Understanding the evolutions of public responses using social media: Hurricane Matthew case study

Yuan, Faxi; Li, Min; Li, Rui

doi:10.1016/j.ijdrr.2020.101798

Cited by 46 publications

(26 citation statements)

References 35 publications

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“…Sentiment analysis of social media is crucial for crisis management. With the development of machine learning, information on geographic locations has been gradually utilised to study user-generated information [ 39 ]. Liu et al [ 40 ] proposed the concept of ‘social sensing’ data, which obtains human behaviour trajectories, reflects group behaviours, and characterises social-economic phenomena on a large scale [ 41 ].…”

Section: Literature Reviewmentioning

confidence: 99%

Spatial evolution patterns of public panic on Chinese social networks amidst the COVID-19 pandemic

Yang

Zhang

et al. 2022

International Journal of Disaster Risk Reduction

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Novel coronavirus pneumonia has had a significant impact on people's lives and psychological health. We developed a stage model to analyse the spatial and temporal distribution of public panic during the two waves of the coronavirus disease 2019 (COVID-19) pandemic. We used tweets with geographic location data from the popular hashtag ‘Lockdown Diary’ recorded from 23 January to April 8, 2020, and ‘Nanjing Outbreak’ recorded from 21 July to 1 September 2021 on Weibo. Combining the lexicon-based sentiment analysis and the grounded theory approach, this panic model could explain people's panic and behavioural responses in different areas at different stages of the pandemic. Next, we used the latent Dirichlet allocation topic model to reconfirm the panic model. The results showed that public sentiments fluctuated strongly in the early stages; in this case, panic and prayers were the dominant sentiments. In terms of spatial distribution, public panic showed hierarchical and neighbourhood diffusion, with highly assertive expressions of sentiment at the outbreak sites, economically developed areas, and areas surrounding the outbreak. Most importantly, we considered that public panic was affected by the 17 specific topics extracted based on the perceived and actual distance of the pandemic, thus stimulating the process of panic from minimal, acute, and mild panic to perceived rationality. Consequently, the public's behavioural responses shifted from delayed, negative, and positive, to rational behavioural responses. This study presents a novel approach to explore public panic from both a time and space perspective and provides some suggestions in response to future pandemics.

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Section: Literature Reviewmentioning

confidence: 99%

Spatial evolution patterns of public panic on Chinese social networks amidst the COVID-19 pandemic

Yang

Zhang

et al. 2022

International Journal of Disaster Risk Reduction

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“…Case studies include photos of the 2007 Southern California wildfire 15 , the 2010 Haiti earthquake 16 , the 2017 Hurricane Harvey 17 , the 2019 Indonesia fire 18 , and earthquake detection using Tweets 19 – 21 . Some closely relevant research also discussed people’s responses to natural hazards, such as earthquakes 22 , the 2012 Hurricane Sandy 23 , the 2015 Typhoon Etau 24 , the 2016 Hurricane Matthew 25 , and the recent COVID-19 26 . Most studies only focused on a single platform, and there is limited work on cross-platform analysis.…”

Section: Related Workmentioning

confidence: 99%

Cross-platform analysis of public responses to the 2019 Ridgecrest earthquake sequence on Twitter and Reddit

Ruan

Kong

McBride

et al. 2022

Sci Rep

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Online social networks (OSNs) have become a powerful tool to study collective human responses to extreme events such as earthquakes. Most previous research concentrated on a single platform and utilized users’ behaviors on a single platform to study people’s general responses. In this study, we explore the characteristics of people’s behaviors on different OSNs and conduct a cross-platform analysis of public responses to earthquakes. Our findings support the Uses and Gratification theory that users on Reddit and Twitter are engaging with platforms that they may feel best reflect their sense of self. Using the 2019 Ridgecrest earthquakes as our study cases, we collected 510,579 tweets and 45,770 Reddit posts (including 1437 submissions and 44,333 comments) to answer the following research questions: (1) What were the similarities and differences between public responses on Twitter and Reddit? (2) Considering the different mechanisms of Twitter and Reddit, what unique information of public responses can we learn from Reddit as compared with Twitter? By answering these research questions, we aim to bridge the gap of cross-platform public responses research towards natural hazards. Our study evinces that the users on the two different platforms have both different topics of interest and different sentiments towards the same earthquake, which indicates the necessity of investigating cross-platform OSNs to reveal a more comprehensive picture of people’s general public responses towards certain disasters. Our analysis also finds that r/conspiracy subreddit is one of the major venues where people discuss the 2019 Ridgecrest earthquakes on Reddit and different misinformation/conspiracies spread on Twitter and Reddit platforms (e.g., “Big one is coming” on Twitter and “Nuclear test” on Reddit).

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“…Furthermore, several tweets show a distinct sporadic distribution of trend patterns across different phases. The public sentiment during a disaster period does not always reflect the actual impact of disasters they experience because social media users have their own preferences to respond to a disaster topic in a certain sentiment [42]. Figure 4 shows the wordcloud containing the responses of Twitter users which came from the impact during the Sunda Strait tsunami emergency response period.…”

Section: Spatial Characteristics Of Tweet Sentimentmentioning

confidence: 99%

Understanding Community Collective Behaviour Through Social Media Responses: Case of Sunda Strait Tsunami, 2018, Indonesia

Salsabilla

Hizbaron

2021

E3S Web Conf.

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The Sunda Strait Tsunami which occurred on 22nd December 2018 is one among too many examples of a rapid on-set disaster that attracted public attention through Twitter. This sudden event had a massive impact on parts of the west coast of Banten Province, Indonesia. Therefore, this research aimed to evaluate the collective response reflected on Twitter due to the 2018 Sunda Strait Tsunami. Previous studies shows the utilization of crowd sourcing data from social media for community capacity and quick assessment of disaster impacts. Therefore, the characteristics of people’s responses on social media based on spatio-temporal attributes needs to be understood first to build better understanding about the information that can be used for emergency response strategies consideration. This research method involved a spatial statistics approach, while data collection and descriptive analysis were carried out based on Twitter word cloud data. This analysis showed that temporally, the highest number of tweets was generated at the beginning of the disaster period with downward trend into the end of phase. As for spatially, people in directly affected areas by the disaster tend to give negative sentiments as their expression of sadness and fears towards the disaster. The content of the tweets involved asking for help, reporting on the current situation, and confirming the news on accounts belonging to government agencies. Furthermore, people in areas that were not directly affected produced tweets with more positive sentiment with expressions of condolences, sympathy, gratitude and invitations for volunteers and social actions.

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Understanding the evolutions of public responses using social media: Hurricane Matthew case study

Cited by 46 publications

References 35 publications

Spatial evolution patterns of public panic on Chinese social networks amidst the COVID-19 pandemic

Spatial evolution patterns of public panic on Chinese social networks amidst the COVID-19 pandemic

Cross-platform analysis of public responses to the 2019 Ridgecrest earthquake sequence on Twitter and Reddit

Understanding Community Collective Behaviour Through Social Media Responses: Case of Sunda Strait Tsunami, 2018, Indonesia

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