Analyzing Discussions Around Rural Health on Twitter During the COVID-19 Pandemic: Social Network Analysis of Twitter Data

Background Individuals from rural areas are increasingly using social media as a means of communication, receiving information, or actively complaining of inequalities and injustices. Objective The aim of our study is to analyze conversations about rural health taking place on Twitter during a particular phase of the COVID-19 pandemic. Methods This study captured 57 days' worth of Twitter data related to rural health from June to August 2021, using English-language keywords. The study used social network analysis and natural language processing to analyze the data. Results It was found that Twitter served as a fruitful platform to raise awareness of problems faced by users living in rural areas. Overall, Twitter was used in rural areas to express complaints, debate, and share information. Conclusions Twitter could be leveraged as a powerful social listening tool for individuals and organizations that want to gain insight into popular narratives around rural health.

Analyzing Discussions Around Rural Health on Twitter During the COVID-19 Pandemic: Social Network Analysis of Twitter Data.

Background: Individuals from rural areas are increasingly using social media as a means of communication, receiving information, or actively complaining of inequalities and injustices. Objective: The aim of our study is to analyze conversations about rural health taking place on Twitter during a particular phase of the COVID-19 pandemic. Methods: This study captured 57 days' worth of Twitter data related to rural health from June to August 2021, using English-language keywords. The study used social network analysis and natural language processing to analyze the data. Results: It was found that Twitter served as a fruitful platform to raise awareness of problems faced by users living in rural areas. Overall, Twitter was used in rural areas to express complaints, debate, and share information. Conclusions: Twitter could be leveraged as a powerful social listening tool for individuals and organizations that want to gain insight into popular narratives around rural health.

Twitter's Role in Combating the Magnetic Vaccine Conspiracy Theory: Social Network Analysis of Tweets.

BACKGROUND: The popularity of the magnetic vaccine conspiracy theory and other conspiracy theories of a similar nature creates challenges to promoting vaccines and disseminating accurate health information. OBJECTIVE: Health conspiracy theories are gaining in popularity. This study's objective was to evaluate the Twitter social media network related to the magnetic vaccine conspiracy theory and apply social capital theory to analyze the unique social structures of influential users. As a strategy for web-based public health surveillance, we conducted a social network analysis to identify the important opinion leaders sharing the conspiracy, the key websites, and the narratives. METHODS: A total of 18,706 tweets were retrieved and analyzed by using social network analysis. Data were retrieved from June 1 to June 13, 2021, using the keyword vaccine magnetic. Tweets were retrieved via a dedicated Twitter application programming interface. More specifically, the Academic Track application programming interface was used, and the data were analyzed by using NodeXL Pro (Social Media Research Foundation) and Gephi. RESULTS: There were a total of 22,762 connections between Twitter users within the data set. This study found that the most influential user within the network consisted of a news account that was reporting on the magnetic vaccine conspiracy. There were also several other users that became influential, such as an epidemiologist, a health economist, and a retired sports athlete who exerted their social capital within the network. CONCLUSIONS: Our study found that influential users were effective broadcasters against the conspiracy, and their reach extended beyond their own networks of Twitter followers. We emphasize the need for trust in influential users with regard to health information, particularly in the context of the widespread social uncertainty resulting from the COVID-19 pandemic, when public sentiment on social media may be unpredictable. This study highlights the potential of influential users to disrupt information flows of conspiracy theories via their unique social capital.

The #VaccinesWork Hashtag on Twitter in the Context of the COVID-19 Pandemic: Network Analysis.

