Causal Modeling of Twitter Activity During COVID-19

Understanding the characteristics of public attention and sentiment is an essential prerequisite for appropriate crisis management during adverse health events. This is even more crucial during a pandemic such as COVID-19, as primary responsibility of risk management is not centralized to a single institution, but distributed across society. While numerous studies utilize Twitter data in descriptive or predictive context during COVID-19 pandemic, causal modeling of public attention has not been investigated. In this study, we propose a causal inference approach to discover and quantify causal relationships between pandemic characteristics (e.g. number of infections and deaths) and Twitter activity as well as public sentiment. Our results show that the proposed method can successfully capture the epidemiological domain knowledge and identify variables that affect public attention and sentiment. We believe our work contributes to the field of infodemiology by distinguishing events that correlate with public attention from events that cause public attention.Comment: 13 pages, 3 figure

COVID-19 study on sccientific articles in health communication: A science mapping analysis in Web of Science.

The COVID-19 pandemic continues to cause a collapse in the health systems and economies of many countries around the world, after 2 years of struggle and with the number of cases still growing exponentially. Health communication has become as essential and necessary for control of the pandemic as epidemiology. This bibliometric analysis identifies existing contributions, jointly studying health communication and the pandemic in scientific journals indexed. A systematic search of the Web of Science was performed, using keywords related to COVID-19 and health communication. Data extracted included the type of study, journal, number of citations, number of authors, country of publication, and study content. As the number of scientific investigations has grown, it is necessary to delve into the areas in which the most impactful publications have been generated. The results show that the scientific community has been quick to react by generating an extraordinary volume of publications. This review provides a comprehensive mapping of contributions to date, showing how research approaches have evolved in parallel with the pandemic. In 2020, concepts related to mental health, mass communication, misinformation and communication risk were more used. In 2021, vaccination, infodemic, risk perception, social distancing and telemedicine were the most prevalent keywords. By highlighting the main topics, authors, manuscripts and journals since the origin of COVID-19, the authors hope to disseminate information that can help researchers to identify subsisting knowledge gaps and a number of future research opportunities.This research was funded by Programa Operativo FEDER Andalucía 2014–2020, grant number UMA18-FEDERJA-148’ and the APC was funded by Universidad de Málaga/CBUA

Public Sentiments towards the COVID-19 Pandemic: Insights from the Academic Literature Review and Twitter Analytics

The recent COVID-19 pandemic has severely impacted nations across the globe. Not only has it created economic shocks, but also long-term impacts on the social and psychological behaviors of the public. This can be attributed to the severity of the pandemic and because of the preventive and control measures such as global lockdowns, social distancing, and selfisolation that the governments imposed. Previous studies have reported significant changes in human emotions and behaviors are used to measure public sentiments about certain phenomena (such as the recent pandemic). The present study aims to study the public's sentiments during the COVID-19 outbreak based on an analytics review of public tweets highlighting changes in emotions. A dataset of 58,320 tweets extracted from Twitter and 61 academic articles was explored to analyze behavioral and emotional changes during previous and current pandemic situations. We chose the RPA – COV (Research Process Approach – COVID-19) approach, which was combined with the LBTA (Literature-Based Thematic Analysis) and the COVTA (COVID-19 Twitter Analytics). The sentiments' analysis results were coupled with word-tree analysis and highlighted that the public showed more highly neutral, positive, and mixed emotions than negative ones. The analysis pointed that people may react differently on Twitter as compared to real-life circumstances. The present study makes a significant contribution towards understanding how the public express their sentiments in pandemic situations

Attempt to understand public-health relevant social dimensions of COVID-19 outbreak in Poland

Recently, the whole of Europe, including Poland, have been significantly affected by COVID-19 and its social and economic consequences which are already causing dozens of billions of euros monthly losses in Poland alone. Social behaviour has a fundamental impact on the dynamics of the spread of infectious diseases such as SARS-CoV-2, challenging the existing health infrastructure and social organization. Modelling and understanding mechanisms of social behaviour (e.g. panic and social distancing) and its contextualization with regard to Poland can contribute to better response to the outbreak on a national and local level. In the presented study we aim to investigate the impact of the COVID-19 on society by: (i) measuring the relevant activity in internet news and social media; (ii) analysing attitudes and demographic patterns in Poland. In the end, we are going to implement computational social science and digital epidemiology research approach to provide urgently needed information on social dynamics during the outbreak. This study is an ad hoc reaction only, and our goal is to signal the main areas of possible research to be done in the future and cover issues with direct or indirect relation to public health

Infodemiology to Improve Public Health Situational Awareness: An Investigation of 2010 Pertussis Outbreaks in California, Michigan Ohio

