Social Media for Nowcasting Flu Activity: Spatio-Temporal Big Data Analysis

Zadeh, Amir Hassan; Zolbanin, Hamed Majidi; Sharda, Ramesh; Delen, Dursun

doi:10.1007/s10796-018-9893-0

Cited by 59 publications

(33 citation statements)

References 54 publications

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“…To achieve the study objectives and conduct the research using a systematic approach, a conceptual framework was developed based on a literature review and health belief change theory. The potential benefits of using social media platforms in public health protection against pandemic diseases include dissemination of public health interventions, enhanced public awareness, promotion of healthy behavior, improved health outcomes, and provision of health information to the community [ 8 , 24 , 46 - 50 ]. The analysis provides empirical evidence regarding the impact of using social media platforms on public health awareness, public health behavior changes, and health protection against COVID-19 in hypotheses 1, 3, and 4, respectively.…”

Section: Discussionmentioning

confidence: 99%

“…Social media platforms can be used to reduce the spread of pandemics, thereby lowering the levels of fear and anxiety among the general public. Researchers have argued that social media communication can transfer useful information about infectious diseases based on identifying and tracking users’ behavioral patterns [ 24 ]. A model was suggested by Misra et al [ 7 ] to explore the impact of awareness realized by social media campaigns on infectious disease prevalence, considering that the campaigns launched on social media platforms represent a variable whose growth depends on the number of individuals infected.…”

Section: Introductionmentioning

confidence: 99%

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Influence of Social Media Platforms on Public Health Protection Against the COVID-19 Pandemic via the Mediating Effects of Public Health Awareness and Behavioral Changes: Integrated Model

Al-Dmour¹,

Masa’deh²,

Salman³

et al. 2020

J Med Internet Res

270

197

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Background Despite the growing body of literature examining social media in health contexts, including public health communication, promotion, and surveillance, limited insight has been provided into how the utility of social media may vary depending on the particular public health objectives governing an intervention. For example, the extent to which social media platforms contribute to enhancing public health awareness and prevention during epidemic disease transmission is currently unknown. Doubtlessly, coronavirus disease (COVID-19) represents a great challenge at the global level, aggressively affecting large cities and public gatherings and thereby having substantial impacts on many health care systems worldwide as a result of its rapid spread. Each country has its capacity and reacts according to its perception of threat, economy, health care policy, and the health care system structure. Furthermore, we noted a lack of research focusing on the role of social media campaigns in public health awareness and public protection against the COVID-19 pandemic in Jordan as a developing country. Objective The purpose of this study was to examine the influence of social media platforms on public health protection against the COVID-19 pandemic via public health awareness and public health behavioral changes as mediating factors in Jordan. Methods A quantitative approach and several social media platforms were used to collect data via web questionnaires in Jordan, and a total of 2555 social media users were sampled. This study used structural equation modeling to analyze and verify the study variables. Results The main findings revealed that the use of social media platforms had a significant positive influence on public health protection against COVID-19 as a pandemic. Public health awareness and public health behavioral changes significantly acted as partial mediators in this relationship. Therefore, a better understanding of the effects of the use of social media interventions on public health protection against COVID-19 while taking public health awareness and behavioral changes into account as mediators should be helpful when developing any health promotion strategy plan. Conclusions Our findings suggest that the use of social media platforms can positively influence awareness of public health behavioral changes and public protection against COVID-19. Public health authorities may use social media platforms as an effective tool to increase public health awareness through dissemination of brief messages to targeted populations. However, more research is needed to validate how social media channels can be used to improve health knowledge and adoption of healthy behaviors in a cross-cultural context.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Influence of Social Media Platforms on Public Health Protection Against the COVID-19 Pandemic via the Mediating Effects of Public Health Awareness and Behavioral Changes: Integrated Model

Al-Dmour¹,

Masa’deh²,

Salman³

et al. 2020

J Med Internet Res

270

197

View full text Add to dashboard Cite

show abstract

“…Therefore, social media are important platforms for risk communication during public health crises [ 10 , 11 ]. The utility of these platforms, however, extends beyond states of emergency to allow for public health surveillance [ 12 , 13 ] and the exchange of health information [ 11 , [14] , [15] , [16] , [17] ], including information about illnesses and associated treatments [ 18 ].…”

