016 [10], and another study used Twitter data to estimate the percentage of evacuees during the same hurricane [11]. Tweets were also used to identify refugee migration patterns from the Middle East and Northern Africa to Europe during the initial surge of refugees aiming for Europe in 2015 [12].Despite such analysis, the effect of natural disasters on local mobility patterns (e.g., for the population remaining in the affected regions during such an event), is less explored. Furthermore, the implications of changed mobility patterns on planning and rescue operations need to be addressed. As a step in this direction, the goal of this study is to investigate changes in human activity patterns du...

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“…Compared to Sina Weibo users, Twitter users prefer hashtags [20], a word or an abbreviation starting with #, which is often used to mark the topic of the microblog. Some scholars use hashtags to distinguish microblogs related to or unrelated to disasters [21,22]. In this study, # was filtered out with other special symbols, and hashtags were not used as a basis for judging emergency information, because their usage is rare in Sina Weibo.…”

Section: Datamentioning

confidence: 99%

Spatiotemporal Change Analysis of Earthquake Emergency Information Based on Microblog Data: A Case Study of the “8.8” Jiuzhaigou Earthquake

Xing

Liu

et al. 2019

IJGI

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Information from social media microblogging has been applied to management of emergency situations following disasters. In particular, such blogs contain much information about the public perception of disasters. However, the effective collection and use of disaster information from microblogs still presents a significant challenge. In this paper, a spatial distribution detection method is established using emergency information based on the urgency degree grading of microblogs and spatial autocorrelation analysis. Moreover, a character-level convolutional neural network classifier is applied for microblog classification in order to mine the spatio-temporal change process of emergency rescue information. The results from the Jiuzhaigou (Sichuan, China) earthquake case study demonstrate that different emergency information types exhibit different time variation characteristics. Moreover, spatial autocorrelation analysis based on the degree of text urgency can exclude uneven spatial distribution influences of the number of microblog users, and accurately determine the level of urgency of the situation. In addition, the classification and spatio-temporal analysis methods combined in this study can effectively mine the required emergency information, allowing us to understand emergency information spatio-temporal changes. Our study can be used as a reference for microblog information applications within the field of emergency rescue activity.

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Disaster Hashtags in Social Media

Cited by 41 publications

References 26 publications

Firms with benefits: A systematic review of responsible entrepreneurship and corporate social responsibility literature

Firms with benefits: A systematic review of responsible entrepreneurship and corporate social responsibility literature

Using Twitter to Analyze the Effect of Hurricanes on Human Mobility Patterns

Spatiotemporal Change Analysis of Earthquake Emergency Information Based on Microblog Data: A Case Study of the “8.8” Jiuzhaigou Earthquake

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