As a disease emerges, one of the greatest challenges for public health practitioners is to differentiate between a normal event and a serious outbreak. Typically, information from official sources and surveillance systems had been the only resource. More recently, the field of infodemiology has emerged with a focus on the distribution and determinants of health information on the internet. This research compared official reports of whooping cough with infodemiology sources, specifically news articles, search engine patterns, and Twitter, to assess the timeliness, accuracy, and correlation of these content sources. Within California, Michigan and Ohio, internet search patterns identified the outbreak of pertussis in 2010 four to eleven weeks in advance of official sources, and there was strong correlation between the epidemic curve and search pattern in Michigan and Ohio. Twitter also provided an indicator in advance of official sources in all three states, but only with a single Tweet. Using all three sources to identify indicators was better than any single source used independently. While understanding the data utility is important, it is equally critical to understand the attitudes and perceptions amongst public health leaders regarding infodemiology data to improve situational awareness. A survey of such leaders showed that infodemiology content had the most value in the first stage of situational awareness for identifying early indications of disease outbreaks. News media and internet search were moderately to highly valuable for 70% of respondents, while social media was moderately to highly valuable to 60% of respondents. For both strengthening comprehension of an outbreak and informing future predictions, beliefs were split regarding the level of potential value (if any) that exists. This led to a framework on how to include infodemiology content in public health situational awareness strategies going forward, so limited resources are used as effectively as possible.Doctor of Public Healt

Public Sentiments towards the COVID-19 Pandemic: Insights from the Academic Literature Review and Twitter Analytics

The recent COVID-19 pandemic has severely impacted nations across the globe. Not only has it created economic shocks, but also long-term impacts on the social and psychological behaviors of the public. This can be attributed to the severity of the pandemic and because of the preventive and control measures such as global lockdowns, social distancing, and selfisolation that the governments imposed. Previous studies have reported significant changes in human emotions and behaviors are used to measure public sentiments about certain phenomena (such as the recent pandemic). The present study aims to study the public's sentiments during the COVID-19 outbreak based on an analytics review of public tweets highlighting changes in emotions. A dataset of 58,320 tweets extracted from Twitter and 61 academic articles was explored to analyze behavioral and emotional changes during previous and current pandemic situations. We chose the RPA – COV (Research Process Approach – COVID-19) approach, which was combined with the LBTA (Literature-Based Thematic Analysis) and the COVTA (COVID-19 Twitter Analytics). The sentiments' analysis results were coupled with word-tree analysis and highlighted that the public showed more highly neutral, positive, and mixed emotions than negative ones. The analysis pointed that people may react differently on Twitter as compared to real-life circumstances. The present study makes a significant contribution towards understanding how the public express their sentiments in pandemic situations

Revisão crítica: Uma abordagem aos estudos sobre o uso dos media sociais durante a pandemia Covid-19

Since the coronavirus disease (covid-19) was declared a public health emergency of international concern by the World Health Organization in January 2020, it has led to the loss of millions of human lives and a global economic recession. Recently, there has been a recognized need for effective health communication via social media to deliver accurate information and promote pertinent behavioral change. Thus, this study provides a systematic review to explore what has been done, what conflicts exist, and what knowledge gap remains in terms of social media use during the covid-19 wave, indicating relevant communication strategies. This research is based on 76 relevant papers taken from searches on the Web of Science and Google Scholar. The analysis revealed that much of the literature confirms the positive effect of social media on information propagation and promotion of precautions in the control of covid-19. The spreading of rumors, especially about government performance, in social media is clearly of increasing concern. Currently, heated debate continues about the association between exposure to social media and public mental health. Another fiercely debated question is whether rumors are shared more widely than fact-checking information. Up to date, far too little attention has been paid to information disparities and vulnerable groups on social media.info:eu-repo/semantics/publishedVersio

Social Media Multidimensional Analysis for Intelligent Health Surveillance

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

Mining Social Media to Understand Consumers' Health Concerns and the Public's Opinion on Controversial Health Topics.

Social media websites are increasingly used by the general public as a venue to express health concerns and discuss controversial medical and public health issues. This information could be utilized for the purposes of public health surveillance as well as solicitation of public opinions. In this thesis, I developed methods to extract health-related information from multiple sources of social media data, and conducted studies to generate insights from the extracted information using text-mining techniques. To understand the availability and characteristics of health-related information in social media, I first identified the users who seek health information online and participate in online health community, and analyzed their motivations and behavior by two case studies of user-created groups on MedHelp and a diabetes online community on Twitter. Through a review of tweets mentioning eye-related medical concepts identified by MetaMap, I diagnosed the common reasons of tweets mislabeled by natural language processing tools tuned for biomedical texts, and trained a classifier to exclude non medically-relevant tweets to increase the precision of the extracted data. Furthermore, I conducted two studies to evaluate the effectiveness of understanding public opinions on controversial medical and public health issues from social media information using text-mining techniques. The first study applied topic modeling and text summarization to automatically distill users' key concerns about the purported link between autism and vaccines. The outputs of two methods cover most of the public concerns of MMR vaccines reported in previous survey studies. In the second study, I estimated the public's view on the ac{ACA} by applying sentiment analysis to four years of Twitter data, and demonstrated that the the rates of positive/negative responses measured by tweet sentiment are in general agreement with the results of Kaiser Family Foundation Poll. Finally, I designed and implemented a system which can automatically collect and analyze online news comments to help researchers, public health workers, and policy makers to better monitor and understand the public's opinion on issues such as controversial health-related topics.PhDInformationUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/120714/1/owenliu_1.pd