Section: Methodsmentioning

confidence: 99%

Miscommunication in the age of communication: A crowdsourcing framework for symptom surveillance at the time of pandemics

Zolbanin

Zadeh

Davazdahemami

2021

International Journal of Medical Informatics

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Objective There was a significant delay in compiling a complete list of the symptoms of COVID-19 during the 2020 outbreak of the disease. When there is little information about the symptoms of a novel disease, interventions to contain the spread of the disease would be suboptimal because people experiencing symptoms that are not yet known to be related to the disease may not limit their social activities. Our goal was to understand whether users’ social media postings about the symptoms of novel diseases could be used to develop a complete list of the disease symptoms in a shorter time. Materials and Methods We used the Twitter API to download tweets that contained ‘coronavirus’, ‘COVID-19’, and ‘symptom’. After data cleaning, the resulting dataset consisted of over 95,000 unique, English tweets posted between January 17, 2020 and March 15, 2020 that contained references to the symptoms of COVID-19. We analyzed this data using network and time series methods. Results We found that a complete list of the symptoms of COVID-19 could have been compiled by mid-March 2020, before most states in the U.S. announced a lockdown and about 75 days earlier than the list was completed on CDC’s website. Discussion & Conclusion We conclude that national and international health agencies should use the crowd-sourced intelligence obtained from social media to develop effective symptom surveillance systems in the early stages of pandemics. We propose a high-level framework that facilitates the collection, analysis, and dissemination of information that are posted in various languages and on different social media platforms about the symptoms of novel diseases.

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“…Considering the spatial dimension of tweets, in a recent work [22], Twitter data are used to extract spatio-temporal behavioural patterns to monitor flu outbreaks and their locations. In addition, they propose future extensions to study the epidemic spread of flu within different subpopulations by leveraging socio-economic and demographic data.…”

Section: Multidimensional Analysis Of Tweetsmentioning

confidence: 99%

Social Media Multidimensional Analysis for Intelligent Health Surveillance

Aramburu

Berlanga²,

Lanza³

2020

IJERPH

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Background: Recent work in social network analysis has shown the usefulness of analysing and predicting outcomes from user-generated data in the context of Public Health Surveillance (PHS). Most of the proposals have focused on dealing with static datasets gathered from social networks, which are processed and mined off-line. However, little work has been done on providing a general framework to analyse the highly dynamic data of social networks from a multidimensional perspective. In this paper, we claim that such a framework is crucial for including social data in PHS systems. Methods: We propose a dynamic multidimensional approach to deal with social data streams. In this approach, dynamic dimensions are continuously updated by applying unsupervised text mining methods. More specifically, we analyse the semantics and temporal patterns in posts for identifying relevant events, topics and users. We also define quality metrics to detect relevant user profiles. In this way, the incoming data can be further filtered to cope with the goals of PHS systems. Results: We have evaluated our approach over a long-term stream of Twitter. We show how the proposed quality metrics allow us to filter out the users that are out-of-domain as well as those with low quality in their messages. We also explain how specific user profiles can be identified through their descriptions. Finally, we illustrate how the proposed multidimensional model can be used to identify main events and topics, as well as to analyse their audience and impact. Conclusions: The results show that the proposed dynamic multidimensional model is able to identify relevant events and topics and analyse them from different perspectives, which is especially useful for PHS systems.

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Social Media for Nowcasting Flu Activity: Spatio-Temporal Big Data Analysis

Cited by 59 publications

References 54 publications

Influence of Social Media Platforms on Public Health Protection Against the COVID-19 Pandemic via the Mediating Effects of Public Health Awareness and Behavioral Changes: Integrated Model

Influence of Social Media Platforms on Public Health Protection Against the COVID-19 Pandemic via the Mediating Effects of Public Health Awareness and Behavioral Changes: Integrated Model

Miscommunication in the age of communication: A crowdsourcing framework for symptom surveillance at the time of pandemics

Social Media Multidimensional Analysis for Intelligent Health Surveillance

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