How people react to Zika virus outbreaks on Twitter? A computational content analysis

Fu, King‐Wa; Liang, Hai; Saroha, Nitin; Tse, Zion Tsz Ho; Ip, Patrick; Fung, Isaac Chun-Hai

doi:10.1016/j.ajic.2016.04.253

Cited by 134 publications

(107 citation statements)

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“…However, it is unknown whether the patterns will hold across different topics. For example, does Zika-related information diffuse on Twitter differently than that of Ebola-related information [14]? Following a similar line of thought, future research may study whether Ebola-related misinformation spreads differently on Twitter networks compared with correct scientific information [15].…”

Section: Limitations and Future Directionsmentioning

confidence: 99%

How did Ebola Information Spread on Twitter: Broadcasting or Viral Spreading? (Preprint)

Liang¹,

Fung²,

Tse³

et al. 2018

Preprint

Self Cite

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Background: It has been argued that information and emotions towards public health issues could spread widely through online social networks. Although aggregate metrics on the volume of information diffusion are available, we know little about how information spreads on online social networks. For example, health information could be transmitted from one to many (i.e. broadcasting), which is similar to how traditional mass media passes information to the general public. Health information could also be transmitted from many to many (i.e. viral spreading), which is analogous to the spread of infectious diseases. Objective: The aim of this study is to determine the spreading pattern of Ebola information on Twitter and identify influential users regarding Ebola messages. On Twitter, influential users are those whose tweets receive a large number of retweets. Methods: Our data was purchased from GNIP, the official Twitter data provider. We obtained all Ebola-related tweets (including retweets and replies) posted from March 23, 2014 to May 31, 2015. We reconstructed Ebola-related retweeting paths based on Twitter content and the follower-followee relationships (who follows whom on Twitter). Social network analysis was performed to investigate retweeting patterns. In addition to describing the diffusion structures, we classify users in the network into four categories (i.e., influential user, hidden influential user, disseminator, common user) based on following and retweeting patterns. Disseminators received fewer retweets than expected based on their number of followers, common users and influential users received as many or fewer retweets than expected, and hidden influential users received more retweets than expected. Results: On average, 91% of the retweets were directly retweeted from the initial message. Moreover, 47.5% of the retweeting paths of the original tweets had a depth of 1 (i.e., from the seed user to its immediate followers). These observations suggested that the broadcast model was more pervasive than viral spreading. Furthermore, we found that influential users and hidden influential users can trigger more retweets than disseminators and common users. Disseminators and common users relied more on the viral model for spreading information beyond their immediate followers via influential and hidden influential users. Conclusions: The broadcast model was the dominant mechanism of information diffusion of a major health event on Twitter. It suggests that public health communicators can work with influential and hidden influential users to get the message across, because influential and hidden influential users can reach more people that are not following the public health Twitter accounts. Although both influential users and hidden influential users can trigger a lot of retweets, recognizing and using the hidden influential users as the source of information could potentially be a cost-effective communication strategy for public health promotion, because the hidden influential users can receive...

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Section: Limitations and Future Directionsmentioning

confidence: 99%

How did Ebola Information Spread on Twitter: Broadcasting or Viral Spreading? (Preprint)

Liang¹,

Fung²,

Tse³

et al. 2018

Preprint

Self Cite

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“…Surveillance studies have also been conducted on enterohemorrhagic Escherichia coli outbreaks [10] and dengue fever [11]. In addition, social media data have been used to improve our understanding of Ebola [12] and Zika virus infections [13][14][15]. While disease surveillance efforts tend to focus on acute infectious diseases, studies have also been conducted on chronic diseases such as cancer [16], hypertension [16], asthma [16][17][18], diabetes [19], and seasonal allergic rhinitis (SAR) [20][21][22].…”

Section: Introductionmentioning

confidence: 99%

Causal Relationships Among Pollen Counts, Tweet Numbers, and Patient Numbers for Seasonal Allergic Rhinitis Surveillance: Retrospective Analysis (Preprint)

Wakamiya¹,

Matsune²,

Okubo³

et al. 2018

Preprint

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Background: Health-related social media data are increasingly used in disease surveillance studies, which have demonstrated moderately high correlations between the number of social media posts and the number of patients. However, there is a need to understand the causal relationship between the behavior of social media users and the actual number of patients to increase the credibility of disease surveillance based on social media data. Objective: To clarify the causal relationships among pollen count, the posting behavior of social media users, and the number of SAR patients in the real world. Methods: This analysis was conducted using datasets of pollen count, tweet numbers, and SAR patient numbers from Kanagawa Prefecture, Japan. We examined daily pollen counts for Japanese cedar (the major cause of SAR in Japan) and hinoki cypress (which commonly complicates SAR) from February 1 to May 31, 2017. The daily numbers of tweets that included the keyword "kafunshō" (or SAR) were calculated between January 1 and May 31, 2017. Daily SAR patient numbers from January 1 to May 31, 2017 were obtained from three healthcare institutes that participated in the study. The Granger causality test was used to examine the causal relationships among pollen count, tweet numbers, and the number of SAR patients during the study period from February to May 2017. To determine if time-variant factors affect these causal relationships, we also analyzed the main SAR phase (February to April) when Japanese cedar trees actively produce and release pollen. Results: Increases in pollen count were found to cause increases in the number of tweets during the overall study period (P = .04), but not the main SAR phase (P = . 05). In contrast, increases in pollen count were found to increase patient numbers in both the study period (P = .04) and the main SAR phase (P = .01). In addition, increases in the number of tweets also caused increases in patient numbers during the main SAR phase (P = .02), but not the overall study period (P = .89). Patient numbers did not affect the number of tweets in both the overall study period (P = . 24) and the main SAR phase (P = .47). Conclusions: Understanding the causal relationships between these factors is an important step to increase the credibility of surveillance systems that use social media data. Further in-depth studies are needed to identify the determinants of social media posts described in this exploratory analysis.