BACKGROUND: Vaccination is one of the most successful public health interventions for the prevention of COVID-19. Toward the end of April 2021, UNICEF (United Nations International Children's Emergency Fund), alongside other organizations, were promoting the hashtag #VaccinesWork. OBJECTIVE: The aim of this paper is to analyze the #VaccinesWork hashtag on Twitter in the context of the COVID-19 pandemic, analyzing the main messages shared and the organizations involved. METHODS: The data set used in this study consists of 11,085 tweets containing the #VaccinesWork hashtag from the 29th to the 30th of April 2021. The data set includes tweets that may not have the hashtag but were replies or mentions in those tweets. The data were retrieved using NodeXL, and the network graph was laid out using the Harel-Koren fast multiscale layout algorithm. RESULTS: The study found that organizations such as the World Health Organization, UNICEF, and Gavi were the key opinion leaders and had a big influence on the spread of information among users. Furthermore, the most shared URLs belonged to academic journals with a high impact factor. Provaccination users had other vaccination-promoting hashtags in common, not only in the COVID-19 scenario. CONCLUSIONS: This study investigated the discussions surrounding the #VaccinesWork hashtag. Social media networks containing conspiracy theories tend to contain dubious accounts leading the discussions and are often linked to unverified information. This kind of analysis can be useful to detect the optimal moment for launching health campaigns on Twitter.

A Social Network Analysis of Tweets Related to Mandatory COVID-19 Vaccination in Poland.

Poland's efforts to combat COVID-19 were hindered by endemic vaccination hesitancy and the prevalence of opponents to pandemic restrictions. In this environment, the policy of a COVID-19 vaccination mandate faces strong resistance in the public debate. Exploring the discourse around this resistance could help uncover the motives and develop an understanding of vaccination hesitancy in Poland. This paper aims to conduct a social network analysis and content analysis of Twitter discussions around the intention of the Polish Ministry of Health to introduce mandatory vaccinations for COVID-19. Twitter was chosen as a platform to study because of the critical role it played during the global health crisis. Twitter data were retrieved from 26 July to 9 December 2021 through the API v2 for Academic Research, and analysed using NodeXL and Gephi. When conducting social network analysis, nodes were ranked by their betweenness centrality. Clustering analysis with the Clauset-Newman-Moore algorithm revealed two important groups of users: advocates and opponents of mandatory vaccination. The temporal trends of tweets, the most used hashtags, the sentiment expressed in the most popular tweets, and correlations with epidemiological data were also studied. The results reveal a substantial degree of polarisation, a high intensity of the discussion, and a high degree of involvement of Twitter users. Vaccination mandate advocates were consistently more numerous, but less engaged and less mobilised to "preach" their own stances. Vaccination mandate opponents were vocal and more mobilised to participate: either as original authors or as information diffusers. Our research leads to the conclusion that systematic monitoring of the public debate on vaccines is essential not only in counteracting misinformation, but also in crafting evidence-based as well as emotionally motivating narratives.

A Social Network Analysis of Twitter Data Related to Blood Clots and Vaccines.

After the first weeks of vaccination against SARS-CoV-2, several cases of acute thrombosis were reported. These news reports began to be shared frequently across social media platforms. The aim of this study was to conduct an analysis of Twitter data related to the overall discussion. The data were retrieved from 14 March to 14 April 2021 using the keyword 'blood clots'. A dataset with n = 266,677 tweets was retrieved, and a systematic random sample of 5% of tweets (n = 13,334) was entered into NodeXL for further analysis. Social network analysis was used to analyse the data by drawing upon the Clauset-Newman-Moore algorithm. Influential users were identified by drawing upon the betweenness centrality measure. Text analysis was applied to identify the key hashtags and websites used at this time. More than half of the network comprised retweets, and the largest groups within the network were broadcast clusters in which a number of key users were retweeted. The most popular narratives involved highlighting the low risk of obtaining a blood clot from a vaccine and highlighting that a number of commonly consumed medicine have higher blood clot risks. A wide variety of users drove the discussion on Twitter, including writers, physicians, the general public, academics, celebrities, and journalists. Twitter was used to highlight the low potential of developing a blood clot from vaccines, and users on Twitter encouraged vaccinations among the public.

An analysis of antibody responses and clinical sequalae of the Sinopharm HB02 COVID19 vaccine in dialysis patients in the United Arab Emirates.

