Emergency information diffusion on online social media during storm Cindy in U.S.

doi:10.1016/j.ijinfomgt.2018.02.003

International Journal of Information Management

Volume 40, June 2018, Pages 153-165

https://doi.org/10.1016/j.ijinfomgt.2018.02.003 Get rights and content

Highlights

•
This study explored connections and patterns of the social network created by the aggregated interactions in Twitter during disaster responses.
•
This study collected Twitter data during the period of the storm Cindy, June 20–30, 2017.
•
This study analyzed the critical roles of four types of online users using social network analysis and text analysis.
•
This study suggested further actions to improve the effectiveness of information diffusion via social media in an emergency.

Abstract

Social media plays a critical role in propagating emergency information during disasters. Governmental agencies have opened social media accounts for emergency communication channels. To understand the underlying mechanism of user behaviors and engagement, this study employs social network analysis to investigate information network and diffusion across news, weather agencies, governmental agencies, organizations and the public during the 2017 Storm Cindy in the U.S. This study identified certain types of Twitter users (news and weather agencies) were dominant as information sources and information diffusers (the public and organizations). However, the information flow in the network was controlled by numerous types of users including news, agency, weather agencies and the public. The results highlighted the importance of understanding the unique characteristics of social media and networks for better emergency communication system.

Introduction

Communication is a core component of disaster management. Given the threats to human life and property that a disaster causes, individuals need information about what has happened and what is still onging within a disaster-affected area and beyond (Rodríguez, Diaz, Santos, & Aguirre, 2007). Thus, developing an effective disaster communication system should be the top priority for relevant governmental agencies, private organizations, and communities. Social media plays a critical role in disaster management for accessing information during an emergency (Lindsay, 2011). Many studies have explored the systematic use of social media during emergency responses by extracting social media data to identify the needs of a disaster-affected community (Gao, Barbier, & Goolsby, 2011; Imran, Elbassuoni, Castillo, Diaz, & Meier, 2013; Yin et al., 2015). For example, social media data was used to develop a GIS-based real-time map during Hurricane Sandy in 2012 (Middleton, Middleton, & Modafferi, 2014). It shared critical information and community needs with emergency agencies and NGOs. Furthermore, real-time data from social media has been used to develop an early warning system for tornadoes (Knox et al., 2013; Tyshchuk, Hui, Grabowski, & Wallace, 2011). Finally, social media is used to communicate emergency information and urgent requests between emergency agencies and disaster-affected communities (Feldman et al., 2016; Kim & Hastak, 2018b). Using social media as a communication method may support emergency agencies in understanding emerging situations rapidly. However, more investigation is needed to determine how emergency agencies can effectively operate their social media during disasters to expedite the diffusion of emergency information within an affected community. For that, it is critical to understand the underlying mechanism of online user behaviors and the unique network structure. Therefore, the objective of this study is to investigate the functioning of social networks during a disaster and analyze the characteristics of an emergent online network, to improve communication strategies and facilitate the development of disaster-related social media tools.

Section snippets

Social capital and infrastructure

Many scholars have found that social capital plays a key role in responses to disasters. Nakagawa and Shaw (2004) identified that social capital and community leadership as the core requisites for rapid disaster recovery. Aldrich (2011) concluded that the power of people (social capital) is the strongest and most robust predictor of population recovery after a catastrophe. Aldrich and Meyer (2015) examined recent literature to investigate the critical role of social capital and networks in

Objectives and methodology

This study investigated the use of Twitter’s social network during the 2017 Storm Cindy in U.S. This study examined online social media networks and user behaviors during the storm through the lens of Social Network Analysis (SNA). It provides insights to understand emergent social networks and the engagement during disaster responses and suggests strategies to propagate emergency information. Objectives of this study are as follows:

1)
Analyze social media data on Twitter during the period of

Search-term trends

Google Trends has been used in several studies to investigate online users’ search behavior around a specific event, with the goal of understanding unique patterns and intensity (Choi & Varian, 2012; Dergiades, Milas, & Panagiotidis, 2015; Dugas et al., 2012; Stocking & Matsa, 2016). Kim and Hastak (2017) found that the trend in a community strongly correlated with behaviors of a disaster. We examined a search-term trend (keyword: Storm Cindy) from June 15–30, 2017 and compared it with the

Discussion

Our findings can be used to understand a large-scale heterogeneity in social networks and to accelerate information diffusion in an emergency. Multiple methods, including SNA and text analysis, identified characteristics of an online social network, its structure and the Twitter user engagement in depth.

