Exploring the emergence of influential users on social media during natural disasters

Yang, Yang; Zhang, Cheng; Fan, Chao; Yao, Wenlin; Huang, Ruihong; Mostafavi, Ali

doi:10.1016/j.ijdrr.2019.101204

Cited by 30 publications

(25 citation statements)

References 21 publications

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“…conveys anticipation and was retweeted in total 1,154,211 times. User behavior in terms of emotional expression directly contributes to the emergence of the underlying communication network [70]. For the nine disaster events, we found that 12 out of 13 possible directed 3-subgraphs are statistically significant building blocks of the emotion-annotated networks.…”

Section: Discussionmentioning

confidence: 87%

“…In recent years, a number of studies analyzed the communication behavior in numerous natural disasters. For example, the 2009 Marseille fire [8], the 2011 and 2013 floods in Brisbane [24], hurricane Irene [39], the 2015 Chennani flood [43], typhoon Haiyan in the Philippines [60], the 2011 Tohoku earthquake and tsunami in Japan [42], hurricane Sandy [51,68], the South East Queensland flood [27], and hurricane Harvey [70], to name just a few.…”

Section: Use Of Twitter During Natural Disastersmentioning

confidence: 99%

“…A recent study found that user (communication) behavior plays a significant role in the dynamics of network structures [70]. In this paper, we explore the influence of expressed emotions, as one important aspect of observable user behavior on OSNs, on the structure of the underlying communication network.…”

Section: Introductionmentioning

confidence: 99%

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“Evacuate everyone south of that line” Analyzing structural communication patterns during natural disasters

Kušen

Strembeck

2020

J Comput Soc Sc

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In this paper, we analyze more than 16 million tweets that have been sent from 6.1 million Twitter accounts and are related to nine natural disasters. As part of our analysis, we identify eight basic emotions conveyed in these tweets. We found that during natural disasters, social media messages at first predominantly express fear, while sadness and positive emotions increase in the immediate aftermath of the event. In this context, positive emotions contribute to the social phenomenon of emotional bonding and are often related to compassion, gratitude, as well as donations for disaster relief. In our analysis, we found that the users’ emotional expressions directly contribute to the emergence of the underlying communication network. In particular, we identified statistically significant structural patterns that we call emotion-exchange motifs and show that: (1) the motifs 021U and 021D are common for the communication of all eight emotions considered in this study, (2) motifs which include bidirectional edges (i.e. online conversations) are generally not characteristic for the communication of surprise, sadness, and disgust, (3) the structural analysis of a set of emotions (rather than a single emotion) leads to the formation of more complex motifs representing more complex social interactions, and (4) the messaging patterns emerging from the communication of joy and sadness show the highest structural similarity, even reaching a perfect similarity score at some point during the data-extraction period.

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Section: Discussionmentioning

confidence: 87%

Section: Use Of Twitter During Natural Disastersmentioning

confidence: 99%

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“Evacuate everyone south of that line” Analyzing structural communication patterns during natural disasters

Kušen

Strembeck

2020

J Comput Soc Sc

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“…Hence, users who received a great number of retweets play the role of information hubs in delivering reliable information to other online users (this phenomenon was examined as the emergence of influential users in a recent study (Y. Yang et al 2019). Hence, the current study solely focused on the number of retweets for examining hubs based on retweets.…”