AIM: To establish the responses to the Sinopharm HB02 COVID-19 vaccination in the dialysis population, which are not well established. We examined the humoral responses to the Sinopharm COVID vaccine in haemodialysis patients. METHODS: Standard vaccinations (two doses at interval of ~21 days) were given to all consenting haemodialysis patients on dialysis (n = 1296). We measured the antibody responses at 14-21 days after the second vaccine to define the development of anti-spike antibodies >15 AU/ml after vaccination and observed the clinical effects of vaccination. RESULTS: Vaccination was very well tolerated with few side-effects. In those who consented to antibody measurements, (n = 446) baseline sampling showed 77 had positive antibodies, yet received full vaccination without any apparent adverse events. Positive anti-spike antibodies developed in 50% of the 270 baseline negative patients who had full sampling, compared with 78.1% in the general population. COVID infection continues to occur in both vaccinated and unvaccinated individuals, but in the whole group vaccination appears to have been associated with a reduction in the case fatality rate. CONCLUSION: The humoral immune responses to standard HB02 vaccination schedules are attenuated in a haemodialysis cohort, but likely the vaccine saves lives. We suggest that an enhanced HB02 vaccination course or antibody checking may be prudent to protect this vulnerable group of patients. We suggest a booster dose of this vaccine at 3 months should be given to all dialysis patients, on the grounds that it is well tolerated even in those with good antibody levels and there may be a survival advantage.

International Gynaecological Cancer Society (IGCS) 2020 Annual Global Meeting: Twitter activity analysis.

OBJECTIVES: Twitter is the most frequently used social media platform by healthcare practitioners, at medical conferences. This study aimed to analyze Twitter conversations during the virtual International Gynecological Cancer Society 2020 conference to understand the interactions between Twitter users related to the conference. METHODS: Tweets using the hashtag '#IGCS2020' were searched using the Twitter Search Application Programming Interface (API) during the period 10-13 September 2020. NodeXL Pro was used to retrieve data. The Clauset-Newman-Moore cluster algorithm clustered users into different groups or 'clusters' based on how users interacted. RESULTS: There were 2009 registrants for the virtual IGCS 2020 conference. The total number of users within the network was 168, and there were 880 edges connecting users. Five types of edges were identified as follows: 'replies to' (n=18), 'mentions' (n=221), 'mentions in retweets' (n=375), retweets (n=198), and tweets (n=68). The most influential account was that of the IGCS account itself (@IGCSociety). The overall network shape resembled a community where distinct groups formed within the network. Our current analyses demonstrated that less than 10% of the total members interacted on Twitter. CONCLUSION: This study identified the most influential Twitter users within the '#IGCS2020' community. he results also confirmed the community network shape of the #IGCS2020 hashtag and found that the most frequent co-related words were 'ovarian' and 'cancer' (n=39).

Rethinking Fake News: Disinformation and Ideology during the time of COVID-19 Global Pandemic

Digital media and citizen journalism has escalated the infiltration of fake news attempting to create a post truth society (Lazer et al., 2018). The COVID-19 pandemic has seen a surge of misinformation leading to anti-mask, anti-vaccine and anti-5G protests on a global scale. Although the term ?misinformation? has been generalized in media and scholarly work, there is a fundamental difference between how misinformation impacts society, compared to more strategically planned disinformation attacks. In this study we explore the ideological constructs of citizens towards acceptance or rejection of disinformation during the heightened time of a COVID-19 global health crisis. Our analysis follows two specific disinformation propagandas evaluated through social network analysis of Twitter data in addition to qualitative insights generated from tweets and in-depth interviews.

Exploring nurses' online perspectives and social networks during a global pandemic COVID-19.

OBJECTIVES: Examine the online interactions, social networks, and perspectives of nursing actors on COVID-19 from conversations on Twitter to understand how the profession responded to this global pandemic. DESIGN: Mixed methods. SAMPLE: Ten-thousand five-hundred and seventy-four tweets by 2790 individuals and organizations. MEASUREMENTS: NodeXL software was used for social network analysis to produce a network visualization. The betweenness centrality algorithm identified key users who were influential in COVID-19 related conversations on Twitter. Inductive content analysis enabled exploration of tweet content. A communicative figurations framework guided the study. RESULTS: Nursing actors formed different social groupings, and communicated with one another across groups. Tweets covered four themes; (1) outbreak and clinical management of the infectious disease, (2) education and information sharing, (3) social, economic, and political context, and (4) working together and supporting each other. CONCLUSION: In addition to spreading knowledge, nurses tried to reach out through social media to political and healthcare leaders to advocate for improvements needed to address COVID-19. However, they primarily conversed within their own professional community. Action is needed to better understand how social media is and can be used by nurses for health communication, and to improve their preparedness to be influential on social media beyond the nursing community.