News (N) and weather agencies (WA) were dominant in the Twitter network as information sources, whereas the public (P) and organizations (O) mainly focused on retweeting the information.

Conclusion

It is critical for the public to receive accurate, reliable and timely information from emergency agencies during disasters. Many studies have shown that social media 1) influences social consciousness, 2) leads to rapid information delivery, and 3) reaches a broader and more targeted population than any conventional methods. Thus, social media, such as Twitter and Facebook, is expected as a powerful tool for rapid information diffusion in an emergency.

We investigated the Twitter social network

References (78)

A.L. Barabási et al.
Evolution of the social network of scientific collaborations
Physica A: Statistical Mechanics and Its Applications
(2002)
J. Chae et al.
Public behavior response analysis in disaster events utilizing visual analytics of microblog data
Computers and Graphics
(2014)
M. Cho et al.
Public engagement with nonprofit organizations on Facebook
Public Relations Review
(2014)
D. Feldman et al.
Communicating flood risk: Looking back and forward at traditional and social media outlets
International Journal of Disaster Risk Reduction
(2016)
S. Fortunato
Community detection in graphs
Physics Reports
(2010)
H. Gao et al.
Harnessing the crowdsourcing power of social media for disaster relief
IEEE Intelligent Systems
(2011)
M.W. Graham et al.
The role of social media in local government crisis communications
Public Relations Review
(2015)
A. Joshi et al.
The role of social capital and public policy in disaster recovery: A case study of Tamil Nadu State, India
International Journal of Disaster Risk Reduction
(2014)
J. Kim et al.
Social network analysis: Characteristics of online social networks after a disaster
International Journal of Information Management
(2018)
Q. Luo et al.
Using social network analysis to explain communication characteristics of travel-related electronic word-of-mouth on social networking sites
Tourism Management
(2015)

S. Muralidharan et al.

Hope for Haiti: An analysis of Facebook and Twitter usage during the earthquake relief efforts

Public Relations Review

(2011)

D. Yates et al.

Emergency knowledge management and social media technologies: A case study of the 2010 Haitian earthquake

International Journal of Information Management

(2011)

E. Yoo et al.

Evaluating information diffusion speed and its determinants in social media networks during humanitarian crises

Journal of Operations Management

(2016)

D.P. Aldrich et al.

Social capital and community resilience

American Behavioral Scientist

(2015)

D.P. Aldrich

The power of people: Social capital’s role in recovery from the 1995 Kobe earthquake

Natural Hazards

(2011)

D.E. Alexander

Social media in disaster risk reduction and crisis management

Science and Engineering Ethics

(2014)

K. Allen et al.

Cindy weakens to tropical depression after making landfall in Louisiana – ABC News

(2017)

J. Bae et al.

Evaluating multi-attributes on cause and effect relationship visualization

Proceedings of the 12th international joint conference on computer vision, imaging and computer graphics theory and applications, (Visigrapp)

(2017)

D. Blei et al.

Probabilistic topic models

IEEE Signal Processing Magazine

(2010)

H. Bosch et al.

ScatterBlogs2: Real-time monitoring of microblog messages through user-guided filtering, 19(December 2013)

(2014)

K.A. Cagney et al.

Social resources and community resilience in the wake of superstorm sandy

PLoS ONE

(2016)

N. Chavez et al.

Cindy weakens into tropical depression after making landfall in Louisiana – CNN

(2017)

S. Chen et al.

Social media visual analytics

Computer Graphics Forum

(2017)

H. Choi et al.

Predicting the present with google trends

Economic Record

(2012)

A. Clauset et al.

Finding community structure in very large networks

Physical Review E

(2004)

D. Combe et al.

A comparative study of social network analysis tools

Social Networks

(2010)

T. Dergiades et al.

Tweets, Google trends, and sovereign spreads in the GIIPS

Oxford Economic Papers

(2015)

A.F. Dugas et al.