SoMeIL: A social media infodemic listening for public health behaviours conceptual framework

Introduction The coronavirus disease 2019 (COVID-19) pandemic has escalated health infodemics given substantially digitalized daily life since the pandemic began. The number of social media users has skyrocketed. However, this has brought issues given misleading health information circulating on social media platforms that can lead to undesirable behaviours compromising individual or public health in real life. One long-lasting health issue is vaccine hesitancy, which has been further compounded by health infodemics on social media. According to the World Health Organization, health infodemics occur when too much information that makes true information competes with misinformation for people’s attention, understanding, and adherence to recommended health interventions. Existing theories and theoretical constructs have been applied to study public behaviours influenced by health infodemics on social media. However, these theories have limited to individual behaviours and ignored other critical factors. Furthermore, the current theories have rarely reflected the nature of social media as information can be disseminated instantly and massively without geographical restrictions regardless of information quality. Therefore, this dissertation aimed to address these limitations by proposing a solution that can listen to public discourse on social media and infer their behavioural intentions in real life. Methods The scoping review (Study I) was conducted by following the methods of Arksey and O'Malley as well as Levac et al. to identify and synthesize literature related to the research question. The theory construction methodology was used in the conceptual paper (Study II) to review existing theories and propose a new conceptual framework. Next, the Latent Dirichlet allocation topic modelling and qualitative thematic analysis were applied in the preliminary and partial qualitative validation study (Study III). The last study (Study IV) applied structural equation modeling (SEM) to infer people’s intentions toward COVID-19 vaccination in real life from Twitter amid the pandemic as a preliminary and partial validation for the proposed conceptual framework. Results A total of 2,405 articles published between November 1, 2019, and November 4, 2020, were retrieved from PubMed, Scopus, and PsycINFO. After removing duplicates, non-empirical literature, and irrelevant studies, a total of 81 articles written in English published in peer-reviewed journals were included in the scoping review (Study I). Six themes were found and reported: (1) surveying public attitudes, (2) identifying infodemics, (3) assessing mental health, (4) detecting or predicting COVID-19 cases, (5) analyzing government responses to the pandemic, and (6) evaluating quality of health information in prevention education videos. The findings also suggested knowledge gaps in real-time COVID-19 surveillance using social media data and limited machine learning or artificial intelligence techniques used in overall COVID-19 research using social media data except the first theme. In the conceptual paper (Study II), a new conceptual framework—social media infodemic listening for public health behaviors (SoMeIL) —was proposed to address limitations in existing theories given lacking systematic and theoretical foundation for such research. After the SoMeIL was proposed, validations were needed. A preliminary qualitative validation and demonstration using Twitter data about the Canadian Freedom Convoy were conductedto partially validate and illustrate how the SoMeIL conceptual framework could be applied (Study III). Finally, the findings from SEM in the last study (Study IV) showed statistically significant associations between the latent variable and the observed variables derived from Twitter. This study provided preliminary evidence to validate partial components in the proposed SoMeIL conceptual framework that could be used as a proxy to infer people’s vaccination intentions in real life. It also demonstrated the feasibility of using Twitter data in SEM research besides typical surveys. Conclusion The scoping review (Study I) was important since it identified various roles that social media data have played in research related to the COVID-19 pandemic. It also informed us of knowledge gaps to be bridged. This led us to the conceptual paper (Study II) since we identified limitations in existing theories when the current theories or theoretical constructs were applied in health research that analyzed social media data. A new conceptual framework—SoMeIL—was proposed accordingly. A preliminary qualitative study was followed to validate and demonstrate partial components of the SoMeIL conceptual framework. The last study (Study IV) showed preliminary evidence to show that parts of the SoMeIL conceptual framework was workable given statistically significant relationships found among certain constructs. As a result, Twitter data in this dissertation could be used as a proxy to infer people’s vaccination behavior in real life as suggested by the proposed conceptual framework. Yet more research is needed to further validate and improve the proposed SoMeIL conceptual framework. If social media listening can be integrated into future pandemic preparedness as the proposed conceptual framework suggests, it can help health authorities and governmental agencies promptly shape public perception, disseminate more scientific information, and influence behaviors during a health crisis in a timely fashion