Outcomes of patients with end stage kidney disease on dialysis with COVID-19 in Abu Dhabi, United Arab Emirates; from PCR to antibody.

BACKGROUND: Individuals with end-stage kidney disease (ESKD) on dialysis are vulnerable to contracting COVID-19 infection, with mortality as high as 31 % in this group. Population demographics in the UAE are dissimilar to many other countries and data on antibody responses to COVID-19 is also limited. The objective of this study was to describe the characteristics of patients who developed COVID-19, the impact of the screening strategy, and to assess the antibody response to a subset of dialysis patients. METHODS: We retrospectively examined the outcomes of COVID19 infection in all our haemodialysis patients, who were tested regularly for COVID 19, whether symptomatic or asymptomatic. In addition, IgG antibody serology was also performed to assess response to COVID-19 in a subset of patients. RESULTS: 152 (13 %) of 1180 dialysis patients developed COVID-19 during the study period from 1st of March to the 1st of July 2020. Of these 81 % were male, average age of 52​ years and 95 % were on in-centre haemodialysis. Family and community contact was most likely source of infection in most patients. Fever (49 %) and cough (48 %) were the most common presenting symptoms, when present. Comorbidities in infected individuals included hypertension (93 %), diabetes (49 %), ischaemic heart disease (30 %). The majority (68 %) developed mild disease, whilst 13 % required critical care. Combinations of drugs including hydroxychloroquine, favipiravir, lopinavir, ritonavir, camostat, tocilizumab and steroids were used based on local guidelines. The median time to viral clearance defined by two negative PCR tests was 15 days [IQR 6-25]. Overall mortality in our cohort was 9.2 %, but ICU mortality was 65 %. COVID-19 IgG antibody serology was performed in a subset (n = 87) but 26 % of PCR positive patients (n = 23) did not develop a significant antibody response. CONCLUSIONS: Our study reports a lower mortality in this patient group compared with many published series. Asymptomatic PCR positivity was present in 40 %. Rapid isolation of positive patients may have contributed to the relative lack of spread of COVID-19 within our dialysis units. The lack of antibody response in a few patients is concerning.

A Social Network Analysis of Tweets Related to Masks during the COVID-19 Pandemic.

BACKGROUND: High compliance in wearing a mask is a crucial factor for stopping the transmission of COVID-19. Since the beginning of the pandemic, social media has been a key communication channel for citizens. This study focused on analyzing content from Twitter related to masks during the COVID-19 pandemic. METHODS: Twitter data were collected using the keyword "mask" from 27 June 2020 to 4 July 2020. The total number of tweets gathered were n = 452,430. A systematic random sample of 1% (n = 4525) of tweets was analyzed using social network analysis. NodeXL (Social Media Research Foundation, California, CA, USA) was used to identify users ranked influential by betweenness centrality and was used to identify key hashtags and content. RESULTS: The overall shape of the network resembled a community network because there was a range of users conversing amongst each other in different clusters. It was found that a range of accounts were influential and/or mentioned within the network. These ranged from ordinary citizens, politicians, and popular culture figures. The most common theme and popular hashtags to emerge from the data encouraged the public to wear masks. CONCLUSION: Towards the end of June 2020, Twitter was utilized by the public to encourage others to wear masks and discussions around masks included a wide range of users.

COVID-19 and the "Film Your Hospital" Conspiracy Theory: Social Network Analysis of Twitter Data.