Google Flu Trends: Correlation with emergency department influenza rates and crowding metrics

Clinical Infectious Diseases

(2012)

FEMA

Harvey rumor control

(2017)

FEMA

Hurricane irma rumor control

(2017)

FEMA

Official accounts

(2017)

Fox News

Tropical Storm Cindy: Video captures rescue of woman in submerged car

(2017)

L.C. Freeman

A set of measures of centrality based on betweenness author (s): Linton C

(1977)

M. Girvan et al.

Community structure in social and biological networks

Proceedings of the National Academy of Sciences

(2002)

Google

How Trends data is adjusted

(2017)

L. Grube et al.

The capacity for self-governance and post-disaster resiliency

The Review of Austrian Economics

(2014)

D. Harel et al.

A fast multi-Scale method for drawing large

In working conf. on advanced visual interfaces (AVI’2000)

(2000)

P. Holley

A terrifying threat from tropical storm cindy: floating masses of deadly fire ants – the washington post

(2017)

L. Hong et al.

Empirical study of topic modeling in twitter

Proceedings of the first workshop on social media analytics – SOMA

(2010)

Cited by (141)

Extracting the diffusion dynamics of crisis information on online social networks: Model and application
2024, International Journal of Disaster Risk Reduction
Disaster mitigation greatly benefits from the diffusion of crisis information. To learn about the diffusion dynamics of crisis information on online social networks, this paper puts forward a crisis information diffusion model based on population dynamics and further analyses its parameters, such as ordinary users’ average number of followers, the number of potential audience members who might spread the crisis information, the forwarding probability for each audience member and the attenuation speed of the information based on real data from Sina Weibo. In addition, a method for the prediction of crisis information diffusion is proposed and verified on the basis of this model. The results show that this model could describe and predict well the diffusion process of crisis information on online social networks, enabling relevant departments to develop appropriate strategies for crisis information diffusion.
Exploring the relationship between urban residents' emotional changes and built environment before and during the COVID-19 pandemic from the perspective of resilience
2023, Cities
During the COVID-19 pandemic, the emotion of urban residents has been largely affected, resulting in different levels of stress and negative emotions. Emotion is an indispensable component of urban resilience, especially in the post COVID-19 period. The changes in residents' moods have the potential to reflect the degree of resilience of urban space as a carrier of daily urban life. This study used machine learning to analyze residents' emotion changes revealed by their posts in Sina Weibo mobile application, and analyzed urban resilience through exploring the relationship between urban built environment and emotional changes before and during the pandemic, using Shenzhen as an example. Our results illustrated the spatial pattern of emotional changes in Shenzhen, and we found that built environments could enhance urban resilience in two approaches. On one hand, catering, retail and sports facilities are supportive to emotional stability and thus contribute to the relatively steady emotional changes. On the other hand, proximity to open space is related with positive emotion, and thus contributes to higher levels of restoration. In contrast, industrial and urban village land uses are associated with lower levels of emotional resilience. This study explored to assess urban resilience from the perspective of emotional changes, which would provide insights for environmental interventions in the post-pandemic era.
The strategic value of IT-enabled self-organised collectives during crises
2023, Journal of Strategic Information Systems
There is a promising body of work pertaining to the strategic value of IT-enabled self-organised collectives in times of crisis. This area is of significant theoretical and practical importance. Yet, we still have little systematic knowledge about precisely how self-organised collectives contribute during crises and how the value of their contributions might be leveraged strategically. To address this, we develop a model to demonstrate four dimensions across which self-organised collectives create strategic value: (i) information sharing value; (ii) collective resource-mobilisation value; (iii) network value; and (iv) generative value. In doing so, we reveal more about the specific capabilities of self-organised collectives, and we use these insights to develop implications for theory. We conclude by outlining an agenda to encourage and accelerate future research on the role of IT-enabled self-organised collectives during crises.
Social response and Disaster management: Insights from twitter data Assimilation on Hurricane Ian
2023, International Journal of Disaster Risk Reduction
During natural disasters, people use social media platforms to share their opinions and general information about the event. Here, we investigate the public response to large and destructive hurricane Ian in late September 2022 by examining the textual content of tweets shared on Twitter across the contiguous United States (CONUS). We mined and processed over twenty million tweets for discovering the main topics of discussion and relationship between them, and classifying tweets into humanitarian topics and categories to help disaster management with thorough sentiment analysis. We employed a variety of algorithms in Artificial Intelligence for Natural Language Processing (NLP) including sentiment analysis, topic modeling, and text classification to assimilate the information content in massive Twitter data. The findings of this study provide insights on how people utilize social media to learn and disseminate information about hurricane events, which accordingly aid emergency responders and disaster managers in mitigating the negative consequences of such catastrophes and improving community preparedness.
A study on predicting crisis information dissemination in epidemic-level public health events
2023, Journal of Safety Science and Resilience
Crisis information dissemination plays a key role in the development of emergency responses to epidemic-level public health events. Therefore, clarifying the causes of crisis information dissemination and making accurate predictions to effectively control such situations have attracted extensive attention. Based on media richness theory and persuasion theory, this study constructs an index system of crisis information dissemination impact factors from two aspects: the crisis information publisher and the published crisis information content. A multi-layer perceptron is used to analyze the weight of the index system, and the prediction is transformed into a pattern classification problem to test crisis information dissemination. In this study, COVID-19 is considered a representative event. An experiment is conducted to predict the crisis information dissemination of COVID-19 in two megacities. Data related to COVID-19 from these two megacities are acquired from the well-known Chinese social media platform Weibo. The experimental results show that not only the identity but also the social influence of the information publisher has a significant impact on crisis information dissemination in epidemic-level public health events. Furthermore, the proposed model achieves more than 95% test accuracy, precision rate, recall value and f1-score in the prediction task. The study provides decision-making support for government departments and a guide for correctly disseminating crisis information and public opinion during future epidemic-level public health events.
Diffusion of real versus misinformation during a crisis event: A big data-driven approach
2023, International Journal of Information Management
Misinformation has captured the interest of academia in recent years with several studies looking at the topic broadly with inconsistent results. In this research, we attempt to bridge the gap in the literature by examining the impacts of user-, time-, and content-based characteristics that affect the virality of real versus misinformation during a crisis event. Using a big data-driven approach, we collected over 42 million tweets during Hurricane Harvey and obtained 3589 original verified real or false tweets by cross-checking with fact-checking websites and a relevant federal agency. Our results show that virality is higher for misinformation, novel tweets, and tweets with negative sentiment or lower lexical density. In addition, we reveal the opposite impacts of sentiment on the virality of real news versus misinformation. We also find that tweets on the environment are less likely to go viral than the baseline religious news, while real social news tweets are more likely to go viral than misinformation on social news.