Section: Reticulation Mechanismsmentioning

confidence: 99%

Characterizing reticulation in online social networks during disasters

et al. 2020

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Online social network has become a new form of infrastructure for communities in spreading situational information in disasters. Developing effective interventions to improve the network performance of information diffusion is essential for people to rapidly retrieve information in coping with disasters and subsequent disruptions. Existing studies have investigated multiple aspects of online social networks in stationary situations and a separate manner. However, the networks are dynamic and different properties of the networks are co-related in the evolving disaster situations. In particular, disaster events motivate people to communicate online, create and reinforce their connections, and lead to a dynamic reticulation of the online social networks. To understand the relationship among these elements, we proposed an Online Network Reticulation (ONR) framework to examine four modalities (i.e., enactment, activation, reticulation, and network performance) in the evolution of online social networks to analyze the interplays among disruptive events in disasters, user activities, and information diffusion performance on social media. Accordingly, we examine the temporal changes in four elements for characterization of reticulation: activity timing, activity types (post, share, reply), reticulation mechanism (creation of new links versus reinforcement of existing links), and structure of communication instances (self-loop, converging, and reciprocal). Finally, the aggregated effects of network reticulation, using attributed network-embedding approach, are examined in the average latent distance among users as a measure of network performance for information propagation. The application of the proposed framework is demonstrated in a study of network reticulation on Twitter for a built environment disruption event during 2017 Hurricane Harvey in Houston. The results show that the main underlying mechanism of network reticulation in evolving situations was the creation of new links by regular users. The main structure for communication instances was converging, indicating communication instances driven by information-seeking behaviors in the wake of a disruptive event. With the evolution of the network, the proportion of converging structures to self-loop and reciprocal structures did not change significantly, indicating the existence of a scaleinvariance property for network structures. The findings demonstrate the capability of the proposed online network reticulation framework for characterizing the complex relationships between events, activities, and network performance in online social networks during disasters.

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“…Similarly, opinion mining using the SentiSAIL tool was feasible for detecting positive and negative sentiments from German Twitter users during the 2013 Central European flood, which could generate insights for first responders [23]. In another study, a widely used sentiment analysis tool called the Valence Aware Dictionary and Sentiment Reasoner (VADER) could detect the unique characteristics of Twitter users and their patterns of influence based on their posts during Hurricane Harvey in the United States [24]. These studies highlight the promise of using social media platforms such as Twitter, and computational sentiment analysis methods to derive public opinions and attitudes about natural disasters and other environmental health threats.…”

Section: Introductionmentioning

confidence: 99%

Digital Surveillance for Monitoring Environmental Health Threats: A Case Study Capturing Public Opinion from Twitter about the 2019 Chennai Water Crisis

Xiong

Hswen

Naslund

2020

IJERPH

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Globally, water scarcity has become a common challenge across many regions. Digital surveillance holds promise for monitoring environmental threats to population health due to severe drought. The 2019 Chennai water crisis in India resulted in severe disruptions to social order and daily life, with local residents suffering due to water shortages. This case study explored public opinion captured through the Twitter social media platform, and whether this information could help local governments with emergency response. Sentiment analysis and topic modeling were used to explore public opinion through Twitter during the 2019 Chennai water crisis. The latent Dirichlet allocation (LDA) method identified topics that were most frequently discussed. A naïve Tweet classification method was built, and Twitter posts (called tweets) were allocated to identified topics. Topics were ranked, and corresponding emotions were calculated. A cross-correlation was performed to examine the relationship between online posts about the water crisis and actual rainfall, determined by precipitation levels. During the Chennai water crisis, Twitter users posted content that appeared to show anxiety about the impact of the drought, and also expressed concerns about the government response. Twitter users also mentioned causes for the drought and potential sustainable solutions, which appeared to be mainly positive in tone. Discussion on Twitter can reflect popular public opinion related to emerging environmental health threats. Twitter posts appear viable for informing crisis management as real-time data can be collected and analyzed. Governments and public health officials should adjust their policies and public communication by leveraging online data sources, which could inform disaster prevention measures.

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Exploring the emergence of influential users on social media during natural disasters

Cited by 30 publications

References 21 publications

“Evacuate everyone south of that line” Analyzing structural communication patterns during natural disasters

“Evacuate everyone south of that line” Analyzing structural communication patterns during natural disasters

Characterizing reticulation in online social networks during disasters

Digital Surveillance for Monitoring Environmental Health Threats: A Case Study Capturing Public Opinion from Twitter about the 2019 Chennai Water Crisis

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