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“…There is much that we do not yet know about Zika and Ebola survivors long-term impacts and its relationship to disability and poor health outcomes that are being reported in affected areas [1,[3][4][5]13,14]. Robust and sustained financial resources and leadership commitment are needed to investigate reservoirs, immune response profile and evolving risk factors of these outbreaks (animal, environment, human interface), and much needed for evidence-based, programmatic and effective mitigation actions to the best extent possible.…”

Section: Introductionmentioning

confidence: 99%

“…Such interactive and real time social mobilization, information via social media; SMS and internet-based information sharing enables individuals, patient groups or survivors and communities to understand epidemics and transmission dynamics, safety and precautionary measures to exposure, pathology and actions in control [2,[8][9][10]13,14]. Proactive prevention and recovery approaches on from mobile applications information regarding these emerging outbreaks from humanitarian organizations, NGOs, healthcare workers, health professionals and communities are needed.…”

Section: Introductionmentioning

confidence: 99%

“…Proactive prevention and recovery approaches on from mobile applications information regarding these emerging outbreaks from humanitarian organizations, NGOs, healthcare workers, health professionals and communities are needed. In addition, the usefulness of mobile mapping, satellite images and drones were important in both epidemics in understanding the transmission dynamics or spread of vector or reservoir, contact tracing and tracking patients, population/animal (bats) movement and migration, thus leveraging on ICT advancements in real time and interactive data collection for evidence-driven decision making and early warning alert and resilience [9,10,14,21]. Furthermore, digital or mobile payment and finance transactions have been used in accepting donations from contributors and communication, in frontline healthcare workers, care providers and humanitarian logistics, tele-consulting and online tutorials, literacy and interactive feedbacks at all levels in West Africa Ebola and The Caribbean Zika epidemics affected areas including remote rural settings (Table 1).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Digital Technology and Mobile Applications Impact on Zika and Ebola Epidemics Data Sharing and Emergency Response

Tambo¹,

M²,

Cf³

et al. 2017

J Health Med Inform

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Increasing diversified world, era of international power diffusion and regionalisation as the world muddles through travel and trade to climate change requires promoting a more cohesive global regime to ameliorate the deficiencies in local and global 2013-2015 and 2015-2016 Ebola and Zika virus epidemics public health emergency of international concern respectively. Zika epidemics devastating and complex complications exposed the flaws in the global surveillance architecture to deal with cross-border health pandemics prevention and containment. This paper examines trade-off between technology-based data access and dissemination, Zika virus (ZIKV) epidemics complications and digital implications in care delivery solutions in pre-, during and post epidemics response and rebuilding. Our findings showed that IT-based health informatics and mobile applications are evolving and minimum global standards to open access and sharing of relevant epidemics data and information on risk communication. Public awareness and alertness, health promotion and education, counselling and guidelines on integrated syndrome surveillance and response were the most common goals and objectives during viral epidemics reduce and avert further transmission. The uses in healthcare services delivery, management, and planning was also documented and have played a critical role in raising awareness EVD and ZIKV travel restriction and delay pregnancy to women at reproductive age, effective use of personal protective equipment and cultural practices against EVD and ZIKV infections spread. Importantly, IT-based enhanced frontline improved collaborative data sharing and communication, coordinated response and recovery. The efficiency of IT-based data and information sharing and communication practices is of critical benefits for evidence informed early warning, social mobilization, advocacy, monitoring and evaluation capabilities. Mobile-based applications innovations during Zika and Ebola epidemics were very crucial for better evidence-based local and global preparedness and solutions to prevent and control epidemics impacts, promote shared benefits and economic growth.

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How people react to Zika virus outbreaks on Twitter? A computational content analysis

Cited by 134 publications

References 5 publications

How did Ebola Information Spread on Twitter: Broadcasting or Viral Spreading? (Preprint)

How did Ebola Information Spread on Twitter: Broadcasting or Viral Spreading? (Preprint)

Causal Relationships Among Pollen Counts, Tweet Numbers, and Patient Numbers for Seasonal Allergic Rhinitis Surveillance: Retrospective Analysis (Preprint)

Digital Technology and Mobile Applications Impact on Zika and Ebola Epidemics Data Sharing and Emergency Response

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