View all citing articles on Scopus

View full text

Emergency information diffusion on online social media during storm Cindy in U.S.

Highlights

Abstract

Introduction

Section snippets

Social capital and infrastructure

Objectives and methodology

Search-term trends

Discussion

Conclusion

Physica A: Statistical Mechanics and Its Applications

Computers and Graphics

Public Relations Review

International Journal of Disaster Risk Reduction

Physics Reports

IEEE Intelligent Systems

Public Relations Review

International Journal of Disaster Risk Reduction

International Journal of Information Management

Tourism Management

Public Relations Review

International Journal of Information Management

Journal of Operations Management

Social capital and community resilience

American Behavioral Scientist

The power of people: Social capital’s role in recovery from the 1995 Kobe earthquake

Natural Hazards

Social media in disaster risk reduction and crisis management

Science and Engineering Ethics

Cindy weakens to tropical depression after making landfall in Louisiana – ABC News

Evaluating multi-attributes on cause and effect relationship visualization

Proceedings of the 12th international joint conference on computer vision, imaging and computer graphics theory and applications, (Visigrapp)

Probabilistic topic models

IEEE Signal Processing Magazine

ScatterBlogs2: Real-time monitoring of microblog messages through user-guided filtering, 19(December 2013)

Social resources and community resilience in the wake of superstorm sandy

PLoS ONE

Cindy weakens into tropical depression after making landfall in Louisiana – CNN

Social media visual analytics

Computer Graphics Forum

Predicting the present with google trends

Economic Record

Finding community structure in very large networks

Physical Review E

A comparative study of social network analysis tools

Social Networks

Tweets, Google trends, and sovereign spreads in the GIIPS

Oxford Economic Papers

Google Flu Trends: Correlation with emergency department influenza rates and crowding metrics

Clinical Infectious Diseases

Harvey rumor control

Hurricane irma rumor control

Official accounts

Tropical Storm Cindy: Video captures rescue of woman in submerged car

A set of measures of centrality based on betweenness author (s): Linton C

Community structure in social and biological networks

Proceedings of the National Academy of Sciences

How Trends data is adjusted

The capacity for self-governance and post-disaster resiliency

The Review of Austrian Economics

A fast multi-Scale method for drawing large

In working conf. on advanced visual interfaces (AVI’2000)

A terrifying threat from tropical storm cindy: floating masses of deadly fire ants – the washington post

Empirical study of topic modeling in twitter

Proceedings of the first workshop on social media analytics – SOMA