BACKGROUND: During the COVID-19 pandemic, a number of conspiracy theories have emerged. A popular theory posits that the pandemic is a hoax and suggests that certain hospitals are "empty." Research has shown that accepting conspiracy theories increases the likelihood that an individual may ignore government advice about social distancing and other public health interventions. Due to the possibility of a second wave and future pandemics, it is important to gain an understanding of the drivers of misinformation and strategies to mitigate it. OBJECTIVE: This study set out to evaluate the #FilmYourHospital conspiracy theory on Twitter, attempting to understand the drivers behind it. More specifically, the objectives were to determine which online sources of information were used as evidence to support the theory, the ratio of automated to organic accounts in the network, and what lessons can be learned to mitigate the spread of such a conspiracy theory in the future. METHODS: Twitter data related to the #FilmYourHospital hashtag were retrieved and analyzed using social network analysis across a 7-day period from April 13-20, 2020. The data set consisted of 22,785 tweets and 11,333 Twitter users. The Botometer tool was used to identify accounts with a higher probability of being bots. RESULTS: The most important drivers of the conspiracy theory are ordinary citizens; one of the most influential accounts is a Brexit supporter. We found that YouTube was the information source most linked to by users. The most retweeted post belonged to a verified Twitter user, indicating that the user may have had more influence on the platform. There was a small number of automated accounts (bots) and deleted accounts within the network. CONCLUSIONS: Hashtags using and sharing conspiracy theories can be targeted in an effort to delegitimize content containing misinformation. Social media organizations need to bolster their efforts to label or remove content that contains misinformation. Public health authorities could enlist the assistance of influencers in spreading antinarrative content.

COVID-19 and the 5G Conspiracy Theory: Social Network Analysis of Twitter Data.

BACKGROUND: Since the beginning of December 2019, the coronavirus disease (COVID-19) has spread rapidly around the world, which has led to increased discussions across online platforms. These conversations have also included various conspiracies shared by social media users. Amongst them, a popular theory has linked 5G to the spread of COVID-19, leading to misinformation and the burning of 5G towers in the United Kingdom. The understanding of the drivers of fake news and quick policies oriented to isolate and rebate misinformation are keys to combating it. OBJECTIVE: The aim of this study is to develop an understanding of the drivers of the 5G COVID-19 conspiracy theory and strategies to deal with such misinformation. METHODS: This paper performs a social network analysis and content analysis of Twitter data from a 7-day period (Friday, March 27, 2020, to Saturday, April 4, 2020) in which the #5GCoronavirus hashtag was trending on Twitter in the United Kingdom. Influential users were analyzed through social network graph clusters. The size of the nodes were ranked by their betweenness centrality score, and the graph's vertices were grouped by cluster using the Clauset-Newman-Moore algorithm. The topics and web sources used were also examined. RESULTS: Social network analysis identified that the two largest network structures consisted of an isolates group and a broadcast group. The analysis also revealed that there was a lack of an authority figure who was actively combating such misinformation. Content analysis revealed that, of 233 sample tweets, 34.8% (n=81) contained views that 5G and COVID-19 were linked, 32.2% (n=75) denounced the conspiracy theory, and 33.0% (n=77) were general tweets not expressing any personal views or opinions. Thus, 65.2% (n=152) of tweets derived from nonconspiracy theory supporters, which suggests that, although the topic attracted high volume, only a handful of users genuinely believed the conspiracy. This paper also shows that fake news websites were the most popular web source shared by users; although, YouTube videos were also shared. The study also identified an account whose sole aim was to spread the conspiracy theory on Twitter. CONCLUSIONS: The combination of quick and targeted interventions oriented to delegitimize the sources of fake information is key to reducing their impact. Those users voicing their views against the conspiracy theory, link baiting, or sharing humorous tweets inadvertently raised the profile of the topic, suggesting that policymakers should insist in the efforts of isolating opinions that are based on fake news. Many social media platforms provide users with the ability to report inappropriate content, which should be used. This study is the first to analyze the 5G conspiracy theory in the context of COVID-19 on Twitter offering practical guidance to health authorities in how, in the context of a pandemic, rumors may be combated in the future.